Recent Fuzzing Paper
# Recent Papers Related To Fuzzing

Remark: This website is only used for collecting and grouping the related paper. If there are any paper need to be updated, you can contribute PR.

Main Repo.: [https://github.com/wcventure/FuzzingPaper](https://github.com/wcventure/FuzzingPaper)  
Mirrors: [https://gitcode.net/mirrors/wcventure/FuzzingPaper](https://gitcode.net/mirrors/wcventure/FuzzingPaper)

# All Papers (Classification according to Publication)

- **Survey/Review**
  - [Fuzzing: A Survey for Roadmap](#fuzzing-a-survey-for-roadmap)
  - [Fuzzing: Challenges and Reflections](#fuzzing-challenges-and-reflections)
  - [SoK: The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing](#sok-the-progress-challenges-and-perspectives-of-directed-greybox-fuzzing)
  - [Fuzzing: Hack, Art, and Science](#fuzzing-hack-art-and-science-cacm-2020)
  - [Survey of Directed Fuzzy Technology](#survey-of-directed-fuzzy-technology)
  - [A Review of Machine Learning Applications in Fuzzing](#a-review-of-machine-learning-applications-in-fuzzing)
  - [A systematic review of fuzzing based on machine learning techniques](#a-systematic-review-of-fuzzing-based-on-machine-learning-techniques)
  - [The Art, Science, and Engineering of Fuzzing: A Survey](#the-art-science-and-engineering-of-fuzzing-a-survey)
  - [Fuzzing: Art, Science, and Engineering](#fuzzing-art-science-and-engineering)
  - [Fuzzing: a survey](#fuzzing-a-survey)
  - [Fuzzing: State of the art](#fuzzing-state-of-the-art)
  - [Survey of Software Fuzzing Techniques](#survey-of-software-fuzzing-techniques)
  - [A Review of Fuzzing Tools and Methods](#a-review-of-fuzzing-tools-and-methods)
- **ASE 2022**
  - [Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing]
  - [Efficient Greybox Fuzzing to Detect Memory Errors]
  - [FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures]
  - [Fuzzle: Making a Puzzle for Fuzzers]
  - [Griffin: Grammar-Free DBMS Fuzzing]
  - [HTFuzz: Heap Operation Sequence Sensitive Fuzzing]
  - [LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles]
  - [QATest: A Uniform Fuzzing Framework for Question Answering Systems]
  - [So Many Fuzzers, So Little Time - Experience from Evaluating Fuzzers on the Contiki-NG Network (Hay)StackVirt]
- **ISSTA 2022**
  - [sAlmost Correct Invariants: Synthesizing Inductive Invariants by Fuzzing Proof](#almost-correct-invariants-synthesizing-inductive-invariants-by-fuzzing-proofs-issta-2022)
  - [ocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions](#octer-documentation-guided-fuzzing-for-testing-deep-learning-api-functions-issta-2022)
  - [Efficient Greybox Fuzzing of Applications in Linux-based IoT Devices via Enhanced User-mode Emulation](#efficient-greybox-fuzzing-of-applications-in-linux-based-iot-devices-via-enhanced-user-mode-emulation-issta-2022)
  - [MDPFuzz: Testing Models Solving Markov Decision Processes](#mdpfuzz-testing-models-solving-markov-decision-processes-issta-2022)
  - [PrIntFuzz: Fuzzing Linux Drivers via Automated Virtual Device Simulation](#printfuzz-fuzzing-linux-drivers-via-automated-virtual-device-simulation-issta-2022)
  - [SLIME: Program-sensitive Energy Allocation for Fuzzing](#slime-program-sensitive-energy-allocation-for-fuzzing-issta-2022)
  - [SnapFuzz: High-Throughput Fuzzing of Network Applications](#snapfuzz-high-throughput-fuzzing-of-network-applications-issta-2022)
  - [TensileFuzz: Facilitating Seed Input Generation in Fuzzing via String Constraint Solving](#tensilefuzz-facilitating-seed-input-generation-in-fuzzing-via-string-constraint-solving-issta-2022)
  - [WASAI: Uncovering Vulnerabilities in Wasm Smart Contracts](#wasai-uncovering-vulnerabilities-in-wasm-smart-contracts-issta-2022)
  - [Unicorn: Detect Runtime Error in Time-Series Databases With Hybrid Input Synthesis](#unicorn-detect-runtime-error-in-time-series-databases-with-hybrid-input-synthesis-issta-2022)
- **ESEC/FSE 2022**
  - [Minerva: Browser API Fuzzing with Dynamic Mod-Ref Analysis](#minerva-browser-api-fuzzing-with-dynamic-mod-ref-analysis-fse-2022)
  - [RoboFuzz: Fuzzing Robotic Systems over Robot Operating System (ROS) for Finding Correctness Bugs](#robofuzz-fuzzing-robotic-systems-over-robot-operating-system-ros-for-finding-correctness-bugs-fse-2022)
  - [SEDiff: Scope-Aware Differential Fuzzing to Test Internal Function Models in Symbolic Execution](#sediff-scope-aware-differential-fuzzing-to-test-internal-function-models-in-symbolic-execution-fse-2022)
  - [Fuzzing Deep-Learning Libraries via Automated Relational API Inference](#fuzzing-deep-learning-libraries-via-automated-relational-api-inference-esec-2022)
- **S&P 2022**
  - [Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis](#effective-seed-scheduling-for-fuzzing-with-graph-centrality-analysis-sp-2022)
  - [BEACON: Directed Grey-Box Fuzzing with Provable Path Pruning](#beacon-directed-grey-box-fuzzing-with-provable-path-pruning-sp-2022)
  - [JIGSAW: Efficient and Scalable Path Constraints Fuzzing](#jigsaw-efficient-and-scalable-path-constraints-fuzzing-sp-2022)
  - [PATA: Fuzzing with Path Aware Taint Analysis](#pata-fuzzing-with-path-aware-taint-analysis-sp-2022)
  - [FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks](#fuzzusb-hybrid-stateful-fuzzing-of-usb-gadget-stacks-fse-2022)
- **NDSS 2022**
  - [Cooper: Testing the Binding Code of Scripting Languages with Cooperative Mutation](#cooper-testing-the-binding-code-of-scripting-languages-with-cooperative-mutation-ndss-2022)
  - [EMS: History-Driven Mutation for Coverage-based Fuzzing](#ems-history-driven-mutation-for-coverage-based-fuzzing-ndss-2022)
  - [Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and the Emulators](#semantic-informed-driver-fuzzing-without-both-the-hardware-devices-and-the-emulators-icse-2022)
  - [MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing](#mobfuzz-adaptive-multi-objective-optimization-in-gray-box-fuzzing-ndss-2022)
  - [Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection](#context-sensitive-and-directional-concurrency-fuzzing-for-data-race-detection-ndss-2022)
  - [datAFLow: Towards a Data-Flow-Guided Fuzzer](#dataflow-towards-a-data-flow-guided-fuzzer-ndss-2022)
- **USENIX SEC 2022**
  - [SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel](#syzscope-revealing-high-risk-security-impacts-of-fuzzer-exposed-bugs-in-linux-kernel-usenix-sec22)
  - [MundoFuzz: Hypervisor Fuzzing with Statistical Coverage Testing and Grammar Inference]
  - [TheHuzz: Instruction Fuzzing of Processors Using Golden-Reference Models for Finding Software-Exploitable Vulnerabilities]
  - [Morphuzz: Bending (Input) Space to Fuzz Virtual Devices]
  - [Fuzzware: Using Precise MMIO Modeling for Effective Firmware Fuzzing](#fuzzware-using-precise-mmio-modeling-for-effective-firmware-fuzzing)
  - [FuzzOrigin: Detecting UXSS vulnerabilities in Browsers through Origin Fuzzing]
  - [Drifuzz: Harvesting Bugs in Device Drivers from Golden Seeds](#drifuzz-harvesting-bugs-in-device-drivers-from-golden-seeds-usenix-sec-2022)
  - [Fuzzing Hardware Like Software](#fuzzing-hardware-like-software-usenix-sec-2022)
  - [BrakTooth: Causing Havoc on Bluetooth Link Manager via Directed Fuzzing](#braktooth-causing-havoc-on-bluetooth-link-manager-via-directed-fuzzing-usenix-sec-2022)
  - [Stateful Greybox Fuzzing](#stateful-greybox-fuzzing-usenix-security-2022)
  - [AmpFuzz: Fuzzing for Amplification DDoS Vulnerabilities]
  - [SGXFuzz: Efficiently Synthesizing Nested Structures for SGX Enclave Fuzzing](#sgxfuzz-efficiently-synthesizing-nested-structures-for-sgx-enclave-fuzzing-usenix-security2022)
  - [FRAMESHIFTER: Manipulating HTTP/2 Frame Sequences with Fuzzing](#frameshifter-manipulating-http2-frame-sequences-with-fuzzing-usenix-security2020)
  - [FIXREVERTER: A Realistic Bug Injection Methodology for Benchmarking Fuzz Testing](#fixreverter-a-realistic-bug-injection-methodology-for-benchmarking-fuzz-testing-usenix-sec22)
  - [StateFuzz: System Call-Based State-Aware Linux Driver Fuzzing](#statefuzz-system-call-based-state-aware-linux-driver-fuzzing-usenix-sec-2022)
- **ICSE 2022**
  - [?AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware](#?afl-non-intrusive-feedback-driven-fuzzing-for-microcontroller-firmware-icse-2022)
  - [BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing](#bedivfuzz-integrating-behavioral-diversity-into-generator-based-fuzzing-icse-2022)
  - [CONFETTI: Amplifying Concolic Guidance for Fuzzers](#confetti-amplifying-concolic-guidance-for-fuzzers-icse-2022)
  - [Demystifying the Dependency Challenge in Kernel Fuzzing](#demystifying-the-dependency-challenge-in-kernel-fuzzing-icse-2022)
  - [Evaluating and Improving Neural Program-Smoothing-based Fuzzing](#evaluating-and-improving-neural-program-smoothing-based-fuzzing-icse-2022)
  - [Fuzzing Class Specifications](#fuzzing-class-specifications-icse-2022)
  - [GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs](#graphfuzz-library-api-fuzzing-with-lifetime-aware-dataflow-graphs-icse-2022)
  - [Linear-time Temporal Logic guided Greybox Fuzzing](#linear-time-temporal-logic-guided-greybox-fuzzing-icse-2022)
  - [Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing](#muffin-testing-deep-learning-libraries-via-neural-architecture-fuzzing-icse-2022)
  - [One Fuzzing Strategy to Rule Them All](#one-fuzzing-strategy-to-rule-them-all-icse-2022)
  - [On the Reliability of Coverage-Based Fuzzer Benchmarking](#on-the-reliability-of-coverage-based-fuzzer-benchmarking-icse-2022)
  - [Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States](#path-transitions-tell-more-optimizing-fuzzing-schedules-via-runtime-program-states-icse-2022)
  - [R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing](#r2z2-detecting-rendering-regressions-in-web-browsers-through-differential-fuzz-testing-icse-2022)
  - [Semantic Image Fuzzing of AI Perception Systems](#semantic-image-fuzzing-of-ai-perception-systems)
  - [Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source](#free-lunch-for-testing-fuzzing-deep-learning-libraries-from-open-source-icse-2022)
  - [WindRanger: A Directed Greybox Fuzzer driven by Deviation Basic Block](#windranger-a-directed-greybox-fuzzer-driven-by-deviation-basic-block-icse-2022)
  - [MOREST: Model-based RESTful API Testing with Execution Feedback](#morest-model-based-restful-api-testing-with-execution-feedback-icse-2022)
  - [Controlled Concurrency Testing via Periodical Scheduling](#controlled-concurrency-testing-via-periodical-scheduling-icse-2022)
  - [Combinatorial Testing of RESTful APIs](#combinatorial-testing-of-restful-apis-icse-2022)
  - [Automated Testing of Software that Uses Machine Learning APIs](#automated-testing-of-software-that-uses-machine-learning-apis-icse-2022)
  - [FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems](#fadatest-fast-and-adaptive-performance-regression-testing-of-dynamic-binary-translation-systems-icse-2022)
  - [Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks](#nessie-automatically-testing-javascript-apis-with-asynchronous-callbacks-icse-2022)
- **SANER 2022**
  - [FuzzingDriver: the Missing Dictionary to Increase Code Coverage in Fuzzers](#fuzzingdriver-the-missing-dictionary-to-increase-code-coverage-in-fuzzers-saner-2022)
- **Others 2022**
  - [UltraFuzz: Towards Resource-saving in Distributed Fuzzing (TSE 2022)](#ultrafuzz-towards-resource-saving-in-distributed-fuzzing-tse-2022)
  - [Fuzzing with automatically controlled interleavings to detect concurrency bugs (The Journal of Systems & Software)](#fuzzing-with-automatically-controlled-interleavings-to-detect-concurrency-bugs-the-journal-of-systems--software-2022)
  - [ROZZ: Property-based Fuzzing for Robotic Programs in ROS (ICRA 2022)]
  - [SnapFuzz: An Efficient Fuzzing Framework for Network Applications (2022)](#snapfuzz-an-efficient-fuzzing-framework-for-network-applications-2022)
  - [Efficient ECU Analysis Technology through Structure-aware CAN Fuzzing (Access 2022)](#efficient-ecu-analysis-technology-through-structure-aware-can-fuzzing-access-2022)
  - [Smart Contract Fuzzing for Enterprises: The Language Agnostic Way (COMSNETS 2022)](#smart-contract-fuzzing-for-enterprises-the-language-agnostic-way-comsnets-2022)
  - [spotFuzzer: Static Instrument and Fuzzing Windows COTs (2022)](#spotfuzzer-static-instrument-and-fuzzing-windows-cots-2022)
  - [Mutation Analysis: Answering the Fuzzing Challenge (2022)](#mutation-analysis-answering-the-fuzzing-challenge-2022)
  - [SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots (2022)](#snpsfuzzer-a-fast-greybox-fuzzer-for-stateful-network-protocols-using-snapshots-2022)
  - [Better Pay Attention Whilst Fuzzing (2022)](#better-pay-attention-whilst-fuzzing-2022)
  - [looking for lacunae in bitcoin cores fuzzing efforts](#looking-for-lacunae-in-bitcoin-cores-fuzzing-efforts-2022)
  - [FEFuzzer: Hybrid Files Fuzzing Tool (2022)](#fefuzzer-hybrid-files-fuzzing-tool-2022)
- **ASE 2021**
  - [CorbFuzz: Checking Browser Security Policies with Fuzzing](#corbfuzz-checking-browser-security-policies-with-fuzzing-ase-2021)
  - [Scalable Fuzzing of Program Binaries with E9AFL](#scalable-fuzzing-of-program-binaries-with-e9afl-ase-2021)
  - [SMARTIAN : Enhancing Smart Contract Fuzzing with Static and Dynamic Data-Flow Analyses](#smartian--enhancing-smart-contract-fuzzing-with-static-and-dynamic-data-flow-analyses-ase-2021)
  - [Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing](#towards-systematic-and-dynamic-task-allocation-for-collaborative-parallel-fuzzing-ase-2021-nier)
  - [Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA](#improving-configurability-of-unit-level-continuous-fuzzing-an-industrial-case-study-with-sap-hana-ase-2021-industry)
  - [Understanding and Detecting Performance Bugs in Markdown Compilers](#understanding-and-detecting-performance-bugs-in-markdown-compilers-ase-2021)
  - [Rust Library Fuzzing via API Dependency Graph Traversal](#rust-library-fuzzing-via-api-dependency-graph-traversal-ase-2021)
  - [InstruGuard: Find and Fix Instrumentation Errors for Coverage-based Greybox Fuzzing](#instruguard-find-and-fix-instrumentation-errors-for-coverage-based-greybox-fuzzing-ase-2021)
- **ACSAC 2021**
  - [Westworld: Fuzzing-Assisted Remote Dynamic Symbolic Execution of Smart Apps on IoT Cloud Platforms](#westworld-fuzzing-assisted-remote-dynamic-symbolic-execution-of-smart-apps-on-iot-cloud-platforms-acsac-2021)
  - [ICS3Fuzzer: A Framework for Discovering Protocol Implementation Bugs in ICS Supervisory Software by Fuzzing](#ics3fuzzer-a-framework-for-discovering-protocol-implementation-bugs-in-ics-supervisory-software-by-fuzzing-acsac-2021)
- **SOSP 2021**
  - [HEALER: Relation Learning Guided Kernel Fuzzing](#healer-relation-learning-guided-kernel-fuzzing-sosp-2021)
- **S&P 2021**
  - [DiFuzzRTL: Differential Fuzz Testing to Find CPU Bugs](#difuzzrtl-differential-fuzz-testing-to-find-cpu-bug-sp-2021)
  - [STOCHFUZZ: Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting](#stochfuzz-sound-and-cost-effective-fuzzing-of-stripped-binaries-by-incremental-and-stochastic-rewriting-sp-2021)
  - [NTFUZZ: Enabling Type-Aware Kernel Fuzzing on Windows with Static Binary Analysis](#ntfuzz-enabling-type-aware-kernel-fuzzing-on-windows-with-static-binary-analysissp-2021)
  - [DIANE: Identifying Fuzzing Triggers in Apps to Generate Under-constrained Inputs for IoT Devices](#diane-identifying-fuzzing-triggers-in-apps-to-generate-under-constrained-inputs-for-iot-devices-sp-2020)
  - [One Engine to Fuzz 'em All: Generic Language Processor Testing with Semantic Validation](#one-engine-to-fuzz-em-all-generic-language-processor-testing-with-semantic-validation-sp-2021)
- **ISSTA 2021**
  - [Fuzzing SMT Solvers via Two-Dimensional Input Space Exploration](#fuzzing-smt-solvers-via-two-dimensional-input-space-exploration-issta-2021)
  - [Gramatron: Effective Grammar-Aware Fuzzing](#gramatron-effective-grammar-aware-fuzzing-issta-2021)
  - [QFuzz: Quantitative Fuzzing for Side Channels](#qfuzz-quantitative-fuzzing-for-side-channels-issta-2021)
  - [Seed Selection for Successful Fuzzing](#seed-selection-for-successful-fuzzing-issta-2021)
  - [ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing](#profuzzbench---a-benchmark-for-stateful-protocol-fuzzing-issta-2021)
- **ESEC/FSE 2021**
  - [Skeletal Approximation Enumeration for SMT Solver Testing ](#skeletal-approximation-enumeration-for-smt-solver-testing-fse-2021)
  - [Estimating Residual Risk in Greybox Fuzzing](#estimating-residual-risk-in-greybox-fuzzing-fse-2021)
  - [HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications](#heterofuzz-fuzz-testing-to-detect-platform-dependent-divergence-for-heterogeneous-applications-fse-2021)
  - [FuzzBench: An Open Fuzzer Benchmarking Platform and Service](#fuzzbench-an-open-fuzzer-benchmarking-platform-and-service-fse-2021)
- **CCS 2021**
  - [Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference](#snipuzz-black-box-fuzzing-of-iot-firmware-via-message-snippet-inference-ccs-2021)
  - [Hardware Support to Improve Fuzzing Performance and Precision](#hardware-support-to-improve-fuzzing-performance-and-precision-ccs-2021)
  - [SoFi: Reflection-Augmented Fuzzing for JavaScript Engines](#sofi-reflection-augmented-fuzzing-for-javascript-engines-ccs-2021)
  - [T-Reqs: HTTP Request Smuggling with Differential Fuzzing](#t-reqs-http-request-smuggling-with-differential-fuzzing-ccs-2021)
  - [V-SHUTTLE: Scalable and Semantics-Aware Hypervisor Fuzzing](#v-shuttle-scalable-and-semantics-aware-hypervisor-fuzzing-ccs-2021)
  - [Same Coverage, Less Bloat: Accelerating Binary-only Fuzzing with Coverage-preserving Coverage-guided Tracing](#same-coverage-less-bloat-accelerating-binary-only-fuzzing-with-coverage-preserving-coverage-guided-tracing-ccs-2021)
  - [Regression Greybox Fuzzing](#regression-greybox-fuzzing-ccs-2021)
  - [SyzGen: Automated Generation of Syscall Specification of Closed-Source macOS Drivers](#SyzGen-Automated-Generation-of-Syscall-Specification-of-Closed-Source-macOS-Drivers-ccs-2021)
- **OOPSLA 2021**
  - [Fuzzing Channel-Based Concurrency Runtimes using Types and Effects](#fuzzing-channel-based-concurrency-runtimes-using-types-and-effects-slides-oopsla-2021)
  - [Fully Automated Functional Fuzzing of Android Apps for Detecting Non-crashing Logic Bugs](#fully-automated-functional-fuzzing-of-android-apps-for-detecting-non-crashing-logic-bugs-oopsla-2021)
- **PLDI 2021**
  - [Automated Conformance Testing for JavaScript Engines via Deep Compiler Fuzzing](#automated-conformance-testing-for-javascript-engines-via-deep-compiler-fuzzing-pldi-2021)
- **ICSE 2021**
  - [Fuzzing Symbolic Expressions](#fuzzing-symbolic-expressions-icse-2021)
  - [Graph-based Fuzz Testing for Deep Learning Inference Engines](#graph-based-fuzz-testing-for-deep-learning-inference-engines-icse-2021)
  - [Growing A Test Corpus with Bonsai Fuzzing](#growing-a-test-corpus-with-bonsai-fuzzing-icse-2021)
  - [Industry Practice of Coverage-Guided Enterprise-Level DBMS Fuzzing](#industry-practice-of-coverage-guided-enterprise-level-dbms-fuzzing--icse-2021)
  - [IntelliGen: Automatic Driver Synthesis for Fuzz Testing](#intelligen-automatic-driver-synthesis-for-fuzz-testing-icse-2021-seip)
- **USENIX ATC 2021**
  - [RIFF: Reduced Instruction Footprint for Coverage-Guided Fuzzing](#riff-reduced-instruction-footprint-for-coverage-guided-fuzzing-usenix-atc-2021)
  - [TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing](#tcp-fuzz-detecting-memory-and-semantic-bugs-in-tcp-stacks-with-fuzzing-usenix-atc-2021)
- **USENIX Security 2021**
  - [Constraint-guided Directed Greybox Fuzzing](#constraint-guided-directed-greybox-fuzzing-usenix-security2021)
  - [UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers](#unifuzz-a-holistic-and-pragmatic-metrics-driven-platform-for-evaluating-fuzzers-usenix-security2021)
  - [Nyx: Greybox Hypervisor Fuzzing using Fast Snapshots and Affine Types](#nyx-greybox-hypervisor-fuzzing-using-fast-snapshots-and-affine-types-usenix-security2021)
  - [Breaking Through Binaries: Compiler-quality Instrumentation for Better Binary-only Fuzzing](#breaking-through-binaries-compiler-quality-instrumentation-for-better-binary-only-fuzzing--usenix-security2021)
  - [The Use of Likely Invariants as Feedback for Fuzzers](#the-use-of-likely-invariants-as-feedback-for-fuzzers-usenix-security2021)
- **NDSS 2021**
  - [Favocado: Fuzzing the Binding Code of JavaScript Engines Using Semantically Correct Test Cases](#favocado-fuzzing-the-binding-code-of-javascript-engines-using-semantically-correct-test-cases-ndss-2021)
  - [WINNIE: Fuzzing Windows Applications with Harness Synthesis and Fast Cloning](#winnie-fuzzing-windows-applications-with-harness-synthesis-and-fast-cloning-ndss-2021)
  - [PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles](#pgfuzz-policy-guided-fuzzing-for-robotic-vehicles-ndss-2021)
  - [Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing](#reinforcement-learning-based-hierarchical-seed-scheduling-for-greybox-fuzzing-ndss-2021)
- **ICST 2021**
  - [Industrial Oriented Evaluation of Fuzzing Techniques](#industrial-oriented-evaluation-of-fuzzing-techniques-icst-2021)
  - [Learning-Based Fuzzing of IoT Message Broker](#learning-based-fuzzing-of-iot-message-brokers-icst-2021)
  - [RiverFuzzRL - an open-source tool to experiment with reinforcement learning for fuzzing](#riverfuzzrl---an-open-source-tool-to-experiment-with-reinforcement-learning-for-fuzzing-icst-2021)
- **SANER 2021**
  - [OTA: An Operation-oriented Time Allocation Strategy for Greybox Fuzzing](#ota-an-operation-oriented-time-allocation-strategy-for-greybox-fuzzing-saner-2021)
- **JFLA 2022**
  - [Alt-Ergo-Fuzz: A fuzzer for the Alt-Ergo SMT solver](#alt-ergo-fuzz-a-fuzzer-for-the-alt-ergo-smt-solver-jfla-2022)
- **Others 2021**
  - [FuSeBMC v. 4: Smart Seed Generation for Hybrid Fuzzing (2021)](#fusebmc-v-4-smart-seed-generation-for-hybrid-fuzzing-2021)
  - [REST API Fuzzing by Coverage Level Guided Blackbox Testing (2021)](#rest-api-fuzzing-by-coverage-level-guided-blackbox-testing-2021)
  - [State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing (2021)](#state-selection-algorithms-and-their-impact-on-the-performance-of-stateful-network-protocol-fuzzing-2021)
  - [DIAR: Removing Uninteresting Bytes from Seeds in Software Fuzzing (2021)](#diar-removing-uninteresting-bytes-from-seeds-in-software-fuzzing-2021)
  - [CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing (2021)](#catchbackdoor-backdoor-testing-by-critical-trojan-neural-path-identification-via-differential-fuzzing-2021)
  - [Emmutaler: Fuzzing the iOS Boot Loader (Thesis 2021)](#emmutaler-fuzzing-the-ios-boot-loader-thesis-2021)
  - [Concolic-Fuzzing of JavaScript Programs using GraalVM and Truffle (SKILL 2021)](#concolic-fuzzing-of-javascript-programs-using-graalvm-and-truffle-skill-2021)
  - [BanditFuzz: Fuzzing SMT Solvers with Multi-agent Reinforcement Learning (FM 2021)](#banditfuzz-fuzzing-smt-solvers-with-multi-agent-reinforcement-learning-fm-2021)
  - [My Fuzzer Beats Them All! Developing a Framework for Fair Evaluation and Comparison of Fuzzers (2021)](#my-fuzzer-beats-them-all-developing-a-framework-for-fair-evaluation-and-comparison-of-fuzzers-2021)
  - [Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph (Access 2021)](#evaluating-code-coverage-for-kernel-fuzzers-via-function-call-graph-access-2021)
  - [A Tight Integration of Symbolic Execution and Fuzzing (short paper 2021)](#a-tight-integration-of-symbolic-execution-and-fuzzing-short-paper-2021)
  - [DeltaFuzz: Historical Version Information Guided Fuzz Testing (Journal of Computer Science and Technology 2021)](#deltafuzz-historical-version-information-guided-fuzz-testing-journal-of-computer-science-and-technology-2021)
  - [ovAFLow: Detecting Memory Corruption Bugs with Fuzzing-based Taint Inference (Journal of Computer Science and Technology 2021)](#ovaflow-detecting-memory-corruption-bugs-with-fuzzing-based-taint-inference-journal-of-computer-science-and-technology-2021)
  - [Machine Learning Guided Cross-Contract Fuzzing (2021)](#machine-learning-guided-cross-contract-fuzzing-2021)
  - [SANRAZOR: Reducing Redundant Sanitizer Checks in C/C++ Programs (OSDI 2021)](#sanrazor-reducing-redundant-sanitizer-checks-in-cc-programs-osdi-2021)
  - [webFuzz: Grey-Box Fuzzing for Web Applications (ESORICS 2021)](#webfuzz-grey-box-fuzzing-for-web-applications-esorics-2021)
  - [Vulnerability-Oriented Fuzz Testing for Connected Autonomous Vehicle Systems (IEEE Transactions on Reliability 2021)](#vulnerability-oriented-fuzz-testing-for-connected-autonomous-vehicle-systems--ieee-transactions-on-reliability-2021)
  - [Duo: Differential Fuzzing for Deep Learning Operators (IEEE Transactions on Reliability 2021)](#duo-differential-fuzzing-for-deep-learning-operators-ieee-transactions-on-reliability-2021)
  - [Rtkaller: State-aware Task Generation for RTOS Fuzzing (EMSOFT 2021)](#rtkaller-state-aware-task-generation-for-rtos-fuzzing-emsoft-2021)
  - [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles (arXiv 2021)](#neural-network-guided-evolutionary-fuzzing-for-finding-traffic-violations-of-autonomous-vehicles-arxiv-2021)
  - [Fuzzing: Cyberphysical System Testing for Security and Dependability (Computer 2021)](#fuzzing-cyberphysical-system-testing-for-security-and-dependability-computer-2021)
  - [ESRFuzzer: an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities (Cybersecurity 2021)](#esrfuzzer-an-enhanced-fuzzing-framework-for-physical-soho-router-devices-to-discover-multi-type-vulnerabilities-cybersecurity-2021)
  - [ICPFuzzer: proprietary communication protocol fuzzing by using machine learning and feedback strategies (Cybersecurity 2021)](#icpfuzzer-proprietary-communication-protocol-fuzzing-by-using-machine-learning-and-feedback-strategies-cybersecurity-2021)
  - [Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection (TSE 2021)](#hashing-fuzzing-introducing-input-diversity-to-improve-crash-detection-tse-2021)
  - [KCFuzz: Directed Fuzzing Based on Keypoint Coverage (ICAIS 2021)](#kcfuzz-directed-fuzzing-based-on-keypoint-coverage-icais-2021)
  - [Token-Level Fuzzing (WiSec'21)](#token-level-fuzzing-wisec-2021)
  - [SHFuzz: A hybrid fuzzing method assisted by static analysis for binary programs (China Communications 2021)](#shfuzz-a-hybrid-fuzzing-method-assisted-by-static-analysis-for-binary-programs-china-communications-2021)
  - [RapidFuzz: Accelerating fuzzing via Generative Adversarial Networks (Neurocomputing 2021)](#rapidfuzz-accelerating-fuzzing-via-generative-adversarial-networks-neurocomputing-2021)
  - [HFContractFuzzer: Fuzzing Hyperledger Fabric Smart Contracts for Vulnerability Detection (EASE 2021)](#hfcontractfuzzer-fuzzing-hyperledger-fabric-smart-contracts-for-vulnerability-detection-ease-2021)
  - [Fuzzing With Optimized Grammar-Aware Mutation Strategies (Access 2021)](#fuzzing-with-optimized-grammar-aware-mutation-strategies-access-2021)
  - [FIRM-COV: High-Coverage Greybox Fuzzing for IoT Firmware via Optimized Process Emulation (Access 2021)](#firm-cov-high-coverage-greybox-fuzzing-for-iot-firmware-via-optimized-process-emulation-access-2021)
  - [Extended grammar-based fuzzing algorithm for JavaScript Engines (2021)](#extended-grammar-based-fuzzing-algorithm-for-javascript-engines-2021)
  - [CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing (2021)](#cocofuzzing-testing-neural-code-models-with-coverage-guided-fuzzing-2021)
  - [FUZZOLIC: Mixing fuzzing and concolic execution (Computers&Security 2021)](#fuzzolic-mixing-fuzzing-and-concolic-execution-computerssecurity-2021)
  - [DirectFuzz: Automated Test Generation for RTL Designs using Directed Graybox Fuzzing (DAC 2021)](#directfuzz-automated-test-generation-for-rtl-designs-using-directed-graybox-fuzzing-dac-2021)
  - [CollabFuzz: A Framework for Collaborative Fuzzing (EuroSec 2021)](#collabfuzz-a-framework-for-collaborative-fuzzing-eurosec-2021)
  - [HDR-Fuzz: Detecting Buffer Overruns using AddressSanitizer Instrumentation and Fuzzing (2021)](#hdr-fuzz-detecting-buffer-overruns-using-addresssanitizer-instrumentation-and-fuzzing-2021)
  - [Unleashing Fuzzing Through Comprehensive, Efficient, and Faithful Exploitable-Bug Exposing (2021)](#unleashing-fuzzing-through-comprehensive-efficient-and-faithful-exploitable-bug-exposing)
  - [Improving Web Application Vulnerability Detection Leveraging Ensemble Fuzzing (ENASE 2021)](#improving-web-application-vulnerability-detection-leveraging-ensemble-fuzzing-enase-2021)
  - [Coverage-guided binary fuzzing with rev.ng and llvm libfuzzer](#coverage-guided-binary-fuzzing-with-revng-and-llvm-libfuzzer)
  - [An Empirical Study of OSS-Fuzz Bugs (MSR 2021)](#an-empirical-study-of-oss-fuzz-bugs-msr-2021)
  - [MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer (Mathematics 2021)](#moofuzz-many-objective-optimization-seed-schedule-for-fuzzer-mathematics-2021)
  - [PMFuzz: Test Case Generation for Persistent Memory Programs (ASPLOS 2021)](#pmfuzz-test-case-generation-for-persistent-memory-programs-asplos-2021)
  - [A Priority Based Path Searching Method for Improving Hybrid Fuzzing (Computers & Security 2021)](#a-priority-based-path-searching-method-for-improving-hybrid-fuzzing-computers--security-2021)
  - [CMFuzz: context-aware adaptive mutation for fuzzers (Empirical Software Engineering 2021)](#cmfuzz-context-aware-adaptive-mutation-for-fuzzers-empirical-software-engineering-2021)
  - [Refined Grey-Box Fuzzing with Sivo (arXiv 2021)](#refined-grey-box-fuzzing-with-sivo-arxiv-2021)
  - [Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing (MDPI 2021)](#constructing-more-complete-control-flow-graphs-utilizing-directed-gray-box-fuzzing-mdpi-2021)
  - [Symbolic Security Predicates: Hunt Program Weaknesses (ISPRAS Open 2021)](#symbolic-security-predicates-hunt-program-weaknesses-ispras-open-2021)
  - [Towards Symbolic Pointers Reasoning in Dynamic Symbolic Execution (IVMEM 2021)](#towards-symbolic-pointers-reasoning-in-dynamic-symbolic-execution-ivmem-2021)
- **CCS 2020**
  - [FREEDOM: Engineering a State-of-the-Art DOM Fuzzer](#freedom-engineering-a-state-of-the-art-dom-fuzzer-ccs-2020)
  - [SQUIRREL: Testing Database Management Systems with Language Validity and Coverage Feedback](#squirrel-testing-database-management-systems-with-language-validity-and-coverage-feedback-ccs-2020)
- **ASE 2020**
  - [BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction](#bigfuzz-efficient-fuzz-testing-for-data-analytics-using-framework-abstraction-ase-2020)
  - [MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools](#mofuzz-a-fuzzer-suite-for-testing-model-driven-software-engineering-tools-ase-2020)
  - [Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and Scheduling](#zeror-speed-up-fuzzing-with-coverage-sensitive-tracing-and-scheduling-ase-2020)
  - [Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing](#generating-highly-structured-input-data-by-combining-search-based-testing-and-grammar-based-fuzzing-ase-2020)
- **ISSTA 2020**
  - [Active Fuzzing for Testing and Securing Cyber-Physical Systems](#active-fuzzing-for-testing-and-securing-cyber-physical-systems-issta-2020)
  - [Learning Input Tokens for Effective Fuzzing](#learning-input-tokens-for-effective-fuzzing-issta-2020)
  - [WEIZZ: Automatic Grey-Box Fuzzing for Structured Binary Formats](#weizz-automatic-grey-box-fuzzing-for-structured-binary-formats)
- **ESEC/FSE 2020**
  - [Fuzzing: On the Exponential Cost of Vulnerability Discovery](#fuzzing-on-the-exponential-cost-of-vulnerability-discovery-fse-2020)
  - [Boosting Fuzzer Efficiency: An Information Theoretic Perspective](#boosting-fuzzer-efficiency-an-information-theoretic-perspective-fse-2020)
  - [CrFuzz: Fuzzing Multi-purpose Programs through Input Validation](#crfuzz-fuzzing-multi-purpose-programs-through-input-validation-fse-2020)
  - [Detecting Critical Bugs in SMT Solvers using Blackbox Mutational Fuzzing](#detecting-critical-bugs-in-smt-solvers-using-blackbox-mutational-fuzzing-fse)
  - [Intelligent REST API Data Fuzzing](#intelligent-rest-api-data-fuzzing-fse-2020)
  - [MTFuzz: Fuzzing with a Multi-task Neural Network](#mtfuzz-fuzzing-with-a-multi-task-neural-network-fse-2020)
- **ACSAC 2020**
  - [DPIFuzz: A Differential Fuzzing Framework to Detect DPI Elusion Strategies for QUIC](#dpifuzz-a-differential-fuzzing-framework-to-detect-dpi-elusion-strategies-for-quic-acsac-2020)
  - [Cupid: Automatic Fuzzer Selection for Collaborative Fuzzing](#cupid-automatic-fuzzer-selection-for-collaborative-fuzzing-acsac-2020)
  - [OmniFuzz: A Flexible Framework for Expediting Bug Finding by Leveraging Past (Mis-)Behavior to Discover New Bugs](#omnifuzz-a-flexible-framework-for-expediting-bug-finding-by-leveraging-past-mis-behavior-to-discover-new-bugs-acsac-2020)
  - [RUSTY: A Fuzzing Tool for Rust](#rusty-a-fuzzing-tool-for-rust-acsac-2020)
- **PLDI 2020**
  - [Validating SMT Solvers via Semantic Fusion](#validating-smt-solvers-via-semantic-fusion-pldi-2020)
- **USENIX Security 2020**
  - [Analysis of DTLS Implementations Using Protocol State Fuzzing](#analysis-of-dtls-implementations-using-protocol-state-fuzzing-usenix-security2020)
  - [Frankenstein: Advanced Wireless Fuzzing to Exploit New Bluetooth Escalation Targets](#frankenstein-advanced-wireless-fuzzing-to-exploit-new-bluetooth-escalation-targets-usenix-security2020)
  - [SpecFuzz: Bringing Spectre-type vulnerabilities to the surface](#specfuzz-bringing-spectre-type-vulnerabilities-to-the-surface-usenix-security2020)
  - [FuzzGen: Automatic Fuzzer Generation](#fuzzgen-automatic-fuzzer-generation-usenix-security2020)
  - [MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs](#muzz-thread-aware-grey-box-fuzzing-for-effective-bug-hunting-in-multithreaded-programs-usenix-security2020)
  - [Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer](#montage-a-neural-network-language-model-guided-javascript-engine-fuzzer-usenix-security2020)
  - [GREYONE: Data Flow Sensitive Fuzzing](#greyone-data-flow-sensitive-fuzzing-usenix-security2020)
  - [FuzzGuard: Filtering out Unreachable Inputs in Directed Grey-box Fuzzing through Deep Learning](#fuzzguard-filtering-out-unreachable-inputs-in-directed-grey-box-fuzzing-through-deep-learning-usenix-security2020)
  - [ParmeSan: Sanitizer-guided Greybox Fuzzing](#parmesan-sanitizer-guided-greybox-fuzzing-usenix-security2020)
  - [EcoFuzz: Adaptive Energy-Saving Greybox Fuzzing as a Variant of the Adversarial Multi-Armed Bandit](#ecofuzz-adaptive-energy-saving-greybox-fuzzing-as-a-variant-of-the-adversarial-multi-armed-bandit-usenix-security2020)
  - [FANS: Fuzzing Android Native System Services via Automated Interface Analysis](#fans-fuzzing-android-native-system-services-via-automated-interface-analysis-usenix-security2020)
  - [Fuzzing Error Handling Code using Context-Sensitive Software Fault Injection](#fuzzing-error-handling-code-using-context-sensitive-software-fault-injection-usenix-security2020)
  - [USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation](#usbfuzz-a-framework-for-fuzzing-usb-drivers-by-device-emulation-usenix-security2020)
  - [AFL++: Combining Incremental Steps of Fuzzing Research (USENIX Woot2020)](#afl-combining-incremental-steps-of-fuzzing-research-usenix-woot2020)
- **ICSE 2020**
  - [Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities](#typestate-guided-fuzzer-for-discovering-use-after-free-vulnerabilities-icse-2020)
  - [MemLock: Memory Usage Guided Fuzzing](#memlock-memory-usage-guided-fuzzing-icse2020)
  - [Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference](#ankou-guiding-grey-box-fuzzing-towards-combinatorial-difference-icse-2020)
  - [JVM Fuzzing for JIT-Induced Side-Channel Detection](#jvm-fuzzing-for-jit-induced-side-channel-detection-icse-2020)
  - [Targeted Greybox Fuzzing with Static Lookahead Analysis](#targeted-greybox-fuzzing-with-static-lookahead-analysis-icse-2020)
  - [Fuzz Testing based Data Augmentation to Improve Robustness of Deep Neural Networks](#fuzz-testing-based-data-augmentation-to-improve-robustness-of-deep-neural-networks-icse-2020)
  - [sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts](#sfuzz-an-efficient-adaptive-fuzzer-for-solidity-smart-contracts-icse-2020)
  - [HyDiff: Hybrid Differential Software Analysis](#hydiff-hybrid-differential-software-analysis-icse-2020)
  - [Automatically Testing String Solvers](#automatically-testing-string-solvers-icse-2020)
- **NDSS 2020**
  - [HYPER-CUBE: High-Dimensional Hypervisor Fuzzing](#hyper-cube-high-dimensional-hypervisor-fuzzing-ndss-2020)
  - [HotFuzz: Discovering Algorithmic Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing](#hotfuzz-discovering-algorithmic-denial-of-service-vulnerabilities-through-guided-micro-fuzzing-ndss-2020)
  - [HFL: Hybrid Fuzzing on the Linux Kernel](#hfl-hybrid-fuzzing-on-the-linux-kernel-ndss-2020)
  - [Not All Coverage Measurements Are Equal: Fuzzing by Coverage Accounting for Input Prioritization](#not-all-coverage-measurements-are-equal-fuzzing-by-coverage-accounting-for-input-prioritization-ndss-2020)
- **S&P 2020**
  - [SAVIOR: Towards Bug-Driven Hybrid Testing](#savior-towards-bug-driven-hybrid-testing-sp-2020)
  - [RetroWrite: Statically Instrumenting COTS Binaries for Fuzzing and Sanitization](#retrowrite-statically-instrumenting-cots-binaries-for-fuzzing-and-sanitization-sp-2020)
  - [IJON: Exploring Deep State Spaces via Fuzzing](#ijon-exploring-deep-state-spaces-via-fuzzing-sp-2020)
  - [PANGOLIN: Incremental Hybrid Fuzzing with Polyhedral Path Abstraction](#pangolin-incremental-hybrid-fuzzing-with-polyhedral-path-abstraction-sp-2020)
  - [KRace: Data Race Fuzzing for Kernel File Systems](#krace-data-race-fuzzing-for-kernel-file-systems-sp-2020)
  - [Fuzzing JavaScript Engines with Aspect-preserving Mutation](#fuzzing-javascript-engines-with-aspect-preserving-mutation-sp-2020)
- **SANER 2020**
  - [ETHPLOIT: From Fuzzing to Efficient Exploit Generation against Smart Contracts](#ethploit-from-fuzzing-to-efficient-exploit-generation-against-smart-contracts-saner2020)
  - [Sequence directed hybrid fuzzing](#sequence-directed-hybrid-fuzzing-saner-2020)
- **ICST 2020**
  - [Language-Agnostic Generation of Compilable Test Programs](#language-agnostic-generation-of-compilable-test-programs-icst-2020)
  - [ct-fuzz: Fuzzing for Timing Leaks](#ct-fuzz-fuzzing-for-timing-leaks-icst-2020)
  - [AFLNET: A Greybox Fuzzer for Network Protocols](#aflnet-a-greybox-fuzzer-for-network-protocols-icst-2020)
- **ASIACCS 2020**
  - [PathAFL: Path-Coverage Assisted Fuzzing](#pathafl-path-coverage-assisted-fuzzing-asia-ccs-2020)
- **Others 2020**
  - [Reproducible Crashes: Fuzzing Pharo by Mutating the Test Methods (IWST20 2020)](#reproducible-crashes-fuzzing-pharo-by-mutating-the-test-methods-iwst20-2020)
  - [Finding race conditions in Kernels: from fuzzing to symbolic exection (2020)](#finding-race-conditions-in-kernels-from-fuzzing-to-symbolic-exection-2020)
  - [BanditFuzz: Fuzzing SMT Solvers with Reinforcement Learning (2020)](#banditfuzz-fuzzing-smt-solvers-with-reinforcement-learning-2020)
  - [Fuzzing Technique in Web Applications and Beyond (MCTE 2020)](#fuzzing-technique-in-web-applications-and-beyond-mcte-2020)
  - [Vulnerability Detection in SIoT Applications: A Fuzzing Method on their Binaries (IEEE Transactions on Network Science and Engineering 2020)](#vulnerability-detection-in-siot-applications-a-fuzzing-method-on-their-binaries-ieee-transactions-on-network-science-and-engineering-2020)
  - [MaxAFL: Maximizing Code Coverage with a Gradient-Based Optimization Technique (Electronics 2020)](#maxafl-maximizing-code-coverage-with-a-gradient-based-optimization-technique-electronics-2020)
  - [CSEFuzz: Fuzz Testing Based on Symbolic Execution (Access 2020)](#csefuzz-fuzz-testing-based-on-symbolic-execution-access-2020)
  - [A Quantitative Comparison of Covera (AST 2020)](#a-quantitative-comparison-of-covera-ast-2020)
  - [Finding Bugs in File Systems with an Extensible Fuzzing Framework (TOS 2020)](#finding-bugs-in-file-systems-with-an-extensible-fuzzing-framework-tos-2020)
  - [ICS Protocol Fuzzing: Coverage Guided Packet Crack and Generation (DAC 2020)](#ics-protocol-fuzzing-coverage-guided-packet-crack-and-generation-dac-2020)
  - [Finding Security Vulnerabilities in Network Protocol Implementations (Arxiv 2020)](#finding-security-vulnerabilities-in-network-protocol-implementations-arxiv-2020)
  - [Coverage Guided Differential Adversarial Testing of Deep Learning Systems](#coverage-guided-differential-adversarial-testing-of-deep-learning-systems-tnse-2020)
  - [Fwâfuzz: A code coverageâguided fuzzing framework for network protocols on firmware](#fw%e2%80%90fuzz-a-code-coverage%e2%80%90guided-fuzzing-framework-for-network-protocols-on-firmware-2020)
  - [Greybox Fuzzing Based on Ant Colony Algorithm](#greybox-fuzzing-based-on-ant-colony-algorithm-aina-2020)    
  - [MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing](#meuzz-smart-seed-scheduling-for-hybrid-fuzzing)
  - [Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities](#binary-level-directed-fuzzing-for-use-after-free-vulnerabilities-raid-2020)
  - [Smart seed selection-based effective black box fuzzing for IIoT protocol](#smart-seed-selection-based-effective-black-box-fuzzing-for-iiot-protocol)
  - [RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation](#rdfuzz-accelerating-directed-fuzzing-with-intertwined-schedule-and-optimized-mutation-2020)
  - [A deep convolution generative adversarial networks based fuzzing framework for industry control protocols](#a-deep-convolution-generative-adversarial-networks-based-fuzzing-framework-for-industry-control-protocols)
  - [TOFU: Target-Oriented FUzzer](#tofu-target-oriented-fuzzer-arxiv-2020)
  - [BaseSAFE: Baseband SAnitized Fuzzing through Emulation](#basesafe-baseband-sanitized-fuzzing-through-emulation-wisec-2020)
  - [Sydr: Cutting Edge Dynamic Symbolic Execution (ISPRAS Open 2020)](#sydr-cutting-edge-dynamic-symbolic-execution-ispras-open-2020)
- **ACSAC 2019**
  - [Opening Pandoraâs Box through ATFuzzer: Dynamic Analysis of AT Interface for AndroidSmartphones](#Opening-Pandoras-Box-through-ATFuzzer-Dynamic-Analysis-of-AT-Interface-for-Android-Smartphones-ACSAC-2019)
- **OOPSLA 2019**
  - [FuzzFactory: Domain-Specific Fuzzing with Waypoints](#fuzzfactory-domain-specific-fuzzing-with-waypoints-oopsla-2019)
  - [Compiler Fuzzing: How Much Does It Matter](#compiler-fuzzing-how-much-does-it-matter-oopsla2019)
- **TSE 2019**  
  - [The Art, Science, and Engineering of Fuzzing: A Survey](#the-art-science-and-engineering-of-fuzzing-a-survey)
  - [Smart Greybox Fuzzing](#smart-greybox-fuzzing-tse-2019)
- **Access 2019**
  - [NeuFuzz: Efficient Fuzzing With Deep Neural Network](#neufuzz-efficient-fuzzing-with-deep-neural-network-access-2019)
  - [LearnAFL: Greybox Fuzzing With Knowledge Enhancement](#learnafl-greybox-fuzzing-with-knowledge-enhancement-access-2019)
- **CCS 2019**
  - [Intriguer: Field-Level Constraint Solving for Hybrid Fuzzing](#intriguer-field-level-constraint-solving-for-hybrid-fuzzing-ccs-2019)
  - [Learning to Fuzz from Symbolic Execution with Application to Smart Contracts](#learning-to-fuzz-from-symbolic-execution-with-application-to-smart-contracts-ccs-2019)
  - [Matryoshka: fuzzing deeply nested branches](#matryoshka-fuzzing-deeply-nested-branches-ccs-2019)
  - [Different is Good: Detecting the Use of Uninitialized Variables through Differential Replay](#different-is-good-detecting-the-use-of-uninitialized-variables-through-differential-replay-ccs-2019)
  - [Gollum: Modular and Greybox Exploit Generation for Heap Overflows in Interpreters](#gollum-modular-and-greybox-exploit-generation-for-heap-overflows-in-interpreters-ccs-2019)
  - [Poster: Fuzzing IoT Firmware via Multi-stage Message Generation](#poster-fuzzing-iot-firmware-via-multi-stage-message-generation-ccs-2019)
- **S&P 2019**
  - [NEUZZ: Efficient Fuzzing with Neural Program Smoothing](#neuzz-efficient-fuzzing-with-neural-program-smoothing-sp-2019)
  - [Fuzzing File Systems via Two-Dimensional Input Space Exploration](#fuzzing-file-systems-via-two-dimensional-input-space-exploration-sp-2019)
  - [ProFuzzer: On-the-fly Input Type Probing for Better Zero-day Vulnerability Discovery](#profuzzer-on-the-fly-input-type-probing-for-better-zero-day-vulnerability-discovery-sp-2019)
  - [Razzer: Finding Kernel Race Bugs through Fuzzing](#razzer-finding-kernel-race-bugs-through-fuzzing-sp-2019)
  - [Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing](#full-speed-fuzzing-reducing-fuzzing-overhead-through-coverage-guided-tracing-sp-2019)
- **USENIX Security 2019**
  - [MOPT: Optimize Mutation Scheduling for Fuzzers](#mopt-optimize-mutation-scheduling-for-fuzzers-usenix-security2019)
  - [Antifuzz: impeding fuzzing audits of binary executables](#antifuzz-impeding-fuzzing-audits-of-binary-executables-usenix-security2019)
  - [FUZZIFICATION: Anti-Fuzzing Technique](#fuzzification-anti-fuzzing-technique-usenix-security2019)
  - [EnFuzz: Ensemble Fuzzing with Seed Synchronization among Diverse Fuzzers](#enfuzz-ensemble-fuzzing-with-seed-synchronization-among-diverse-fuzzers-usenix-security2019)
  - [GRIMOIRE : Synthesizing Structure while Fuzzing](#grimoire-synthesizing-structure-while-fuzzing-usenix-security2019)
  - [RVFuzzer: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Random Testing](#rvfuzzer-finding-input-validation-bugs-in-robotic-vehicles-through-control-guided-random-testing-usenix-security2019)
  - [FIRM-AFL: High-Throughput Greybox Fuzzing of IoT Firmware via Augmented Process Emulation](#firm-afl-high-throughput-greybox-fuzzing-of-iot-firmware-via-augmented-process-emulation-usenix-security2019)
  - [Unicorefuzz: On the Viability of Emulation for Kernelspace Fuzzing](#unicorefuzz-on-the-viability-of-emulation-for-kernelspace-fuzzing-usenix-woot19)
- **ASE 2019**
  - [Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences](#learning-guided-network-fuzzing-for-testing-cyber-physical-system-defences-ase-2019)
- **NDSS 2019**
  - [REDQUEEN: Fuzzing with Input-to-State Correspondence](#redqueen-fuzzing-with-input-to-state-correspondence-ndss2019)
  - [PeriScope: An Effective Probing and Fuzzing Framework for the Hardware-OS Boundary](#periscope-an-effective-probing-and-fuzzing-framework-for-the-hardware-os-boundary-ndss2019)
  - [Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice Assistant Applications](#life-after-speech-recognition-fuzzing-semantic-misinterpretation-for-voice-assistant-applications-ndss-2019)
  - [Send Hardest Problems My Way: Probabilistic Path Prioritization for Hybrid Fuzzing](#send-hardest-problems-my-way-probabilistic-path-prioritization-for-hybrid-fuzzing-ndss-2019)
  - [CodeAlchemist: Semantics-Aware Code Generation to Find Vulnerabilities in JavaScript Engines](#codealchemist-semantics-aware-code-generation-to-find-vulnerabilities-in-javascript-engines-ndss-2019)
  - [NAUTILUS: Fishing for Deep Bugs with Grammars](#nautilus-fishing-for-deep-bugs-with-grammars-ndss-2019)
- **ICSE 2019**
  - [DifFuzz: Differential Fuzzing for Side-Channel Analysis](#diffuzz-differential-fuzzing-for-side-channel-analysis-icse-2019)
  - [Deep Differential Testing of JVM Implementations](#deep-differential-testing-of-jvm-implementations-icse-2019)
  - [REST-ler: Stateful REST API Fuzzing](#rest-ler-stateful-rest-api-fuzzing-icse-2019)
  - [SLF: Fuzzing without Valid Seed Inputs](#slf-fuzzing-without-valid-seed-inputs-icse-2019)
  - [Superion: Grammar-Aware Greybox Fuzzing](#superion-grammar-aware-greybox-fuzzing-icse-2019)
  - [Hunting for bugs in code coverage tools via randomized differential testing](#hunting-for-bugs-in-code-coverage-tools-via-randomized-differential-testing-icse-2019)
- **ESEC/FSE 2019**
  - [Cerebro: Context-aware Adaptive Fuzzing for Effective Vulnerability Detection](#cerebro-context-aware-adaptive-fuzzing-for-effective-vulnerability-detection-fse-2019)
  - [Just Fuzz It: Solving Floating-Point Constraints Using Coverage-guided Fuzzing](#just-fuzz-it-solving-floating-point-constraints-using-coverage-guided-fuzzing-fse-2019)
  - [FUDGE: Fuzz Driver Generation at Scale](#fudge-fuzz-driver-generation-at-scale-fse-2019)
- **ISSTA 2019**
  - [Semantic Fuzzing with Zest](#semantic-fuzzing-with-zest-issta-2019)
  - [DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks](#deephunter-a-coverage-guided-fuzz-testing-framework-for-deep-neural-networks-issta-2019)
  - [Deferred Concretization in Symbolic Execution via Fuzzing](#deferred-concretization-in-symbolic-execution-via-fuzzing-issta-2019)
- **PLDI 2019**
  - [Parser-Directed Fuzzing](#parser-directed-fuzzing-pldi-2019)
- **ASIACCS 2019**
  - [Ptrix: Efficient Hardware-Assisted Fuzzing for COTS Binary](#ptrix-efficient-hardware-assisted-fuzzing-for-cots-binary-asiaccs-2019)
  - [A Feature-Oriented Corpus for understanding, Evaluating and Improving Fuzz Testing](#a-feature-oriented-corpus-for-understanding-evaluating-and-improving-fuzz-testing-asiaccs-2019)
- **ICST 2019**
  - [MemFuzz: Using Memory Accesses to Guide Fuzzing](#memfuzz-using-memory-accesses-to-guide-fuzzing-icst-2019)
  - [SeqFuzzer: An Industrial Protocol Fuzzing Framework in Deep Learning Perspective](#seqfuzzer-an-industrial-protocol-fuzzing-framework-in-deep-learning-perspective-icst-2019)
- **ISSRE 2019**
  - [Engineering a Better Fuzzer with SynergicallyIntegrated Optimizations](#engineering-a-better-fuzzer-with-synergically-integrated-optimizations-issre-2019)
  - [Fuzzing Error Handling Code in Device Drivers Based on Software Fault Injection](#fuzzing-error-handling-code-in-device-drivers-based-on-software-fault-injection-issre-2019)
- **Other 2019**
  - [Fuzz Testing of Multithreaded Applications Based on Waiting (2019)](#fuzz-testing-of-multithreaded-applications-based-on-waiting-micsecs-2019)
  - [Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols (AAAI 2019)](#leveraging-textual-specifications-for-grammar-based-fuzzing-of-network-protocols-aaai-2019)
  - [Fuzzing JavaScript Environment APIs with Interdependent Function Calls (IFM 2019)](#fuzzing-javascript-environment-apis-with-interdependent-function-calls-ifm-2019)
  - [DeepFuzzer: Accelerated Deep Greybox Fuzzing (TDSC 2019)](#deepfuzzer-accelerated-deep-greybox-fuzzing-tdsc-2019)
  - [CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems](#cagfuzz-coverage-guided-adversarial-generative-fuzzing-testing-of-deep-learning-systems-2019)
  - [Suzzer: A Vulnerability-Guided Fuzzer Based on Deep Learning](#suzzer-a-vulnerability-guided-fuzzer-based-on-deep-learning-inscrypt-2019)
  - [ConFuz: A Concurrency Fuzzer](#confuzz-a-concurrency-fuzzer-2019)
  - [INSTRCR: Lightweight instrumentation optimization based on coverage-guided fuzz testing](#instrcr-lightweight-instrumentation-optimization-based-on-coverage-guided-fuzz-testing-ccet-2019)
  - [HFuzz: Towards automatic fuzzing testing of NB-IoT core network protocols implementations](#hfuzz-towards-automatic-fuzzing-testing-of-nb-iot-core-network-protocols-implementations-fgcs-2019)
  - [Study and Comparison of General Purpose Fuzzers](#study-and-comparison-of-general-purpose-fuzzers)
  - [From proof-of-concept to exploitable](#from-proof-of-concept-to-exploitable-cybersecurity-2019)
  - [Sequence coverage directed greybox fuzzing](#sequence-coverage-directed-greybox-fuzzing-icpc-2019)
  - [Field-aware Evolutionary Fuzzing Based on Input Specifications and Vulnerability Metrics](#field-aware-evolutionary-fuzzing-based-on-input-specifications-and-vulnerability-metrics-2019)
  - [Fuzzing IPC with Knowledge Inference](#fuzzing-ipc-with-knowledge-inference-srds-2019)
  - [Be Sensitive and Collaborative: Analyzing Impact of Coverage Metrics in Greybox Fuzzing](#be-sensitive-and-collaborative-analyzing-impact-of-coverage-metrics-in-greybox-fuzzing-raid-2019)
  - [Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems](#hydra-an-extensible-fuzzing-framework-for-finding-semantic-bugs-in-file-systems-sosp-2019)
  - [Exploring Effective Fuzzing Strategies to Analyze Communication Protocols](#exploring-effective-fuzzing-strategies-to-analyze-communication-protocols-feast-2019)
  - [V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing](#v-fuzz-vulnerability-oriented-evolutionary-fuzzing-arxiv-2019)
- **S&P 2018**
  - [T-Fuzz: fuzzing by program transformation](#t-fuzz-fuzzing-by-program-transformation-sp-2018)
  - [Angora: Efficient Fuzzing by Principled Search](#angora-efficient-fuzzing-by-principled-search-sp-2018)
  - [CollAFL: Path Sensitive Fuzzing](#collafl-path-sensitive-fuzzing-sp-2018)
- **USENIX Security 2018**
  - [FUZE: Towards Facilitating Exploit Generation for Kernel Use-After-Free Vulnerabilities](#fuze-towards-facilitating-exploit-generation-for-kernel-use-after-free-vulnerabilities-usenix-security2018)
  - [MoonShine: Optimizing OS Fuzzer Seed Selection with Trace Distillation](#moonshine-optimizing-os-fuzzer-seed-selection-with-trace-distillation-usenix-security2018)
  - [QSYM : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing](#qsym-a-practical-concolic-execution-engine-tailored-for-hybrid-fuzzing-usenix-security2018)
- **CCS 2018**
  - [Evaluating fuzz testing](#evaluate-fuzzing)
  - [Hawkeye Towards a Desired Directed Grey-box Fuzzer](#hawkeye-towards-a-desired-directed-grey-box-fuzzer-ccs-2018)
  - [Revery: From Proof-of-Concept to Exploitable](#revery-from-proof-of-concept-to-exploitable-ccs-2018)
- **NDSS 2018**
  - [IoTFuzzer: Discovering Memory Corruptions in IoT Through App-based Fuzzing](#iotfuzzer-discovering-memory-corruptions-in-iot-through-app-based-fuzzing-ndss-2018)
  - [What You Corrupt Is Not What You Crash: Challenges in Fuzzing Embedded Devices](#what-you-corrupt-is-not-what-you-crash-challenges-in-fuzzing-embedded-devices-ndss-2018)
  - [Enhancing Memory Error Detection for Large-Scale Applications and Fuzz Testing](#enhancing-memory-error-detection-for-large-scale-applications-and-fuzz-testing-ndss-2018)
  - [INSTRIM Lightweight Instrumentation for Coverage-guided Fuzzing](#instrim-lightweight-instrumentation-for-coverage-guided-fuzzing-ndss-2018-workshop)
- **ICSE 2018**
  - [SAFL: increasing and accelerating testing coverage with symbolic execution and guided fuzzing](#safl-increasing-and-accelerating-testing-coverage-with-symbolic-execution-and-guided-fuzzing-icse-2018)
- **ESEC/FSE 2018**
  - [Singularity: Pattern Fuzzing for Worst Case Complexity](#singularity-pattern-fuzzing-for-worst-case-complexity-fse-2018)
  - [PAFL: Extend Fuzzing Optimizations of Single Mode to Industrial Parallel Mode](#pafl-extend-fuzzingoptimizations-of-single-mode-to-industrial-parallel-mode-esecfse-2018)
  - [FOT: A Versatile, Configurable, Extensible Fuzzing Framework](#fot-a-versatile-configurable-extensible-fuzzing-framework-fse-2018)
  - [DLFuzz: Differential Fuzzing Testing of Deep Learning Systems](#dlfuzz-differential-fuzzing-testing-of-deep-learning-systems-fse-2018)
- **ASE 2018**
  - [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection](#contractfuzzer-fuzzing-smart-contracts-for-vulnerability-detection-ase-2018)
  - [FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage](#fairfuzz-a-targeted-mutation-strategy-for-increasing-greybox-fuzz-testing-coverage-ase-2018)
  - [Differential Program Analysis with Fuzzing and Symbolic Execution](#differential-program-analysis-with-fuzzing-and-symbolic-execution-ase-2018)
- **ISSTA 2018**
  - [Badger: Complexity Analysis with Fuzzing and Symbolic Execution](#badger-complexity-analysis-with-fuzzing-and-symbolic-execution-issta-2018)
  - [Compiler Fuzzing through Deep Learning](#compiler-fuzzing-through-deep-learning-issta-2018)
  - [PerfFuzz: Automatically Generating Pathological Inputs](#perffuzz-automatically-generating-pathological-inputs-issta-2018)
- **CAV 2018**
  - [StringFuzz: A fuzzer for string solvers](#stringfuzz-a-fuzzer-for-string-solvers-cav-2018)
- **ACSAC 2018**
  - [TIFF: Using Input Type Inference To Improve Fuzzing](#tiff-using-input-type-inference-to-improve-fuzzing-acsac-2018)
  - [A Heuristic Framework to Detect Concurrency Vulnerabilities](#a-heuristic-framework-to-detect-concurrency-vulnerabilities-acsac-2018)
- **ICCAD 2018**
  - [RFUZZ: Coverage-Directed Fuzz Testing of RTL on FPGAs](#rfuzz-coverage-directed-fuzz-testing-of-rtl-on-fpgas-iccad-2018)
- **Other 2018**
  - [FuzzerGym: A Competitive Framework for Fuzzing and Learning](#fuzzergym-a-competitive-framework-for-fuzzing-and-learning-arxiv-2018)
  - [MTF-Storm: a high performance fuzzer for Modbus-TCP](#MTF-Storm-a-high-performance-fuzzer-for-Modbus-TCP-ETFA-2018)
- **S&P 2017**
  - [NEZHA: Efficient Domain-Independent Differential Testing](#nezha-efficient-domain-independent-differential-testing-sp-2017)
  - [Skyfire: Data-Driven Seed Generation for Fuzzing](#skyfire-data-driven-seed-generation-for-fuzzing-sp-2017)
- **USENIX Security 2017**
  - [kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels](#kafl-hardware-assisted-feedback-fuzzing-for-os-kernels-usenix-security2017)
  - [CAB-Fuzz: Practical Concolic Testing Techniques for COTS Operating Systems](#cab-fuzz-practical-concolic-testing-techniques-for-cots-operating-systems-usenix-security2017)
- **CCS 2017**
  - [Directed Greybox Fuzzing](#directed-greybox-fuzzing-ccs-2017)
  - [Designing New Operating Primitives to Improve Fuzzing Performance](#designing-new-operating-primitives-to-improve-fuzzing-performance-ccs-2017)
  - [DIFUZE: Interface aware fuzzing for kernel drivers](#difuze-interface-aware-fuzzing-for-kernel-drivers-ccs-2017)
  - [SemFuzz: Semantics-based Automatic Generation of Proof-of-Concept Exploits](#semfuzz-semantics-based-automatic-generation-of-proof-of-concept-exploits-ccs-2017)
  - [SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities](#slowfuzz-automated-domain-independent-detection-of-algorithmic-complexity-vulnerabilities-ccs-2017)
  - [IMF: Inferred Model-based Fuzzer](#imf-inferred-model-based-fuzzer-ccs-2017)
- **NDSS 2017**
  - [VUzzer: Application-aware Evolutionary Fuzzing](#vuzzer-application-aware-evolutionary-fuzzing-ndss-2017)
- **ESEC/FSE 2017**
  - [Steelix: Program-State Based Binary Fuzzing](#steelix-program-state-based-binary-fuzzing-fse-2017)
- **ASE 2017**
  - [Learn&Fuzz: Machine Learning for Input Fuzzing](#learnfuzz-machine-learning-for-input-fuzzing-ase-2017)
  - [In-memory fuzzing for binary code similarity analysis](#in-memory-fuzzing-for-binary-code-similarity-analysis-ase-2017)
- **ISSRE 2017**
  - [Chizpurfle: A Gray-Box Android Fuzzer for Vendor Service Customizations](#chizpurfle-a-gray-box-android-fuzzer-for-vendor-service-customizations-issre-2017)
- **Other 2017**
  - [Bbuzz: A Bit-aware Fuzzing Framework for Network Protocol Systematic Reverse Engineering and Analysis](#bbuzz-a-bit-aware-fuzzing-framework-for-network-protocol-systematic-reverse-engineering-and-analysis-mcc-2017)
- **CCS 2016**
  - [Coverage-based Greybox Fuzzing as Markov Chain](#coverage-based-greybox-fuzzing-as-markov-chain-ccs-2016)
  - [Systematic Fuzzing and Testing of TLS Libraries](#systematic-fuzzing-and-testing-of-tls-libraries-ccs-2016)
- **NDSS 2016**
  - [Driller: Argumenting Fuzzing Through Selective Symbolic Execution](#driller-argumenting-fuzzing-through-selective-symbolic-execution-ndss-2016)
- **PLDI 2016**
  - [Coverage-Directed Differential Testing of JVM Implementations](#coverage-directed-differential-testing-of-jvm-implementations-pldi-2016)
- **S&P 2015**
  - [Program-Adaptive Mutational Fuzzing](#program-adaptive-mutational-fuzzing-sp-2015)
- **USENIX Security 2015**
  - [Protocol State Fuzzing of TLS Implementations](#protocol-state-fuzzing-of-tls-implementations-usenix-security2015)
- **Others**
  - [Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine](#test-data-generation-for-stateful-network-protocol-fuzzing-using-a-rule-based-state-machine-2016)
  - [PULSAR: Stateful Black-Box Fuzzing of Proprietary Network Protocols](#pulsar-stateful-black-box-fuzzing-of-proprietary-network-protocols-springer-cham-2015)
  -  [A Modbus-TCP Fuzzer for testing internetworked industrial systems](#A-Modbus-TCP-Fuzzer-for-testing-internetworked-industrial-systems-ETFA-2015)
  - [SECFUZZ: Fuzz-testing Security Protocols](#secfuzz-fuzz-testing-security-protocols-ast-2012)
  - [AutoFuzz: Automated Network Protocol Fuzzing Framewor](#autofuzz-automated-network-protocol-fuzzing-framework-ijcsns-2010)

# All Papers (Classification according to Subject)

- [Recent Papers Related To Fuzzing](#recent-papers-related-to-fuzzing)
- [All Papers (Classification according to Publication)](#all-papers-classification-according-to-publication)
- [All Papers (Classification according to Subject)](#all-papers-classification-according-to-subject)
- [Survey/Review](#surveyreview)
    - [Fuzzing: Challenges and Reflections](#fuzzing-challenges-and-reflections)
    - [SoK: The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing](#sok-the-progress-challenges-and-perspectives-of-directed-greybox-fuzzing)
    - [Fuzzing: Hack, Art, and Science (CACM 2020)](#fuzzing-hack-art-and-science-cacm-2020)
    - [Survey of Directed Fuzzy Technology](#survey-of-directed-fuzzy-technology)
    - [A Review of Machine Learning Applications in Fuzzing](#a-review-of-machine-learning-applications-in-fuzzing)
    - [A systematic review of fuzzing based on machine learning techniques](#a-systematic-review-of-fuzzing-based-on-machine-learning-techniques)
    - [The Art, Science, and Engineering of Fuzzing: A Survey](#the-art-science-and-engineering-of-fuzzing-a-survey)
    - [Fuzzing: Art, Science, and Engineering](#fuzzing-art-science-and-engineering)
    - [Fuzzing: a survey](#fuzzing-a-survey)
    - [Fuzzing: State of the art](#fuzzing-state-of-the-art)
    - [Fuzzing: A Survey for Roadmap](#fuzzing-a-survey-for-roadmap)
    - [Survey of Software Fuzzing Techniques](#survey-of-software-fuzzing-techniques)
    - [A Review of Fuzzing Tools and Methods](#a-review-of-fuzzing-tools-and-methods)
- [Differential Fuzzing](#differential-fuzzing)
    - [SEDiff: Scope-Aware Differential Fuzzing to Test Internal Function Models in Symbolic Execution (FSE 2022)](#sediff-scope-aware-differential-fuzzing-to-test-internal-function-models-in-symbolic-execution-fse-2022)
    - [T-Reqs: HTTP Request Smuggling with Differential Fuzzing (CCS 2021)](#t-reqs-http-request-smuggling-with-differential-fuzzing-ccs-2021)
    - [CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing (2021)](#catchbackdoor-backdoor-testing-by-critical-trojan-neural-path-identification-via-differential-fuzzing-2021)
    - [Duo: Differential Fuzzing for Deep Learning Operators (IEEE Transactions on Reliability 2021)](#duo-differential-fuzzing-for-deep-learning-operators-ieee-transactions-on-reliability-2021)
    - [DiFuzzRTL: Differential Fuzz Testing to Find CPU Bug (S\&P 2021)](#difuzzrtl-differential-fuzz-testing-to-find-cpu-bug-sp-2021)
    - [DPIFuzz: A Differential Fuzzing Framework to Detect DPI Elusion Strategies for QUIC (ACSAC 2020)](#dpifuzz-a-differential-fuzzing-framework-to-detect-dpi-elusion-strategies-for-quic-acsac-2020)
    - [DifFuzz: Differential Fuzzing for Side-Channel Analysis (ICSE 2019)](#diffuzz-differential-fuzzing-for-side-channel-analysis-icse-2019)
    - [Deep Differential Testing of JVM Implementations (ICSE 2019)](#deep-differential-testing-of-jvm-implementations-icse-2019)
    - [Hunting for bugs in code coverage tools via randomized differential testing (ICSE 2019)](#hunting-for-bugs-in-code-coverage-tools-via-randomized-differential-testing-icse-2019)
    - [Different is Good: Detecting the Use of Uninitialized Variables through Differential Replay (CCS 2019)](#different-is-good-detecting-the-use-of-uninitialized-variables-through-differential-replay-ccs-2019)
    - [Differential Program Analysis with Fuzzing and Symbolic Execution (ASE 2018)](#differential-program-analysis-with-fuzzing-and-symbolic-execution-ase-2018)
    - [NEZHA: Efficient Domain-Independent Differential Testing (S\&P 2017)](#nezha-efficient-domain-independent-differential-testing-sp-2017)
    - [Coverage-Directed Differential Testing of JVM Implementations (PLDI 2016)](#coverage-directed-differential-testing-of-jvm-implementations-pldi-2016)
- [Evaluate Fuzzing](#evaluate-fuzzing)
    - [FIXREVERTER: A Realistic Bug Injection Methodology for Benchmarking Fuzz Testing (USENIX Security2022)](#fixreverter-a-realistic-bug-injection-methodology-for-benchmarking-fuzz-testing-usenix-security2022)
    - [On the Reliability of Coverage-Based Fuzzer Benchmarking (ICSE 2022)](#on-the-reliability-of-coverage-based-fuzzer-benchmarking-icse-2022)
    - [Mutation Analysis: Answering the Fuzzing Challenge (2022)](#mutation-analysis-answering-the-fuzzing-challenge-2022)
    - [Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph (Access 2021)](#evaluating-code-coverage-for-kernel-fuzzers-via-function-call-graph-access-2021)
    - [FuzzBench: An Open Fuzzer Benchmarking Platform and Service (FSE 2021)](#fuzzbench-an-open-fuzzer-benchmarking-platform-and-service-fse-2021)
    - [An Empirical Study of OSS-Fuzz Bugs (MSR 2021)](#an-empirical-study-of-oss-fuzz-bugs-msr-2021)
    - [Industrial Oriented Evaluation of Fuzzing Techniques (ICST 2021)](#industrial-oriented-evaluation-of-fuzzing-techniques-icst-2021)
    - [UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers (USENIX Security2021)](#unifuzz-a-holistic-and-pragmatic-metrics-driven-platform-for-evaluating-fuzzers-usenix-security2021)
    - [My Fuzzer Beats Them All! Developing a Framework for Fair Evaluation and Comparison of Fuzzers (2021)](#my-fuzzer-beats-them-all-developing-a-framework-for-fair-evaluation-and-comparison-of-fuzzers-2021)
    - [A Quantitative Comparison of Covera (AST 2020)](#a-quantitative-comparison-of-covera-ast-2020)
    - [Fuzzing: On the Exponential Cost of Vulnerability Discovery (FSE 2020)](#fuzzing-on-the-exponential-cost-of-vulnerability-discovery-fse-2020)
    - [A Feature-Oriented Corpus for understanding, Evaluating and Improving Fuzz Testing (ASIACCS 2019)](#a-feature-oriented-corpus-for-understanding-evaluating-and-improving-fuzz-testing-asiaccs-2019)
    - [Be Sensitive and Collaborative: Analyzing Impact of Coverage Metrics in Greybox Fuzzing (RAID 2019)](#be-sensitive-and-collaborative-analyzing-impact-of-coverage-metrics-in-greybox-fuzzing-raid-2019)
    - [Study and Comparison of General Purpose Fuzzers](#study-and-comparison-of-general-purpose-fuzzers)
    - [Evaluating Fuzz Testing (CCS 2018)](#evaluating-fuzz-testing-ccs-2018)
- [Instrumentation](#instrumentation)
    - [InstruGuard: Find and Fix Instrumentation Errors for Coverage-based Greybox Fuzzing (ASE 2021)](#instruguard-find-and-fix-instrumentation-errors-for-coverage-based-greybox-fuzzing-ase-2021)
    - [RIFF: Reduced Instruction Footprint for Coverage-Guided Fuzzing (USENIX ATC 2021)](#riff-reduced-instruction-footprint-for-coverage-guided-fuzzing-usenix-atc-2021)
    - [Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection (TSE 2021)](#hashing-fuzzing-introducing-input-diversity-to-improve-crash-detection-tse-2021)
    - [RetroWrite: Statically Instrumenting COTS Binaries for Fuzzing and Sanitization (S\&P 2020)](#retrowrite-statically-instrumenting-cots-binaries-for-fuzzing-and-sanitization-sp-2020)
    - [INSTRCR: Lightweight instrumentation optimization based on coverage-guided fuzz testing (CCET 2019)](#instrcr-lightweight-instrumentation-optimization-based-on-coverage-guided-fuzz-testing-ccet-2019)
    - [Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing (S\&P 2019)](#full-speed-fuzzing-reducing-fuzzing-overhead-through-coverage-guided-tracing-sp-2019)
    - [INSTRIM Lightweight Instrumentation for Coverage-guided Fuzzing (NDSS 2018 workshop)](#instrim-lightweight-instrumentation-for-coverage-guided-fuzzing-ndss-2018-workshop)
    - [SyzGen: Automated Generation of Syscall Specification of Closed-Source macOS Drivers (CCS 2021)](#syzgen-automated-generation-of-syscall-specification-of-closed-source-macos-drivers-ccs-2021)
- [IoT or protocols fuzzing](#iot-or-protocols-fuzzing)
    - [PrIntFuzz: Fuzzing Linux Drivers via Automated Virtual Device Simulation (ISSTA 2022)](#printfuzz-fuzzing-linux-drivers-via-automated-virtual-device-simulation-issta-2022)
    - [SnapFuzz: High-Throughput Fuzzing of Network Applications (ISSTA 2022)](#snapfuzz-high-throughput-fuzzing-of-network-applications-issta-2022)
    - [Efficient Greybox Fuzzing of Applications in Linux-based IoT Devices via Enhanced User-mode Emulation (ISSTA 2022)](#efficient-greybox-fuzzing-of-applications-in-linux-based-iot-devices-via-enhanced-user-mode-emulation-issta-2022)
    - [FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks (FSE 2022)](#fuzzusb-hybrid-stateful-fuzzing-of-usb-gadget-stacks-fse-2022)
    - [BrakTooth: Causing Havoc on Bluetooth Link Manager via Directed Fuzzing (USENIX SEC 2022)](#braktooth-causing-havoc-on-bluetooth-link-manager-via-directed-fuzzing-usenix-sec-2022)
    - [Drifuzz: Harvesting Bugs in Device Drivers from Golden Seeds (USENIX SEC 2022)](#drifuzz-harvesting-bugs-in-device-drivers-from-golden-seeds-usenix-sec-2022)
    - [StateFuzz: System Call-Based State-Aware Linux Driver Fuzzing (USENIX SEC 2022)](#statefuzz-system-call-based-state-aware-linux-driver-fuzzing-usenix-sec-2022)
    - [SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots (2022)](#snpsfuzzer-a-fast-greybox-fuzzer-for-stateful-network-protocols-using-snapshots-2022)
    - [SnapFuzz: An Efficient Fuzzing Framework for Network Applications (2022)](#snapfuzz-an-efficient-fuzzing-framework-for-network-applications-2022)
    - [State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing (2021)](#state-selection-algorithms-and-their-impact-on-the-performance-of-stateful-network-protocol-fuzzing-2021)
    - [ICS3Fuzzer: A Framework for Discovering Protocol Implementation Bugs in ICS Supervisory Software by Fuzzing (ACSAC 2021)](#ics3fuzzer-a-framework-for-discovering-protocol-implementation-bugs-in-ics-supervisory-software-by-fuzzing-acsac-2021)
    - [Westworld: Fuzzing-Assisted Remote Dynamic Symbolic Execution of Smart Apps on IoT Cloud Platforms (ACSAC 2021)](#westworld-fuzzing-assisted-remote-dynamic-symbolic-execution-of-smart-apps-on-iot-cloud-platforms-acsac-2021)
    - [ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing (ISSTA 2021)](#profuzzbench---a-benchmark-for-stateful-protocol-fuzzing-issta-2021)
    - [TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing (USENIX ATC 2021)](#tcp-fuzz-detecting-memory-and-semantic-bugs-in-tcp-stacks-with-fuzzing-usenix-atc-2021)
    - [ICPFuzzer: proprietary communication protocol fuzzing by using machine learning and feedback strategies (Cybersecurity 2021)](#icpfuzzer-proprietary-communication-protocol-fuzzing-by-using-machine-learning-and-feedback-strategies-cybersecurity-2021)
    - [Fuzzing With Optimized Grammar-Aware Mutation Strategies (Access 2021)](#fuzzing-with-optimized-grammar-aware-mutation-strategies-access-2021)
    - [FIRM-COV: High-Coverage Greybox Fuzzing for IoT Firmware via Optimized Process Emulation (Access 2021)](#firm-cov-high-coverage-greybox-fuzzing-for-iot-firmware-via-optimized-process-emulation-access-2021)
    - [DIANE: Identifying Fuzzing Triggers in Apps to Generate Under-constrained Inputs for IoT Devices (S\&P 2020)](#diane-identifying-fuzzing-triggers-in-apps-to-generate-under-constrained-inputs-for-iot-devices-sp-2020)
    - [Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference (CCS 2021)](#snipuzz-black-box-fuzzing-of-iot-firmware-via-message-snippet-inference-ccs-2021)
    - [Learning-Based Fuzzing of IoT Message Brokers (ICST 2021)](#learning-based-fuzzing-of-iot-message-brokers-icst-2021)
    - [RiverFuzzRL - an open-source tool to experiment with reinforcement learning for fuzzing (ICST 2021)](#riverfuzzrl---an-open-source-tool-to-experiment-with-reinforcement-learning-for-fuzzing-icst-2021)
    - [Vulnerability Detection in SIoT Applications: A Fuzzing Method on their Binaries (IEEE Transactions on Network Science and Engineering 2020)](#vulnerability-detection-in-siot-applications-a-fuzzing-method-on-their-binaries-ieee-transactions-on-network-science-and-engineering-2020)
    - [Analysis of DTLS Implementations Using Protocol State Fuzzing (USENIX Security2020)](#analysis-of-dtls-implementations-using-protocol-state-fuzzing-usenix-security2020)
    - [Frankenstein: Advanced Wireless Fuzzing to Exploit New Bluetooth Escalation Targets (USENIX Security2020)](#frankenstein-advanced-wireless-fuzzing-to-exploit-new-bluetooth-escalation-targets-usenix-security2020)
    - [A deep convolution generative adversarial networks based fuzzing framework for industry control protocols](#a-deep-convolution-generative-adversarial-networks-based-fuzzing-framework-for-industry-control-protocols)
    - [ICS Protocol Fuzzing: Coverage Guided Packet Crack and Generation (DAC 2020)](#ics-protocol-fuzzing-coverage-guided-packet-crack-and-generation-dac-2020)
    - [AFLNET: A Greybox Fuzzer for Network Protocols (ICST 2020)](#aflnet-a-greybox-fuzzer-for-network-protocols-icst-2020)
    - [Finding Security Vulnerabilities in Network Protocol Implementations (Arxiv 2020)](#finding-security-vulnerabilities-in-network-protocol-implementations-arxiv-2020)
    - [Smart seed selection-based effective black box fuzzing for IIoT protocol (2020)](#smart-seed-selection-based-effective-black-box-fuzzing-for-iiot-protocol-2020)
    - [Fwâfuzz: A code coverageâguided fuzzing framework for network protocols on firmware (2020)](#fwâfuzz-a-code-coverageâguided-fuzzing-framework-for-network-protocols-on-firmware-2020)
    - [BaseSAFE: Baseband SAnitized Fuzzing through Emulation (WiSec 2020)](#basesafe-baseband-sanitized-fuzzing-through-emulation-wisec-2020)
    - [Poster: Fuzzing IoT Firmware via Multi-stage Message Generation (CCS 2019)](#poster-fuzzing-iot-firmware-via-multi-stage-message-generation-ccs-2019)
    - [SeqFuzzer: An Industrial Protocol Fuzzing Framework in Deep Learning Perspective (ICST 2019)](#seqfuzzer-an-industrial-protocol-fuzzing-framework-in-deep-learning-perspective-icst-2019)
    - [SPFuzz: A Hierarchical Scheduling Framework for Stateful Network Protocol Fuzzing (IEEE Access 2019)](#spfuzz-a-hierarchical-scheduling-framework-for-stateful-network-protocol-fuzzing-ieee-access-2019)
    - [HFuzz: Towards automatic fuzzing testing of NB-IoT core network protocols implementations (FGCS 2019)](#hfuzz-towards-automatic-fuzzing-testing-of-nb-iot-core-network-protocols-implementations-fgcs-2019)
    - [FIRM-AFL: High-Throughput Greybox Fuzzing of IoT Firmware via Augmented Process Emulation (USENIX Security2019)](#firm-afl-high-throughput-greybox-fuzzing-of-iot-firmware-via-augmented-process-emulation-usenix-security2019)
    - [Exploring Effective Fuzzing Strategies to Analyze Communication Protocols (FEAST 2019)](#exploring-effective-fuzzing-strategies-to-analyze-communication-protocols-feast-2019)
    - [Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols (AAAI 2019)](#leveraging-textual-specifications-for-grammar-based-fuzzing-of-network-protocols-aaai-2019)
    - [MTF-Storm: a high performance fuzzer for Modbus-TCP (ETFA 2018)](#mtf-storm-a-high-performance-fuzzer-for-modbus-tcp-etfa-2018)
    - [Advancing Protocol Fuzzing for Industrial Automation and Control Systems (ICISSP 2018)](#advancing-protocol-fuzzing-for-industrial-automation-and-control-systems-icissp-2018)
    - [IoTFuzzer: Discovering Memory Corruptions in IoT Through App-based Fuzzing (NDSS 2018)](#iotfuzzer-discovering-memory-corruptions-in-iot-through-app-based-fuzzing-ndss-2018)
    - [Bbuzz: A Bit-aware Fuzzing Framework for Network Protocol Systematic Reverse Engineering and Analysis (MCC 2017)](#bbuzz-a-bit-aware-fuzzing-framework-for-network-protocol-systematic-reverse-engineering-and-analysis-mcc-2017)
    - [Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine (2016)](#test-data-generation-for-stateful-network-protocol-fuzzing-using-a-rule-based-state-machine-2016)
    - [Protocol State Fuzzing of TLS Implementations (USENIX Security2015)](#protocol-state-fuzzing-of-tls-implementations-usenix-security2015)
    - [A Modbus-TCP Fuzzer for testing internetworked industrial systems (ETFA 2015)](#a-modbus-tcp-fuzzer-for-testing-internetworked-industrial-systems-etfa-2015)
    - [PULSAR: Stateful Black-Box Fuzzing of Proprietary Network Protocols (Springer, Cham, 2015)](#pulsar-stateful-black-box-fuzzing-of-proprietary-network-protocols-springer-cham-2015)
    - [SECFUZZ: Fuzz-testing Security Protocols (AST 2012)](#secfuzz-fuzz-testing-security-protocols-ast-2012)
    - [Extension of SPIKE for Encrypted Protocol Fuzzing (2011)](#extension-of-spike-for-encrypted-protocol-fuzzing-2011)
    - [AutoFuzz: Automated Network Protocol Fuzzing Framework (IJCSNS 2010)](#autofuzz-automated-network-protocol-fuzzing-framework-ijcsns-2010)
- [SMT Fuzzing](#smt-fuzzing)
    - [Alt-Ergo-Fuzz: A fuzzer for the Alt-Ergo SMT solver (JFLA 2022)](#alt-ergo-fuzz-a-fuzzer-for-the-alt-ergo-smt-solver-jfla-2022)
    - [BanditFuzz: Fuzzing SMT Solvers with Multi-agent Reinforcement Learning (FM 2021)](#banditfuzz-fuzzing-smt-solvers-with-multi-agent-reinforcement-learning-fm-2021)
    - [Skeletal Approximation Enumeration for SMT Solver Testing (FSE 2021)](#skeletal-approximation-enumeration-for-smt-solver-testing-fse-2021)
    - [Fuzzing SMT Solvers via Two-Dimensional Input Space Exploration (ISSTA 2021)](#fuzzing-smt-solvers-via-two-dimensional-input-space-exploration-issta-2021)
    - [Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing (FSE 2020)](#detecting-critical-bugs-in-smt-solvers-using-blackbox-mutational-fuzzing-fse-2020)
    - [On the Unusual Effectiveness of Type-aware Mutations for Testing SMT Solvers](#on-the-unusual-effectiveness-of-type-aware-mutations-for-testing-smt-solvers)
    - [BanditFuzz: Fuzzing SMT Solvers with Reinforcement Learning (2020)](#banditfuzz-fuzzing-smt-solvers-with-reinforcement-learning-2020)
    - [Validating SMT Solvers via Semantic Fusion (PLDI 2020)](#validating-smt-solvers-via-semantic-fusion-pldi-2020)
    - [Automatically Testing String Solvers (ICSE 2020)](#automatically-testing-string-solvers-icse-2020)
    - [StringFuzz: A fuzzer for string solvers (CAV 2018)](#stringfuzz-a-fuzzer-for-string-solvers-cav-2018)
- [Anti Fuzzing](#anti-fuzzing)
    - [Antifuzz: impeding fuzzing audits of binary executables (USENIX Security2019)](#antifuzz-impeding-fuzzing-audits-of-binary-executables-usenix-security2019)
    - [FUZZIFICATION: Anti-Fuzzing Technique (USENIX Security2019)](#fuzzification-anti-fuzzing-technique-usenix-security2019)
- [Kernel Fuzzing](#kernel-fuzzing)
    - [Demystifying the Dependency Challenge in Kernel Fuzzing (ICSE 2022)](#demystifying-the-dependency-challenge-in-kernel-fuzzing-icse-2022)
    - [Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and the Emulators (ICSE 2022)](#semantic-informed-driver-fuzzing-without-both-the-hardware-devices-and-the-emulators-icse-2022)
    - [SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel  (USENIX SEC'22)](#syzscope-revealing-high-risk-security-impacts-of-fuzzer-exposed-bugs-in-linux-kernel--usenix-sec22)
    - [HEALER: Relation Learning Guided Kernel Fuzzing (SOSP 2021)](#healer-relation-learning-guided-kernel-fuzzing-sosp-2021)
    - [NTFUZZ: Enabling Type-Aware Kernel Fuzzing on Windows with Static Binary Analysis(S\&P 2021)](#ntfuzz-enabling-type-aware-kernel-fuzzing-on-windows-with-static-binary-analysissp-2021)
    - [Finding Bugs in File Systems with an Extensible Fuzzing Framework (TOS 2020)](#finding-bugs-in-file-systems-with-an-extensible-fuzzing-framework-tos-2020)
    - [Finding race conditions in Kernels: from fuzzing to symbolic exection (2020)](#finding-race-conditions-in-kernels-from-fuzzing-to-symbolic-exection-2020)
    - [Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems (SOSP 2019)](#hydra-an-extensible-fuzzing-framework-for-finding-semantic-bugs-in-file-systems-sosp-2019)
    - [Fuzzing File Systems via Two-Dimensional Input Space Exploration (S\&P 2019)](#fuzzing-file-systems-via-two-dimensional-input-space-exploration-sp-2019)
    - [Unicorefuzz: On the Viability of Emulation for Kernelspace Fuzzing (USENIX WOOT'19)](#unicorefuzz-on-the-viability-of-emulation-for-kernelspace-fuzzing-usenix-woot19)
    - [Razzer: Finding Kernel Race Bugs through Fuzzing (S\&P 2019)](#razzer-finding-kernel-race-bugs-through-fuzzing-sp-2019)
    - [MoonShine: Optimizing OS Fuzzer Seed Selection with Trace Distillation (USENIX Security2018)](#moonshine-optimizing-os-fuzzer-seed-selection-with-trace-distillation-usenix-security2018)
    - [FUZE: Towards Facilitating Exploit Generation for Kernel Use-After-Free Vulnerabilities (USENIX Security2018)](#fuze-towards-facilitating-exploit-generation-for-kernel-use-after-free-vulnerabilities-usenix-security2018)
    - [kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels (Usenix Security2017)](#kafl-hardware-assisted-feedback-fuzzing-for-os-kernels-usenix-security2017)
    - [DIFUZE: Interface aware fuzzing for kernel drivers (CCS 2017)](#difuze-interface-aware-fuzzing-for-kernel-drivers-ccs-2017)
    - [IMF: Inferred Model-based Fuzzer (CCS 2017)](#imf-inferred-model-based-fuzzer-ccs-2017)
- [Hybrid Fuzzing:](#hybrid-fuzzing)
    - [TensileFuzz: Facilitating Seed Input Generation in Fuzzing via String Constraint Solving (ISSTA 2022)](#tensilefuzz-facilitating-seed-input-generation-in-fuzzing-via-string-constraint-solving-issta-2022)
    - [CONFETTI: Amplifying Concolic Guidance for Fuzzers (ICSE 2022)](#confetti-amplifying-concolic-guidance-for-fuzzers-icse-2022)
    - [FuSeBMC v. 4: Smart Seed Generation for Hybrid Fuzzing (2021)](#fusebmc-v-4-smart-seed-generation-for-hybrid-fuzzing-2021)
    - [A Tight Integration of Symbolic Execution and Fuzzing (short paper 2021)](#a-tight-integration-of-symbolic-execution-and-fuzzing-short-paper-2021)
    - [Symbolic Security Predicates: Hunt Program Weaknesses (ISPRAS Open 2021)](#symbolic-security-predicates-hunt-program-weaknesses-ispras-open-2021)
    - [Towards Symbolic Pointers Reasoning in Dynamic Symbolic Execution (IVMEM 2021)](#towards-symbolic-pointers-reasoning-in-dynamic-symbolic-execution-ivmem-2021)
    - [FUZZOLIC: Mixing fuzzing and concolic execution (Computers\&Security 2021)](#fuzzolic-mixing-fuzzing-and-concolic-execution-computerssecurity-2021)
    - [Concolic-Fuzzing of JavaScript Programs using GraalVM and Truffle (SKILL 2021)](#concolic-fuzzing-of-javascript-programs-using-graalvm-and-truffle-skill-2021)
    - [SHFuzz: A hybrid fuzzing method assisted by static analysis for binary programs (China Communications 2021)](#shfuzz-a-hybrid-fuzzing-method-assisted-by-static-analysis-for-binary-programs-china-communications-2021)
    - [A Priority Based Path Searching Method for Improving Hybrid Fuzzing (Computers \& Security 2021)](#a-priority-based-path-searching-method-for-improving-hybrid-fuzzing-computers--security-2021)
    - [Sydr: Cutting Edge Dynamic Symbolic Execution (ISPRAS Open 2020)](#sydr-cutting-edge-dynamic-symbolic-execution-ispras-open-2020)
    - [CSEFuzz: Fuzz Testing Based on Symbolic Execution (Access 2020)](#csefuzz-fuzz-testing-based-on-symbolic-execution-access-2020)
    - [Sequence directed hybrid fuzzing (SANER 2020)](#sequence-directed-hybrid-fuzzing-saner-2020)
    - [HFL: Hybrid Fuzzing on the Linux Kernel (NDSS 2020)](#hfl-hybrid-fuzzing-on-the-linux-kernel-ndss-2020)
    - [PANGOLIN: Incremental Hybrid Fuzzing with Polyhedral Path Abstraction (S\&P 2020)](#pangolin-incremental-hybrid-fuzzing-with-polyhedral-path-abstraction-sp-2020)
    - [SAVIOR: Towards Bug-Driven Hybrid Testing (S\&P 2020)](#savior-towards-bug-driven-hybrid-testing-sp-2020)
    - [Deferred Concretization in Symbolic Execution via Fuzzing (ISSTA 2019)](#deferred-concretization-in-symbolic-execution-via-fuzzing-issta-2019)
    - [Send Hardest Problems My Way: Probabilistic Path Prioritization for Hybrid Fuzzing (NDSS 2019)](#send-hardest-problems-my-way-probabilistic-path-prioritization-for-hybrid-fuzzing-ndss-2019)
    - [Intriguer: Field-Level Constraint Solving for Hybrid Fuzzing (CCS 2019)](#intriguer-field-level-constraint-solving-for-hybrid-fuzzing-ccs-2019)
    - [DeepFuzzer: Accelerated Deep Greybox Fuzzing (TDSC 2019)](#deepfuzzer-accelerated-deep-greybox-fuzzing-tdsc-2019)
    - [QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing (USENIX Security2018)](#qsym-a-practical-concolic-execution-engine-tailored-for-hybrid-fuzzing-usenix-security2018)
    - [Angora: Efficient Fuzzing by Principled Search (S\&P 2018)](#angora-efficient-fuzzing-by-principled-search-sp-2018)
    - [SAFL: increasing and accelerating testing coverage with symbolic execution and guided fuzzing (ICSE 2018)](#safl-increasing-and-accelerating-testing-coverage-with-symbolic-execution-and-guided-fuzzing-icse-2018)
    - [CAB-Fuzz: Practical Concolic Testing Techniques for COTS Operating Systems (Usenix Security2017)](#cab-fuzz-practical-concolic-testing-techniques-for-cots-operating-systems-usenix-security2017)
    - [Driller: Argumenting Fuzzing Through Selective Symbolic Execution (NDSS 2016)](#driller-argumenting-fuzzing-through-selective-symbolic-execution-ndss-2016)
    - [Hybrid Fuzz Testing - Discovering Software Bugs via Fuzzing and Symbolic Execution (2012)](#hybrid-fuzz-testing---discovering-software-bugs-via-fuzzing-and-symbolic-execution-2012)
    - [Hybrid concolic testing (2007)](#hybrid-concolic-testing-2007)
- [Mutation\\Coverage\\Path](#mutationcoveragepath)
    - [One Fuzzing Strategy to Rule Them All (ICSE 2022)](#one-fuzzing-strategy-to-rule-them-all-icse-2022)
    - [BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing (ICSE 2022)](#bedivfuzz-integrating-behavioral-diversity-into-generator-based-fuzzing-icse-2022)
    - [FuzzingDriver: the Missing Dictionary to Increase Code Coverage in Fuzzers (SANER 2022)](#fuzzingdriver-the-missing-dictionary-to-increase-code-coverage-in-fuzzers-saner-2022)
    - [EMS: History-Driven Mutation for Coverage-based Fuzzing (NDSS 2022)](#ems-history-driven-mutation-for-coverage-based-fuzzing-ndss-2022)
    - [OTA: An Operation-oriented Time Allocation Strategy for Greybox Fuzzing (SANER 2021)](#ota-an-operation-oriented-time-allocation-strategy-for-greybox-fuzzing-saner-2021)
    - [MaxAFL: Maximizing Code Coverage with a Gradient-Based Optimization Technique (Electronics 2020)](#maxafl-maximizing-code-coverage-with-a-gradient-based-optimization-technique-electronics-2020)
    - [PathAFL: Path-Coverage Assisted Fuzzing (ASIA CCS 2020)](#pathafl-path-coverage-assisted-fuzzing-asia-ccs-2020)
    - [Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and Scheduling (ASE 2020)](#zeror-speed-up-fuzzing-with-coverage-sensitive-tracing-and-scheduling-ase-2020)
    - [Not All Coverage Measurements Are Equal: Fuzzing by Coverage Accounting for Input Prioritization (NDSS 2020)](#not-all-coverage-measurements-are-equal-fuzzing-by-coverage-accounting-for-input-prioritization-ndss-2020)
    - [Matryoshka: fuzzing deeply nested branches (CCS 2019)](#matryoshka-fuzzing-deeply-nested-branches-ccs-2019)
    - [REDQUEEN: Fuzzing with Input-to-State Correspondence (NDSS2019)](#redqueen-fuzzing-with-input-to-state-correspondence-ndss2019)
    - [T-Fuzz: fuzzing by program transformation (S\&P 2018)](#t-fuzz-fuzzing-by-program-transformation-sp-2018)
    - [FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (ASE 2018)](#fairfuzz-a-targeted-mutation-strategy-for-increasing-greybox-fuzz-testing-coverage-ase-2018)
    - [VUzzer: Application-aware Evolutionary Fuzzing (NDSS 2017)](#vuzzer-application-aware-evolutionary-fuzzing-ndss-2017)
- [Grammars \\ Semantic \\ Context-aware Fuzzing](#grammars--semantic--context-aware-fuzzing)
    - [FRAMESHIFTER: Manipulating HTTP/2 Frame Sequences with Fuzzing (Usenix Security2020)](#frameshifter-manipulating-http2-frame-sequences-with-fuzzing-usenix-security2020)
    - [SGXFuzz: Efficiently Synthesizing Nested Structures for SGX Enclave Fuzzing (Usenix Security2022)](#sgxfuzz-efficiently-synthesizing-nested-structures-for-sgx-enclave-fuzzing-usenix-security2022)
    - [Unicorn: Detect Runtime Error in Time-Series Databases With Hybrid Input Synthesis (ISSTA 2022)](#unicorn-detect-runtime-error-in-time-series-databases-with-hybrid-input-synthesis-issta-2022)
    - [Cooper: Testing the Binding Code of Scripting Languages with Cooperative Mutation (NDSS 2022)](#cooper-testing-the-binding-code-of-scripting-languages-with-cooperative-mutation-ndss-2022)
    - [Fuzzing Class Specifications (ICSE 2022)](#fuzzing-class-specifications-icse-2022)
    - [Efficient ECU Analysis Technology through Structure-aware CAN Fuzzing (Access 2022)](#efficient-ecu-analysis-technology-through-structure-aware-can-fuzzing-access-2022)
    - [Semantic Image Fuzzing of AI Perception Systems](#semantic-image-fuzzing-of-ai-perception-systems)
    - [SoFi: Reflection-Augmented Fuzzing for JavaScript Engines (CCS 2021)](#sofi-reflection-augmented-fuzzing-for-javascript-engines-ccs-2021)
    - [V-SHUTTLE: Scalable and Semantics-Aware Hypervisor Fuzzing (CCS 2021)](#v-shuttle-scalable-and-semantics-aware-hypervisor-fuzzing-ccs-2021)
    - [Token-Level Fuzzing (WiSec 2021)](#token-level-fuzzing-wisec-2021)
    - [Extended grammar-based fuzzing algorithm for JavaScript Engines (2021)](#extended-grammar-based-fuzzing-algorithm-for-javascript-engines-2021)
    - [Gramatron: Effective Grammar-Aware Fuzzing (ISSTA 2021)](#gramatron-effective-grammar-aware-fuzzing-issta-2021)
    - [One Engine to Fuzz 'em All: Generic Language Processor Testing with Semantic Validation (S\&P 2021)](#one-engine-to-fuzz-em-all-generic-language-processor-testing-with-semantic-validation-sp-2021)
    - [Growing A Test Corpus with Bonsai Fuzzing (ICSE 2021)](#growing-a-test-corpus-with-bonsai-fuzzing-icse-2021)
    - [Favocado: Fuzzing the Binding Code of JavaScript Engines Using Semantically Correct Test Cases (NDSS 2021)](#favocado-fuzzing-the-binding-code-of-javascript-engines-using-semantically-correct-test-cases-ndss-2021)
    - [CMFuzz: context-aware adaptive mutation for fuzzers (Empirical Software Engineering 2021)](#cmfuzz-context-aware-adaptive-mutation-for-fuzzers-empirical-software-engineering-2021)
    - [Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing (ASE 2020)](#generating-highly-structured-input-data-by-combining-search-based-testing-and-grammar-based-fuzzing-ase-2020)
    - [Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer (Usenix Security2020)](#montage-a-neural-network-language-model-guided-javascript-engine-fuzzer-usenix-security2020)
    - [Fuzzing JavaScript Engines with Aspect-preserving Mutation (S\&P 2020)](#fuzzing-javascript-engines-with-aspect-preserving-mutation-sp-2020)
    - [Language-Agnostic Generation of Compilable Test Programs (ICST 2020)](#language-agnostic-generation-of-compilable-test-programs-icst-2020)
    - [Smart Greybox Fuzzing (TSE 2019)](#smart-greybox-fuzzing-tse-2019)
    - [Semantic Fuzzing with Zest (ISSTA 2019)](#semantic-fuzzing-with-zest-issta-2019)
    - [Field-aware Evolutionary Fuzzing Based on Input Specifications and Vulnerability Metrics (2019)](#field-aware-evolutionary-fuzzing-based-on-input-specifications-and-vulnerability-metrics-2019)
    - [Parser-Directed Fuzzing (PLDI 2019)](#parser-directed-fuzzing-pldi-2019)
    - [GRIMOIRE: Synthesizing Structure while Fuzzing (USENIX Security2019)](#grimoire-synthesizing-structure-while-fuzzing-usenix-security2019)
    - [Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice Assistant Applications (NDSS 2019)](#life-after-speech-recognition-fuzzing-semantic-misinterpretation-for-voice-assistant-applications-ndss-2019)
    - [SLF: Fuzzing without Valid Seed Inputs (ICSE 2019)](#slf-fuzzing-without-valid-seed-inputs-icse-2019)
    - [Superion: Grammar-Aware Greybox Fuzzing (ICSE 2019)](#superion-grammar-aware-greybox-fuzzing-icse-2019)
    - [ProFuzzer: On-the-fly Input Type Probing for Better Zero-day Vulnerability Discovery (S\&P 2019)](#profuzzer-on-the-fly-input-type-probing-for-better-zero-day-vulnerability-discovery-sp-2019)
    - [CodeAlchemist: Semantics-Aware Code Generation to Find Vulnerabilities in JavaScript Engines (NDSS 2019)](#codealchemist-semantics-aware-code-generation-to-find-vulnerabilities-in-javascript-engines-ndss-2019)
    - [NAUTILUS: Fishing for Deep Bugs with Grammars (NDSS 2019)](#nautilus-fishing-for-deep-bugs-with-grammars-ndss-2019)
    - [TIFF: Using Input Type Inference To Improve Fuzzing (ACSAC 2018)](#tiff-using-input-type-inference-to-improve-fuzzing-acsac-2018)
    - [Skyfire: Data-Driven Seed Generation for Fuzzing (S\&P 2017)](#skyfire-data-driven-seed-generation-for-fuzzing-sp-2017)
- [Exploit Generation](#exploit-generation)
    - [ETHPLOIT: From Fuzzing to Efficient Exploit Generation against Smart Contracts (SANER2020)](#ethploit-from-fuzzing-to-efficient-exploit-generation-against-smart-contracts-saner2020)
    - [Gollum: Modular and Greybox Exploit Generation for Heap Overflows in Interpreters (CCS 2019)](#gollum-modular-and-greybox-exploit-generation-for-heap-overflows-in-interpreters-ccs-2019)
    - [From proof-of-concept to exploitable (Cybersecurity 2019)](#from-proof-of-concept-to-exploitable-cybersecurity-2019)
    - [Revery: From Proof-of-Concept to Exploitable (CCS 2018)](#revery-from-proof-of-concept-to-exploitable-ccs-2018)
    - [SemFuzz: Semantics-based Automatic Generation of Proof-of-Concept Exploits (CCS 2017)](#semfuzz-semantics-based-automatic-generation-of-proof-of-concept-exploits-ccs-2017)
    - [ExploitMeter: Combining Fuzzing with Machine Learning for Automated Evaluation of Software Exploitability (PAC 2017)](#exploitmeter-combining-fuzzing-with-machine-learning-for-automated-evaluation-of-software-exploitability-pac-2017)
- [Parallel / Ensemble Fuzzing](#parallel--ensemble-fuzzing)
    - [UltraFuzz: Towards Resource-saving in Distributed Fuzzing (TSE 2022)](#ultrafuzz-towards-resource-saving-in-distributed-fuzzing-tse-2022)
    - [Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing (ASE 2021 NIER)](#towards-systematic-and-dynamic-task-allocation-for-collaborative-parallel-fuzzing-ase-2021-nier)
    - [CollabFuzz: A Framework for Collaborative Fuzzing (EuroSec 2021)](#collabfuzz-a-framework-for-collaborative-fuzzing-eurosec-2021)
    - [Improving Web Application Vulnerability Detection Leveraging Ensemble Fuzzing (ENASE 2021)](#improving-web-application-vulnerability-detection-leveraging-ensemble-fuzzing-enase-2021)
    - [Cupid: Automatic Fuzzer Selection for Collaborative Fuzzing (ACSAC 2020)](#cupid-automatic-fuzzer-selection-for-collaborative-fuzzing-acsac-2020)
    - [EnFuzz: Ensemble Fuzzing with Seed Synchronization among Diverse Fuzzers (USENIX Security2019)](#enfuzz-ensemble-fuzzing-with-seed-synchronization-among-diverse-fuzzers-usenix-security2019)
    - [PAFL: Extend FuzzingOptimizations of Single Mode to Industrial Parallel Mode (ESEC/FSE 2018)](#pafl-extend-fuzzingoptimizations-of-single-mode-to-industrial-parallel-mode-esecfse-2018)
- [Sanitizer-guided Fuzzing](#sanitizer-guided-fuzzing)
    - [ParmeSan: Sanitizer-guided Greybox Fuzzing (USENIX Security2020)](#parmesan-sanitizer-guided-greybox-fuzzing-usenix-security2020)
- [State / Sequence Guided Fuzzing](#state--sequence-guided-fuzzing)
    - [Stateful Greybox Fuzzing (USENIX Security 2022)](#stateful-greybox-fuzzing-usenix-security-2022)
    - [Linear-time Temporal Logic guided Greybox Fuzzing (ICSE 2022)](#linear-time-temporal-logic-guided-greybox-fuzzing-icse-2022)
    - [Rtkaller: State-aware Task Generation for RTOS Fuzzing (EMSOFT 2021)](#rtkaller-state-aware-task-generation-for-rtos-fuzzing-emsoft-2021)
    - [Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities (ICSE 2020)](#typestate-guided-fuzzer-for-discovering-use-after-free-vulnerabilities-icse-2020)
    - [IJON: Exploring Deep State Spaces via Fuzzing (S\&P 2020)](#ijon-exploring-deep-state-spaces-via-fuzzing-sp-2020)
    - [MemFuzz: Using Memory Accesses to Guide Fuzzing (ICST 2019)](#memfuzz-using-memory-accesses-to-guide-fuzzing-icst-2019)
- [Rust Fuzzing](#rust-fuzzing)
    - [Rust Library Fuzzing via API Dependency Graph Traversal (ASE 2021)](#rust-library-fuzzing-via-api-dependency-graph-traversal-ase-2021)
    - [RUSTY: A Fuzzing Tool for Rust (ACSAC 2020)](#rusty-a-fuzzing-tool-for-rust-acsac-2020)
- [Regression Fuzzing](#regression-fuzzing)
    - [R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing (ICSE 2022)](#r2z2-detecting-rendering-regressions-in-web-browsers-through-differential-fuzz-testing-icse-2022)
    - [FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems (ICSE 2022)](#fadatest-fast-and-adaptive-performance-regression-testing-of-dynamic-binary-translation-systems-icse-2022)
    - [Regression Greybox Fuzzing (CCS 2021)](#regression-greybox-fuzzing-ccs-2021)
    - [DeltaFuzz: Historical Version Information Guided Fuzz Testing (Journal of Computer Science and Technology 2021)](#deltafuzz-historical-version-information-guided-fuzz-testing-journal-of-computer-science-and-technology-2021)
    - [HyDiff: Hybrid Differential Software Analysis (ICSE 2020)](#hydiff-hybrid-differential-software-analysis-icse-2020)
- [Directed Fuzzing](#directed-fuzzing)
    - [WindRanger: A Directed Greybox Fuzzer driven by Deviation Basic Block (ICSE 2022)](#windranger-a-directed-greybox-fuzzer-driven-by-deviation-basic-block-icse-2022)
    - [BEACON: Directed Grey-Box Fuzzing with Provable Path Pruning (S\&P 2022)](#beacon-directed-grey-box-fuzzing-with-provable-path-pruning-sp-2022)
    - [Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA (ASE 2021 Industry)](#improving-configurability-of-unit-level-continuous-fuzzing-an-industrial-case-study-with-sap-hana-ase-2021-industry)
    - [KCFuzz: Directed Fuzzing Based on Keypoint Coverage (ICAIS 2021)](#kcfuzz-directed-fuzzing-based-on-keypoint-coverage-icais-2021)
    - [Constraint-guided Directed Greybox Fuzzing (USENIX Security2021)](#constraint-guided-directed-greybox-fuzzing-usenix-security2021)
    - [Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing (MDPI 2021)](#constructing-more-complete-control-flow-graphs-utilizing-directed-gray-box-fuzzing-mdpi-2021)
    - [Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities (RAID 2020)](#binary-level-directed-fuzzing-for-use-after-free-vulnerabilities-raid-2020)
    - [Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference (ICSE 2020)](#ankou-guiding-grey-box-fuzzing-towards-combinatorial-difference-icse-2020)
    - [RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation (2020)](#rdfuzz-accelerating-directed-fuzzing-with-intertwined-schedule-and-optimized-mutation-2020)
    - [TOFU: Target-Oriented FUzzer (Arxiv 2020)](#tofu-target-oriented-fuzzer-arxiv-2020)
    - [Sequence coverage directed greybox fuzzing (ICPC 2019)](#sequence-coverage-directed-greybox-fuzzing-icpc-2019)
    - [Hawkeye: Towards a Desired Directed Grey-box Fuzzer (CCS 2018)](#hawkeye-towards-a-desired-directed-grey-box-fuzzer-ccs-2018)
    - [RFUZZ: Coverage-Directed Fuzz Testing of RTL on FPGAs (ICCAD 2018)](#rfuzz-coverage-directed-fuzz-testing-of-rtl-on-fpgas-iccad-2018)
    - [Directed Greybox Fuzzing (CCS 2017)](#directed-greybox-fuzzing-ccs-2017)
- [Addressing Collision:](#addressing-collision)
    - [CollAFL: Path Sensitive Fuzzing (S\&P 2018)](#collafl-path-sensitive-fuzzing-sp-2018)
- [Performance Fuzzing](#performance-fuzzing)
    - [Understanding and Detecting Performance Bugs in Markdown Compilers (ASE 2021)](#understanding-and-detecting-performance-bugs-in-markdown-compilers-ase-2021)
    - [HotFuzz: Discovering Algorithmic Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing (NDSS 2020)](#hotfuzz-discovering-algorithmic-denial-of-service-vulnerabilities-through-guided-micro-fuzzing-ndss-2020)
    - [MemLock: Memory Usage Guided Fuzzing (ICSE2020)](#memlock-memory-usage-guided-fuzzing-icse2020)
    - [Singularity: Pattern Fuzzing for Worst Case Complexity (FSE 2018)](#singularity-pattern-fuzzing-for-worst-case-complexity-fse-2018)
    - [PerfFuzz: Automatically Generating Pathological Inputs (ISSTA 2018)](#perffuzz-automatically-generating-pathological-inputs-issta-2018)
    - [Badger: Complexity Analysis with Fuzzing and Symbolic Execution (ISSTA 2018)](#badger-complexity-analysis-with-fuzzing-and-symbolic-execution-issta-2018)
    - [SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities (CCS 2017)](#slowfuzz-automated-domain-independent-detection-of-algorithmic-complexity-vulnerabilities-ccs-2017)
- [Enhancing Memory Error:](#enhancing-memory-error)
    - [SANRAZOR: Reducing Redundant Sanitizer Checks in C/C++ Programs (OSDI 2021)](#sanrazor-reducing-redundant-sanitizer-checks-in-cc-programs-osdi-2021)
    - [Unleashing Fuzzing Through Comprehensive, Efficient, and Faithful Exploitable-Bug Exposing](#unleashing-fuzzing-through-comprehensive-efficient-and-faithful-exploitable-bug-exposing)
    - [HDR-Fuzz: Detecting Buffer Overruns using AddressSanitizer Instrumentation and Fuzzing (2021)](#hdr-fuzz-detecting-buffer-overruns-using-addresssanitizer-instrumentation-and-fuzzing-2021)
    - [Enhancing Memory Error Detection for Large-Scale Applications and Fuzz Testing (NDSS 2018)](#enhancing-memory-error-detection-for-large-scale-applications-and-fuzz-testing-ndss-2018)
    - [AddressSanitizer: A Fast Address Sanity Checker  (USENIX Security2012)](#addresssanitizer-a-fast-address-sanity-checker--usenix-security2012)
- [Schedule (Power \& Mutation)](#schedule-power--mutation)
    - [SLIME: Program-sensitive Energy Allocation for Fuzzing (ISSTA 2022)](#slime-program-sensitive-energy-allocation-for-fuzzing-issta-2022)
    - [MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing (NDSS 2022)](#mobfuzz-adaptive-multi-objective-optimization-in-gray-box-fuzzing-ndss-2022)
    - [Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis (S\&P 2022)](#effective-seed-scheduling-for-fuzzing-with-graph-centrality-analysis-sp-2022)
    - [Path Transitions Tell More: Optimizing Fuzzing Schedules via Runtime Program States (ICSE 2022)](#path-transitions-tell-more-optimizing-fuzzing-schedules-via-runtime-program-states-icse-2022)
    - [FEFuzzer: Hybrid Files Fuzzing Tool (2022)](#fefuzzer-hybrid-files-fuzzing-tool-2022)
    - [Seed Selection for Successful Fuzzing (ISSTA 2021)](#seed-selection-for-successful-fuzzing-issta-2021)
    - [MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer (Mathematics 2021)](#moofuzz-many-objective-optimization-seed-schedule-for-fuzzer-mathematics-2021)
    - [EcoFuzz: Adaptive Energy-Saving Greybox Fuzzing as a Variant of the Adversarial Multi-Armed Bandit (USENIX Security2020)](#ecofuzz-adaptive-energy-saving-greybox-fuzzing-as-a-variant-of-the-adversarial-multi-armed-bandit-usenix-security2020)
    - [MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing (2020)](#meuzz-smart-seed-scheduling-for-hybrid-fuzzing-2020)
    - [Greybox Fuzzing Based on Ant Colony Algorithm (AINA 2020)](#greybox-fuzzing-based-on-ant-colony-algorithm-aina-2020)
    - [Suzzer: A Vulnerability-Guided Fuzzer Based on Deep Learning (Inscrypt 2019)](#suzzer-a-vulnerability-guided-fuzzer-based-on-deep-learning-inscrypt-2019)
    - [MOPT: Optimize Mutation Scheduling for Fuzzers (USENIX Security2019)](#mopt-optimize-mutation-scheduling-for-fuzzers-usenix-security2019)
    - [Cerebro: Context-aware Adaptive Fuzzing for Effective Vulnerability Detection (FSE 2019)](#cerebro-context-aware-adaptive-fuzzing-for-effective-vulnerability-detection-fse-2019)
    - [Coverage-based Greybox Fuzzing as Markov Chain (CCS 2016)](#coverage-based-greybox-fuzzing-as-markov-chain-ccs-2016)
    - [Program-Adaptive Mutational Fuzzing (S\&P 2015)](#program-adaptive-mutational-fuzzing-sp-2015)
- [Learning-based Fuzzing](#learning-based-fuzzing)
    - [Evaluating and Improving Neural Program-Smoothing-based Fuzzing (ICSE 2022)](#evaluating-and-improving-neural-program-smoothing-based-fuzzing-icse-2022)
    - [Better Pay Attention Whilst Fuzzing (2022)](#better-pay-attention-whilst-fuzzing-2022)
    - [Automated Conformance Testing for JavaScript Engines via Deep Compiler Fuzzing (PLDI 2021)](#automated-conformance-testing-for-javascript-engines-via-deep-compiler-fuzzing-pldi-2021)
    - [Reinforcement Learning-based Hierarchical Seed Scheduling for Greybox Fuzzing (NDSS 2021)](#reinforcement-learning-based-hierarchical-seed-scheduling-for-greybox-fuzzing-ndss-2021)
    - [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles (arXiv 2021)](#neural-network-guided-evolutionary-fuzzing-for-finding-traffic-violations-of-autonomous-vehicles-arxiv-2021)
    - [OmniFuzz: A Flexible Framework for Expediting Bug Finding by Leveraging Past (Mis-)Behavior to Discover New Bugs (ACSAC 2020)](#omnifuzz-a-flexible-framework-for-expediting-bug-finding-by-leveraging-past-mis-behavior-to-discover-new-bugs-acsac-2020)
    - [Learning Input Tokens for Effective Fuzzing (ISSTA 2020)](#learning-input-tokens-for-effective-fuzzing-issta-2020)
    - [MTFuzz: Fuzzing with a Multi-task Neural Network (FSE 2020)](#mtfuzz-fuzzing-with-a-multi-task-neural-network-fse-2020)
    - [FuzzGuard: Filtering out Unreachable Inputs in Directed Grey-box Fuzzing through Deep Learning (USENIX Security2020)](#fuzzguard-filtering-out-unreachable-inputs-in-directed-grey-box-fuzzing-through-deep-learning-usenix-security2020)
    - [LearnAFL: Greybox Fuzzing With Knowledge Enhancement (Access 2019)](#learnafl-greybox-fuzzing-with-knowledge-enhancement-access-2019)
    - [NeuFuzz: Efficient Fuzzing With Deep Neural Network (Access 2019)](#neufuzz-efficient-fuzzing-with-deep-neural-network-access-2019)
    - [Learning-Guided Network Fuzzing for Testing Cyber-Physical System Defences (ASE 2019)](#learning-guided-network-fuzzing-for-testing-cyber-physical-system-defences-ase-2019)
    - [NEUZZ: Efficient Fuzzing with Neural Program Smoothing (S\&P 2019)](#neuzz-efficient-fuzzing-with-neural-program-smoothing-sp-2019)
    - [V-Fuzz: Vulnerability-Oriented Evolutionary Fuzzing (Arxiv 2019)](#v-fuzz-vulnerability-oriented-evolutionary-fuzzing-arxiv-2019)
    - [Compiler Fuzzing through Deep Learning (ISSTA 2018)](#compiler-fuzzing-through-deep-learning-issta-2018)
    - [Deep Reinforcement Fuzzing (SPW 2018)](#deep-reinforcement-fuzzing-spw-2018)
    - [FuzzerGym: A Competitive Framework for Fuzzing and Learning (arxiv 2018)](#fuzzergym-a-competitive-framework-for-fuzzing-and-learning-arxiv-2018)
    - [Learn\&Fuzz: Machine Learning for Input Fuzzing (ASE 2017)](#learnfuzz-machine-learning-for-input-fuzzing-ase-2017)
- [Fuzzing Machine Learning Model](#fuzzing-machine-learning-model)
    - [MDPFuzz: Testing Models Solving Markov Decision Processes (ISSTA 2022)](#mdpfuzz-testing-models-solving-markov-decision-processes-issta-2022)
    - [ocTer: Documentation-Guided Fuzzing for Testing Deep Learning API Functions (ISSTA 2022)](#octer-documentation-guided-fuzzing-for-testing-deep-learning-api-functions-issta-2022)
    - [Fuzzing Deep-Learning Libraries via Automated Relational API Inference (ESEC/FSE 2022)](#fuzzing-deep-learning-libraries-via-automated-relational-api-inference-esecfse-2022)
    - [Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source (ICSE 2022)](#free-lunch-for-testing-fuzzing-deep-learning-libraries-from-open-source-icse-2022)
    - [Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing (ICSE 2022)](#muffin-testing-deep-learning-libraries-via-neural-architecture-fuzzing-icse-2022)
    - [RapidFuzz: Accelerating fuzzing via Generative Adversarial Networks (Neurocomputing 2021)](#rapidfuzz-accelerating-fuzzing-via-generative-adversarial-networks-neurocomputing-2021)
    - [CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing (2021)](#cocofuzzing-testing-neural-code-models-with-coverage-guided-fuzzing-2021)
    - [Graph-based Fuzz Testing for Deep Learning Inference Engines (ICSE 2021)](#graph-based-fuzz-testing-for-deep-learning-inference-engines-icse-2021)
    - [Fuzz Testing based Data Augmentation to Improve Robustness of Deep Neural Networks (ICSE 2020)](#fuzz-testing-based-data-augmentation-to-improve-robustness-of-deep-neural-networks-icse-2020)
    - [Coverage Guided Differential Adversarial Testing of Deep Learning Systems (TNSE 2020)](#coverage-guided-differential-adversarial-testing-of-deep-learning-systems-tnse-2020)
    - [CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems (2019)](#cagfuzz-coverage-guided-adversarial-generative-fuzzing-testing-of-deep-learning-systems-2019)
    - [DeepHunter: A Coverage-Guided Fuzz Testing Framework for Deep Neural Networks (ISSTA 2019)](#deephunter-a-coverage-guided-fuzz-testing-framework-for-deep-neural-networks-issta-2019)
    - [TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (ICML 2019)](#tensorfuzz-debugging-neural-networks-with-coverage-guided-fuzzing-icml-2019)
    - [DLFuzz: Differential Fuzzing Testing of Deep Learning Systems (FSE 2018)](#dlfuzz-differential-fuzzing-testing-of-deep-learning-systems-fse-2018)
- [Data Flow Sensitive Fuzzing](#data-flow-sensitive-fuzzing)
    - [PATA: Fuzzing with Path Aware Taint Analysis (S\&P 2022)](#pata-fuzzing-with-path-aware-taint-analysis-sp-2022)
    - [datAFLow: Towards a Data-Flow-Guided Fuzzer (NDSS 2022)](#dataflow-towards-a-data-flow-guided-fuzzer-ndss-2022)
    - [ovAFLow: Detecting Memory Corruption Bugs with Fuzzing-based Taint Inference (Journal of Computer Science and Technology 2021)](#ovaflow-detecting-memory-corruption-bugs-with-fuzzing-based-taint-inference-journal-of-computer-science-and-technology-2021)
    - [DIAR: Removing Uninteresting Bytes from Seeds in Software Fuzzing (2021)](#diar-removing-uninteresting-bytes-from-seeds-in-software-fuzzing-2021)
    - [GREYONE: Data Flow Sensitive Fuzzing (USENIX Security2020)](#greyone-data-flow-sensitive-fuzzing-usenix-security2020)
- [Binary Fuzzing](#binary-fuzzing)
    - [spotFuzzer: Static Instrument and Fuzzing Windows COTs (2022)](#spotfuzzer-static-instrument-and-fuzzing-windows-cots-2022)
    - [Emmutaler: Fuzzing the iOS Boot Loader (Thesis 2021)](#emmutaler-fuzzing-the-ios-boot-loader-thesis-2021)
    - [Same Coverage, Less Bloat: Accelerating Binary-only Fuzzing with Coverage-preserving Coverage-guided Tracing (CCS 2021)](#same-coverage-less-bloat-accelerating-binary-only-fuzzing-with-coverage-preserving-coverage-guided-tracing-ccs-2021)
    - [Scalable Fuzzing of Program Binaries with E9AFL (ASE 2021)](#scalable-fuzzing-of-program-binaries-with-e9afl-ase-2021)
    - [STOCHFUZZ: Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting (S\&P 2021)](#stochfuzz-sound-and-cost-effective-fuzzing-of-stripped-binaries-by-incremental-and-stochastic-rewriting-sp-2021)
    - [Coverage-guided binary fuzzing with rev.ng and llvm libfuzzer](#coverage-guided-binary-fuzzing-with-revng-and-llvm-libfuzzer)
    - [Breaking Through Binaries: Compiler-quality Instrumentation for Better Binary-only Fuzzing  (USENIX Security2021)](#breaking-through-binaries-compiler-quality-instrumentation-for-better-binary-only-fuzzing--usenix-security2021)
    - [WEIZZ: Automatic Grey-box Fuzzing for Structured Binary Formats](#weizz-automatic-grey-box-fuzzing-for-structured-binary-formats)
    - [Ptrix: Efficient Hardware-Assisted Fuzzing for COTS Binary (ASIACCS 2019)](#ptrix-efficient-hardware-assisted-fuzzing-for-cots-binary-asiaccs-2019)
    - [Steelix: Program-State Based Binary Fuzzing (FSE 2017)](#steelix-program-state-based-binary-fuzzing-fse-2017)
    - [In-memory fuzzing for binary code similarity analysis (ASE 2017)](#in-memory-fuzzing-for-binary-code-similarity-analysis-ase-2017)
- [Smart Contracts](#smart-contracts)
    - [WASAI: Uncovering Vulnerabilities in Wasm Smart Contracts (ISSTA 2022)](#wasai-uncovering-vulnerabilities-in-wasm-smart-contracts-issta-2022)
    - [Smart Contract Fuzzing for Enterprises: The Language Agnostic Way (COMSNETS 2022)](#smart-contract-fuzzing-for-enterprises-the-language-agnostic-way-comsnets-2022)
    - [looking for lacunae in bitcoin cores fuzzing efforts (2022)](#looking-for-lacunae-in-bitcoin-cores-fuzzing-efforts-2022)
    - [Machine Learning Guided Cross-Contract Fuzzing (2021)](#machine-learning-guided-cross-contract-fuzzing-2021)
    - [SMARTIAN : Enhancing Smart Contract Fuzzing with Static and Dynamic Data-Flow Analyses (ASE 2021)](#smartian--enhancing-smart-contract-fuzzing-with-static-and-dynamic-data-flow-analyses-ase-2021)
    - [HFContractFuzzer: Fuzzing Hyperledger Fabric Smart Contracts for Vulnerability Detection (EASE 2021)](#hfcontractfuzzer-fuzzing-hyperledger-fabric-smart-contracts-for-vulnerability-detection-ease-2021)
    - [sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts (ICSE 2020)](#sfuzz-an-efficient-adaptive-fuzzer-for-solidity-smart-contracts-icse-2020)
    - [Targeted Greybox Fuzzing with Static Lookahead Analysis (ICSE 2020)](#targeted-greybox-fuzzing-with-static-lookahead-analysis-icse-2020)
    - [Learning to Fuzz from Symbolic Execution with Application to Smart Contracts (CCS 2019)](#learning-to-fuzz-from-symbolic-execution-with-application-to-smart-contracts-ccs-2019)
    - [ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection (ASE 2018)](#contractfuzzer-fuzzing-smart-contracts-for-vulnerability-detection-ase-2018)
- [Constraint Solving](#constraint-solving)
    - [JIGSAW: Efficient and Scalable Path Constraints Fuzzing (S\&P 2022)](#jigsaw-efficient-and-scalable-path-constraints-fuzzing-sp-2022)
    - [Fuzzing Symbolic Expressions (ICSE 2021)](#fuzzing-symbolic-expressions-icse-2021)
    - [Just Fuzz It: Solving Floating-Point Constraints Using Coverage-guided Fuzzing (FSE 2019)](#just-fuzz-it-solving-floating-point-constraints-using-coverage-guided-fuzzing-fse-2019)
- [Side-Channel Detection](#side-channel-detection)
    - [QFuzz: Quantitative Fuzzing for Side Channels (ISSTA 2021)](#qfuzz-quantitative-fuzzing-for-side-channels-issta-2021)
    - [JVM Fuzzing for JIT-Induced Side-Channel Detection (ICSE 2020)](#jvm-fuzzing-for-jit-induced-side-channel-detection-icse-2020)
    - [ct-fuzz: Fuzzing for Timing Leaks (ICST 2020)](#ct-fuzz-fuzzing-for-timing-leaks-icst-2020)
- [Concurrency Fuzzing](#concurrency-fuzzing)
    - [Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection (NDSS 2022)](#context-sensitive-and-directional-concurrency-fuzzing-for-data-race-detection-ndss-2022)
    - [Fuzzing with automatically controlled interleavings to detect concurrency bugs (The Journal of Systems \& Software 2022)](#fuzzing-with-automatically-controlled-interleavings-to-detect-concurrency-bugs-the-journal-of-systems--software-2022)
    - [Controlled Concurrency Testing via Periodical Scheduling (ICSE 2022)](#controlled-concurrency-testing-via-periodical-scheduling-icse-2022)
    - [Fuzzing Channel-Based Concurrency Runtimes using Types and Effects Slides (OOPSLA 2021)](#fuzzing-channel-based-concurrency-runtimes-using-types-and-effects-slides-oopsla-2021)
    - [MUZZ: Thread-aware Grey-box Fuzzing for Effective Bug Hunting in Multithreaded Programs (USENIX Security2020)](#muzz-thread-aware-grey-box-fuzzing-for-effective-bug-hunting-in-multithreaded-programs-usenix-security2020)
    - [KRace: Data Race Fuzzing for Kernel File Systems (S\&P 2020)](#krace-data-race-fuzzing-for-kernel-file-systems-sp-2020)
    - [ConFuzz: A Concurrency Fuzzer (2019)](#confuzz-a-concurrency-fuzzer-2019)
    - [Fuzz Testing of Multithreaded Applications Based on Waiting (MICSECS 2019)](#fuzz-testing-of-multithreaded-applications-based-on-waiting-micsecs-2019)
    - [A Heuristic Framework to Detect Concurrency Vulnerabilities (ACSAC 2018)](#a-heuristic-framework-to-detect-concurrency-vulnerabilities-acsac-2018)
- [API Testing/Fuzzing](#api-testingfuzzing)
    - [Minerva: Browser API Fuzzing with Dynamic Mod-Ref Analysis (FSE 2022)](#minerva-browser-api-fuzzing-with-dynamic-mod-ref-analysis-fse-2022)
    - [Combinatorial Testing of RESTful APIs (ICSE 2022)](#combinatorial-testing-of-restful-apis-icse-2022)
    - [GraphFuzz: Library API Fuzzing with Lifetime-aware Dataflow Graphs (ICSE 2022)](#graphfuzz-library-api-fuzzing-with-lifetime-aware-dataflow-graphs-icse-2022)
    - [MOREST: Model-based RESTful API Testing with Execution Feedback (ICSE 2022)](#morest-model-based-restful-api-testing-with-execution-feedback-icse-2022)
    - [Automated Testing of Software that Uses Machine Learning APIs (ICSE 2022)](#automated-testing-of-software-that-uses-machine-learning-apis-icse-2022)
    - [Nessie: Automatically Testing JavaScript APIs with Asynchronous Callbacks (ICSE 2022)](#nessie-automatically-testing-javascript-apis-with-asynchronous-callbacks-icse-2022)
    - [REST API Fuzzing by Coverage Level Guided Blackbox Testing (2021)](#rest-api-fuzzing-by-coverage-level-guided-blackbox-testing-2021)
    - [FuzzGen: Automatic Fuzzer Generation (USENIX Security2020)](#fuzzgen-automatic-fuzzer-generation-usenix-security2020)
    - [Intelligent REST API Data Fuzzing (FSE 2020)](#intelligent-rest-api-data-fuzzing-fse-2020)
    - [FUDGE: Fuzz Driver Generation at Scale (FSE 2019)](#fudge-fuzz-driver-generation-at-scale-fse-2019)
    - [REST-ler: Stateful REST API Fuzzing (ICSE 2019)](#rest-ler-stateful-rest-api-fuzzing-icse-2019)
    - [Fuzzing JavaScript Environment APIs with Interdependent Function Calls (IFM 2019)](#fuzzing-javascript-environment-apis-with-interdependent-function-calls-ifm-2019)
    - [Systematic Fuzzing and Testing of TLS Libraries (CCS 2016)](#systematic-fuzzing-and-testing-of-tls-libraries-ccs-2016)
- [Hardware Fuzzing](#hardware-fuzzing)
    - [CPU Fuzzing for Discovering Hardware-caused Information Leakage (2022)](#cpu-fuzzing-for-discovering-hardware-caused-information-leakage-2022)
    - [Fuzzing Hardware Like Software (USENIX SEC 2022)](#fuzzing-hardware-like-software-usenix-sec-2022)
    - [PeriScope: An Effective Probing and Fuzzing Framework for the Hardware-OS Boundary (NDSS2019)](#periscope-an-effective-probing-and-fuzzing-framework-for-the-hardware-os-boundary-ndss2019)
- [Other Interesting Fuzzing](#other-interesting-fuzzing)
    - [RoboFuzz: Fuzzing Robotic Systems over Robot Operating System (ROS) for Finding Correctness Bugs (FSE 2022)](#robofuzz-fuzzing-robotic-systems-over-robot-operating-system-ros-for-finding-correctness-bugs-fse-2022)
    - [Almost Correct Invariants: Synthesizing Inductive Invariants by Fuzzing Proofs (ISSTA 2022)](#almost-correct-invariants-synthesizing-inductive-invariants-by-fuzzing-proofs-issta-2022)
    - [?AFL: Non-intrusive Feedback-driven Fuzzing for Microcontroller Firmware (ICSE 2022)](#afl-non-intrusive-feedback-driven-fuzzing-for-microcontroller-firmware-icse-2022)
    - [CorbFuzz: Checking Browser Security Policies with Fuzzing (ASE 2021)](#corbfuzz-checking-browser-security-policies-with-fuzzing-ase-2021)
    - [Fully Automated Functional Fuzzing of Android Apps for Detecting Non-crashing Logic Bugs (OOPSLA 2021)](#fully-automated-functional-fuzzing-of-android-apps-for-detecting-non-crashing-logic-bugs-oopsla-2021)
    - [webFuzz: Grey-Box Fuzzing for Web Applications (ESORICS 2021)](#webfuzz-grey-box-fuzzing-for-web-applications-esorics-2021)
    - [Vulnerability-Oriented Fuzz Testing for Connected Autonomous Vehicle Systems ( IEEE Transactions on Reliability 2021)](#vulnerability-oriented-fuzz-testing-for-connected-autonomous-vehicle-systems--ieee-transactions-on-reliability-2021)
    - [Hardware Support to Improve Fuzzing Performance and Precision (CCS 2021)](#hardware-support-to-improve-fuzzing-performance-and-precision-ccs-2021)
    - [Fuzzing: Cyberphysical System Testing for Security and Dependability (Computer 2021)](#fuzzing-cyberphysical-system-testing-for-security-and-dependability-computer-2021)
    - [ESRFuzzer: an enhanced fuzzing framework for physical SOHO router devices to discover multi-Type vulnerabilities (Cybersecurity 2021)](#esrfuzzer-an-enhanced-fuzzing-framework-for-physical-soho-router-devices-to-discover-multi-type-vulnerabilities-cybersecurity-2021)
    - [Estimating Residual Risk in Greybox Fuzzing (FSE 2021)](#estimating-residual-risk-in-greybox-fuzzing-fse-2021)
    - [HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications (FSE 2021)](#heterofuzz-fuzz-testing-to-detect-platform-dependent-divergence-for-heterogeneous-applications-fse-2021)
    - [DirectFuzz: Automated Test Generation for RTL Designs using Directed Graybox Fuzzing (DAC 2021)](#directfuzz-automated-test-generation-for-rtl-designs-using-directed-graybox-fuzzing-dac-2021)
    - [PMFuzz: Test Case Generation for Persistent Memory Programs (ASPLOS 2021)](#pmfuzz-test-case-generation-for-persistent-memory-programs-asplos-2021)
    - [PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles (NDSS 2021)](#pgfuzz-policy-guided-fuzzing-for-robotic-vehicles-ndss-2021)
    - [IntelliGen: Automatic Driver Synthesis for Fuzz Testing (ICSE 2021 SEIP)](#intelligen-automatic-driver-synthesis-for-fuzz-testing-icse-2021-seip)
    - [WINNIE: Fuzzing Windows Applications with Harness Synthesis and Fast Cloning (NDSS 2021)](#winnie-fuzzing-windows-applications-with-harness-synthesis-and-fast-cloning-ndss-2021)
    - [Industry Practice of Coverage-Guided Enterprise-Level DBMS Fuzzing  (ICSE 2021)](#industry-practice-of-coverage-guided-enterprise-level-dbms-fuzzing--icse-2021)
    - [Refined Grey-Box Fuzzing with Sivo (arXiv 2021)](#refined-grey-box-fuzzing-with-sivo-arxiv-2021)
    - [Fuzzing Technique in Web Applications and Beyond (MCTE 2020)](#fuzzing-technique-in-web-applications-and-beyond-mcte-2020)
    - [Nyx: Greybox Hypervisor Fuzzing using Fast Snapshots and Affine Types (USENIX Security2021)](#nyx-greybox-hypervisor-fuzzing-using-fast-snapshots-and-affine-types-usenix-security2021)
    - [The Use of Likely Invariants as Feedback for Fuzzers (USENIX Security2021)](#the-use-of-likely-invariants-as-feedback-for-fuzzers-usenix-security2021)
    - [FREEDOM: Engineering a State-of-the-Art DOM Fuzzer (CCS 2020)](#freedom-engineering-a-state-of-the-art-dom-fuzzer-ccs-2020)
    - [SQUIRREL: Testing Database Management Systems with Language Validity and Coverage Feedback (CCS 2020)](#squirrel-testing-database-management-systems-with-language-validity-and-coverage-feedback-ccs-2020)
    - [BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction (ASE 2020)](#bigfuzz-efficient-fuzz-testing-for-data-analytics-using-framework-abstraction-ase-2020)
    - [MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools (ASE 2020)](#mofuzz-a-fuzzer-suite-for-testing-model-driven-software-engineering-tools-ase-2020)
    - [AFL++: Combining Incremental Steps of Fuzzing Research (USENIX Woot2020)](#afl-combining-incremental-steps-of-fuzzing-research-usenix-woot2020)
    - [Active Fuzzing for Testing and Securing Cyber-Physical Systems (ISSTA 2020)](#active-fuzzing-for-testing-and-securing-cyber-physical-systems-issta-2020)
    - [CrFuzz: Fuzzing Multi-purpose Programs through Input Validation (FSE 2020)](#crfuzz-fuzzing-multi-purpose-programs-through-input-validation-fse-2020)
    - [SpecFuzz: Bringing Spectre-type vulnerabilities to the surface (USENIX Security2020)](#specfuzz-bringing-spectre-type-vulnerabilities-to-the-surface-usenix-security2020)
    - [USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation (USENIX Security2020)](#usbfuzz-a-framework-for-fuzzing-usb-drivers-by-device-emulation-usenix-security2020)
    - [Boosting Fuzzer Efficiency: An Information Theoretic Perspective (FSE 2020)](#boosting-fuzzer-efficiency-an-information-theoretic-perspective-fse-2020)
    - [Fuzzing Error Handling Code using Context-Sensitive Software Fault Injection (USENIX Security2020)](#fuzzing-error-handling-code-using-context-sensitive-software-fault-injection-usenix-security2020)
    - [FANS: Fuzzing Android Native System Services via Automated Interface Analysis (USENIX Security2020)](#fans-fuzzing-android-native-system-services-via-automated-interface-analysis-usenix-security2020)
    - [Fuzzing IPC with Knowledge Inference (SRDS 2019)](#fuzzing-ipc-with-knowledge-inference-srds-2019)
    - [HYPER-CUBE: High-Dimensional Hypervisor Fuzzing (NDSS 2020)](#hyper-cube-high-dimensional-hypervisor-fuzzing-ndss-2020)
    - [Reproducible Crashes: Fuzzing Pharo by Mutating the Test Methods (IWST20 2020)](#reproducible-crashes-fuzzing-pharo-by-mutating-the-test-methods-iwst20-2020)
    - [Opening Pandoraâs Box through ATFuzzer: Dynamic Analysis of AT Interface for Android Smartphones (ACSAC 2019)](#opening-pandoraâs-box-through-atfuzzer-dynamic-analysis-of-at-interface-for-android-smartphones-acsac-2019)
    - [FuzzFactory: Domain-Specific Fuzzing with Waypoints (OOPSLA 2019)](#fuzzfactory-domain-specific-fuzzing-with-waypoints-oopsla-2019)
    - [Compiler Fuzzing: How Much Does It Matter (OOPSLA2019)](#compiler-fuzzing-how-much-does-it-matter-oopsla2019)
    - [RVFuzzer: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Random Testing (USENIX Security2019)](#rvfuzzer-finding-input-validation-bugs-in-robotic-vehicles-through-control-guided-random-testing-usenix-security2019)
    - [Engineering a Better Fuzzer with Synergically Integrated Optimizations (ISSRE 2019)](#engineering-a-better-fuzzer-with-synergically-integrated-optimizations-issre-2019)
    - [Fuzzing Error Handling Code in Device Drivers Based on Software Fault Injection (ISSRE 2019)](#fuzzing-error-handling-code-in-device-drivers-based-on-software-fault-injection-issre-2019)
    - [What You Corrupt Is Not What You Crash: Challenges in Fuzzing Embedded Devices (NDSS 2018)](#what-you-corrupt-is-not-what-you-crash-challenges-in-fuzzing-embedded-devices-ndss-2018)
    - [FOT: A Versatile, Configurable, Extensible Fuzzing Framework (FSE 2018)](#fot-a-versatile-configurable-extensible-fuzzing-framework-fse-2018)
    - [Designing New Operating Primitives to Improve Fuzzing Performance (CCS 2017)](#designing-new-operating-primitives-to-improve-fuzzing-performance-ccs-2017)
    - [Chizpurfle: A Gray-Box Android Fuzzer for Vendor Service Customizations (ISSRE 2017)](#chizpurfle-a-gray-box-android-fuzzer-for-vendor-service-customizations-issre-2017)
    - [Fuzzware: Using Precise MMIO Modeling for Effective Firmware Fuzzing (USENIX Security2022)](#fuzzware-using-precise-mmio-modeling-for-effective-firmware-fuzzing-usenix-security2022)
        - [Page Traffic Analysis](#page-traffic-analysis)


# Survey/Review

### Fuzzing: Challenges and Reflections

* <img src="image/pdf_24px.png">[Paper](https://www.comp.nus.edu.sg/~abhik/pdf/IEEE-SW-Fuzzing.pdf)

**Abstract:** Fuzzing is a method to discover software bugs and vulnerabilities by automatic test input generation which has found tremendous recent interest in both academia and industry. Fuzzing comes in the form of several techniques. On one hand, we have symbolic execution, which enables a particularly effective approach to fuzzing by systematically enumerating the paths of a program. On the other hand, we have random input generation, which generates large amounts of inputs per second with none or minimal program analysis overhead. In this article, we summarize the open challenges and opportunities for fuzzing and symbolic execution as they emerged in discussions among researchers and practitioners in a Shonan Meeting, and were validated in a subsequent survey. We take a forward-looking view of the software vulnerability discovery technologies and provide concrete directions for future research.


### SoK: The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing

* <img src="image/wechat_24px.png">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/zpibe5)
* 
* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv20_SoK.pdf)

**Abstract:** Greybox fuzzing has been the most scalable and practical approach to software testing. Most greybox fuzzing tools are coverage guided as code coverage is strongly correlated with bug coverage. However, since most covered codes may not containbugs, blindly extending code coverage is less efficient, especially for corner cases. Unlike coverage-based fuzzers who extend the code coverage in an undirected manner, a directed fuzzer spends most of its time budget on reaching specific target locations (e.g.,the bug-prone zone) without wasting resources stressing unrelated parts. Thus, directed greybox fuzzing is particularly suitable for scenarios such as patch testing, bug reproduction, and special bug hunting. In this paper, we conduct the first in-depth study of directed greybox fuzzing. We investigate 28 state-of-the-artfuzzers (82% are published after 2019) closely related to DGF, which have various directed types and optimization techniques. Based on the feature of DGF, we extract 15 metrics to conducta thorough assessment of the collected tools and systemize the knowledge of this field. Finally, we summarize the challenges and provide perspectives of this field, aiming to facilitate and boost future research on this topic.


### Fuzzing: Hack, Art, and Science (CACM 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/CACM20_Fuzzing.pdf)

**Abstract:** Fuzzing, or fuzz testing, is the process of finding security vulnerabilities in input-parsing code by repeatedly testing the parser with modified, or fuzzed, inputs.35 Since the early 2000s, fuzzing has become a mainstream practice in assessing software security. Thousands of security vulnerabilities have been found while fuzzing all kinds of software applications for processing documents, images, sounds, videos, network packets, Web pages, among others. These applications must deal with untrusted inputs encoded in complex data formats. For example, the Microsoft Windows operating system supports over 360 file formats and includes millions of lines of code just to handle all of these.


### Survey of Directed Fuzzy Technology

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSESS19_Survey.pdf)

**Abstract:** The fuzzy testing technology can effectively detect vulnerabilities. Based on Directed Symbolic Execution (DSE) fuzzing and Directed Grey Box Fuzzing (DGF), which can reach the specified target locations and scan the vulnerability quickly and efficiently. This paper introduces the theoretical knowledge of directed fuzzy testing technology, and several state-of-the-art fuzzy testing tools to elaborate the principle, advantages, disadvantages and the prospect of directed fuzzy technology.


### A Review of Machine Learning Applications in Fuzzing

* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv19_Machine.pdf)

**Abstract:** Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.


### A systematic review of fuzzing based on machine learning techniques

* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv19_Machine2.pdf)

**Abstract:** Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzzing techniques have many challenges, such as how to mutate input seed files, how to increase code coverage, and how to effectively bypass verification. Machine learning technology has been introduced as a new method into fuzzing test to alleviate these challenges. This paper reviews the research progress of using machine learning technology for fuzzing test in recent years, analyzes how machine learning improve the fuzz process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies six different stages in which machine learning have been used. Then this paper systematically study the machine learning based fuzzing models from selection of machine learning algorithm, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics. The results of the evaluation prove that machine learning technology has an acceptable capability of categorize predictive for fuzzing. Finally, the comparison on capability of discovering vulnerabilities between traditional fuzzing tools and machine learning based fuzzing tools is analyzed. The results depict that the introduction of machine learning technology can improve the performance of fuzzing. However, there are still some limitations, such as unbalanced training samples and difficult to extract the characteristics related to vulnerabilities.


### The Art, Science, and Engineering of Fuzzing: A Survey

* <img src="image/csdn_24px.png">[Reading Note1](https://blog.csdn.net/qq_32505207/article/details/104303840)

* <img src="image/csdn_24px.png">[Reading Note2](https://blog.csdn.net/qq_40398985/article/details/103585735)

* <img src="image/pdf_24px.png">[Paper](./Paper/TSE19_Survey.pdf)

**Abstract:** Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.


### Fuzzing: Art, Science, and Engineering

* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv18_Fuzzing.pdf)

**Abstract:** Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.


### Fuzzing: a survey

* <img src="image/zhihu.png">[Reading Note1](https://zhuanlan.zhihu.com/p/99099607)

* <img src="image/csdn_24px.png">[Reading Note2](https://blog.csdn.net/u014578266/article/details/89042508)

* <img src="image/pdf_24px.png">[Paper](./Paper/Cybersecurity18_Fuzzing.pdf)

**Abstract:** Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions.


### Fuzzing: State of the art

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/fuzz%E7%BB%BC%E8%BF%B0%E2%80%94Fuzzing%EF%BC%9AState%20of%20the%20Art.png)

* <img src="image/pdf_24px.png">[Paper](./Paper/TRel18_Fuzzing.pdf)

**Abstract:** As one of the most popular software testing techniques, fuzzing can find a variety of weaknesses in a program, such as software bugs and vulnerabilities, by generating numerous test inputs. Due to its effectiveness, fuzzing is regarded as a valuable bug hunting method. In this paper, we present an overview of fuzzing that concentrates on its general process, as well as classifications, followed by detailed discussion of the key obstacles and some state-of-the-art technologies which aim to overcome or mitigate these obstacles. We further investigate and classify several widely used fuzzing tools. Our primary goal is to equip the stakeholder with a better understanding of fuzzing and the potential solutions for improving fuzzing methods in the spectrum of software testing and security. To inspire future research, we also predict some future directions with regard to fuzzing.


### Fuzzing: A Survey for Roadmap

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/abs/10.1145/3512345)

**Abstract:** Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It generates a large number of test cases and monitors the executions for defects. Fuzzing has detected thousands of bugs and vulnerabilities in various applications. Although effective, there lacks systematic analysis of gaps faced by fuzzing. As a technique of defect detection, fuzzing is required to narrow down the gaps between the entire input space and the defect space. Without limitation on the generated inputs, the input space is infinite. However, defects are sparse in an application, which indicates that the defect space is much smaller than the entire input space. Besides, because fuzzing generates numerous test cases to repeatedly examine targets, it requires fuzzing to perform in an automatic manner. Due to the complexity of applications and defects, it is challenging to automatize the execution of diverse applications. In this paper, we systematically review and analyze the gaps as well as their solutions, considering both breadth and depth. This survey can be a roadmap for both beginners and advanced developers to better understand fuzzing.


### Survey of Software Fuzzing Techniques

* <img src="image/pdf_24px.png">[Paper](https://www.researchgate.net/publication/356980212_Survey_of_Software_Fuzzing_Techniques)

**Abstract:** As cybersecurity becomes more than an afterthought and receives the attention it deserves, it is critical to use tools that examine and understand the flaws within programs. One such tool is fuzzing, a testing process which subjects a system or program to a stream of input data, where the goal of the test is to check for exploitable edge cases. This testing is vital, since a hacker may bombard a system with a variety of inputs and scan the system for weaknesses after causing the system to fail. As the security landscape is constantly shifting, there is a need for an up to date review of the state of literature so readers can make informed decisions about the current state of their systems and software. This survey does that by summarizing current state-of-the art fuzzing approaches, classifying these approaches, and highlighting key insights into the current state of research. The paper also identifies current challenges and suggests future research directions in this area.


### A Review of Fuzzing Tools and Methods

* <img src="image/pdf_24px.png">[Paper](./Paper/2017_review.pdf)

**Abstract:** This paper reviewed some of the most noteworthy academic literature and practical work that has been produced in the field of fuzzing. We first examined how vulnerabilities come to exist in software and how security researchers find them. After a brief overview of common vulnerability types and methods of static analysis, we looked in depth at the field of fuzzing. Competing approaches to fuzzing were examined, from simple random inputs all the way to using genetic algorithms and taint analysis. The importance of measuring code coverage to evaluate the completeness of a fuzzing campaign was examined. Finally, the focus was placed on the fuzz testing of web browsers and the specific tools and techniques related to that.


# Differential Fuzzing

### SEDiff: Scope-Aware Differential Fuzzing to Test Internal Function Models in Symbolic Execution (FSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://www.cse.cuhk.edu.hk/~wei/papers/fse22_sediff.pdf)

**Abstract:** Symbolic execution has become a foundational program analysis technique. Performing symbolic execution unavoidably encounters internal functions (e.g., library functions) that provide basic operations such as string processing. Many symbolic execution engines construct internal function models that abstract function behaviors for scalability and compatibility concerns. Due to the high complexity of constructing the models, developers intentionally summarize only partial behaviors of a function, namely modeled functionalities, in the models. The correctness of the internal function models is critical because it would impact all applications of symbolic execution, e.g., bug detection and model checking.

A naive solution to testing the correctness of internal function models is to cross-check whether the behaviors of the models comply with their corresponding original function implementations. However, such a solution would mostly detect overwhelming inconsistencies concerning the unmodeled functionalities, which are out of the scope of models and thus considered false reports. We argue that a reasonable testing approach should target only the functionalities that developers intend to model. While being necessary, automatically identifying the modeled functionalities, i.e., the scope, is a significant challenge.

In this paper, we propose a scope-aware differential testing framework, SEDiff, to tackle this problem. We design a novel algorithm to automatically map the modeled functionalities to the code in the original implementations. SEDiff then applies scope-aware grey-box differential fuzzing to relevant code in the original implementations. It also equips a new scope-aware input generator and a tailored bug checker that efficiently and correctly detect erroneous inconsistencies. We extensively evaluated SEDiff on several popular real-world symbolic execution engines targeting binary, web and kernel. Our manual investigation shows that SEDiff precisely identifies the modeled functionalities and detects 46 new bugs in the internal function models used in the symbolic execution engines.


### T-Reqs: HTTP Request Smuggling with Differential Fuzzing (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](https://seclab.nu/static/publications/ccs2021treqs.pdf)

**Abstract:** HTTP Request Smuggling (HRS) is an attack that exploits the HTTP processing discrepancies between two servers deployed in a proxy-origin configuration, allowing attackers to smuggle hidden requests through the proxy. While this idea is not new, HRS is soaring in popularity due to recently revealed novel exploitation techniques and real-life abuse scenarios. In this work, we step back from the highly-specific exploits hogging the spotlight, and present the first work that systematically explores HRS within a scientific framework. We design an experiment infrastructure powered by a novel grammar-based differential fuzzer, test 10 popular server/proxy/CDN technologies in combinations, identify pairs that result in processing discrepancies, and discover exploits that lead to HRS. Our experiment reveals previously unknown ways to manipulate HTTP requests for exploitation, and for the first time documents the server pairs prone to HRS.


### CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing (2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2112.13064)

**Abstract:** The success of deep neural networks (DNNs) in real-world applications has benefited from abundant pre-trained models. However, the backdoored pre-trained models can pose a significant trojan threat to the deployment of downstream DNNs. Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks. Observing the trojan network behaviors shows that they are not just reflected by a single compromised neuron as proposed by previous work but attributed to the critical neural paths in the activation intensity and frequency of multiple neurons. This work formulates the DNN backdoor testing and proposes the CatchBackdoor framework. Via differential fuzzing of critical neurons from a small number of benign examples, we identify the trojan paths and particularly the critical ones, and generate backdoor testing examples by simulating the critical neurons in the identified paths. Extensive experiments demonstrate the superiority of CatchBackdoor, with higher detection performance than existing methods. CatchBackdoor works better on detecting backdoors by stealthy blending and adaptive attacks, which existing methods fail to detect. Moreover, our experiments show that CatchBackdoor may reveal the potential backdoors of models in Model Zoo.


### Duo: Differential Fuzzing for Deep Learning Operators (IEEE Transactions on Reliability 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9536229)

**Abstract:** Deep learning (DL) libraries reduce the barriers to the DL model construction. In DL libraries, various building blocks are DL operators with different functionality, responsible for processing high-dimensional tensors during training and inference. Thus, the quality of operators could directly impact the quality of models. However, existing DL testing techniques mainly focus on robustness testing of trained neural network models and cannot locate DL operatorsâ? defects. The insufficient test input and undetermined test output in operator testing have become challenging for DL library developers. In this article, we propose an approach, namely Duo, which combines fuzzing techniques and differential testing techniques to generate input and evaluate corresponding output. It implements mutation-based fuzzing to produce tensor inputs by employing nine mutation operators derived from genetic algorithms and differential testing to evaluate outputsâ? correctness from multiple operator instances. Duo is implemented in a tool and used to evaluate seven operators from TensorFlow, PyTorch, MNN, and MXNet in an experiment. The result shows that Duo can expose defects of DL operators and realize multidimension evaluation for DL operators from different DL libraries.


### DiFuzzRTL: Differential Fuzz Testing to Find CPU Bug (S&P 2021)

* <img src="image/github_24px.png">[Code](https://github.com/yannicnoller/hydiff)
  

**Abstract:** DifuzzRTL is a differential fuzz testing approach for CPU verification. We introduce new coverage metric, register-coverage, which comprehensively captures the states of an RTL design and correctly guides the input generation. DifuzzRTL automatically instruments register-coverage, randomly generates and mutates instructions defined in ISA, then cross-check against an ISA simulator to detect bugs.


### DPIFuzz: A Differential Fuzzing Framework to Detect DPI Elusion Strategies for QUIC (ACSAC 2020)

* <img src="image/pdf_24px.png">[Paper](https://cispa.de/en/research/publications/3220-dpifuzz-a-differential-fuzzing-frameworkto-detect-dpi-elusion-strategies-for-quic)

**Abstract:** QUIC is an emerging transport protocol that has the potential to replace TCP in the near future. As such, QUIC will become an important target for Deep Packet Inspection (DPI). Reliable DPI is essential, e.g., for corporate environments, to monitor traffic entering and leaving their networks. However, elusion strategies threaten the validity of DPI systems, as they allow attackers to carefully design traffic to fool and thus evade on-path DPI systems. While such elusion strategies for TCP are well documented, it is unclear if attackers will be able to elude QUIC-based DPI systems. In this paper, we systematically explore elusion methodologies for QUIC. To this end, we present DPIFuzz: a differential fuzzing framework which can automatically detect strategies to elude stateful DPI systems for QUIC. We use DPIFuzz to generate and mutate QUIC streams in order to compare (and find differences in) the server-side interpretations of five popular open-source QUIC implementations. We show that DPIFuzz successfully reveals DPI elusion strategies, such as using packets with duplicate packet numbers or exploiting the diverging handling of overlapping stream offsets by QUIC implementations. DPIFuzz additionally finds four security-critical vulnerabilities in these QUIC implementations.


### DifFuzz: Differential Fuzzing for Side-Channel Analysis (ICSE 2019)

* <img src="image/youdao_note_24px.png">[Reading Note](https://zhuanlan.zhihu.com/p/132466004)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE19_DIFFUZZ.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/ICSE19_DIFFUZZ_Slides.pdf)

**Abstract:** Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or response size. Side-channel vulnerabilities are difficult to reason about as they involve analyzing the correlations between resource usage over multiple program paths. We present DifFuzz, a fuzzing-based approach for detecting side-channel vulnerabilities related to time and space. DifFuzz automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secret-dependent paths. The methodology of DifFuzz is general and can be applied to programs written in any language. For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers. We evaluate DifFuzz on a large number of Java programs and demonstrate that it can reveal unknown side-channel vulnerabilities in popular applications. We also show that DifFuzz compares favorably against Blazer and Themis, two state-of-the-art analysis tools for finding side-channels in Java programs.


### Deep Differential Testing of JVM Implementations (ICSE 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE19_Deep.pdf)

**Abstract:** The Java Virtual Machine (JVM) is the cornerstone of the widely-used Java platform. Thus, it is critical to ensure the reliability and robustness of popular JVM implementations. However, little research exists on validating production JVMs. One notable effort is classfuzz, which mutates Java bytecode syntactically to stress-test different JVMs. It is shown that classfuzz mainly produces illegal bytecode files and uncovers defects in JVMs' startup processes. It remains a challenge to effectively test JVMs' bytecode verifiers and execution engines to expose deeper bugs.

This paper tackles this challenge by introducing classming, a novel, effective approach to performing deep, differential JVM testing. The key of classming is a technique, live bytecode mutation, to generate, from a seed bytecode file f, likely valid, executable (live) bytecode files: (1) capture the seed f's live bytecode, the sequence of its executed bytecode instructions; (2) repeatedly manipulate the control- and data-flow in f's live bytecode to generate semantically different mutants; and (3) selectively accept the generated mutants to steer the mutation process toward live, diverse mutants. The generated mutants are then employed to differentially test JVMs.

We have evaluated classming on mainstream JVM implementations, including OpenJDK's HotSpot and IBM's J9, by mutating the DaCapo benchmarks. Our results show that classming is very effective in uncovering deep JVM differences. More than 1,800 of the generated classes exposed JVM differences, and more than 30 triggered JVM crashes. We analyzed and reported the JVM runtime differences and crashes, of which 14 have already been confirmed/fixed, including a highly critical security vulnerability in J9 that allowed untrusted code to disable the security manager and elevate its privileges (CVE-2017-1376).


### Hunting for bugs in code coverage tools via randomized differential testing (ICSE 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#9tQHI)

* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/125157536)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE19_Difftest.pdf)

**Abstract:** Reliable code coverage tools are critically important as it is heavily used to facilitate many quality assurance activities, such as software testing, fuzzing, and debugging. However, little attention has been devoted to assessing the reliability of code coverage tools. In this study, we propose a randomized differential testing approach to hunting for bugs in the most widely used C code coverage tools. Specifically, by generating random input programs, our approach seeks for inconsistencies in code coverage reports produced by different code coverage tools, and then identifies inconsistencies as potential code coverage bugs. To effectively report code coverage bugs, we addressed three specific challenges: (1) How to filter out duplicate test programs as many of them triggering the same bugs in code coverage tools; (2) how to automatically reduce large test programs to much smaller ones that have the same properties; and (3) how to determine which code coverage tools have bugs? The extensive evaluations validate the effectiveness of our approach, resulting in 42 and 28 confirmed/fixed bugs for gcov and llvm-cov, respectively. This case study indicates that code coverage tools are not as reliable as it might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of code coverage tools. This work opens up a new direction in code coverage validation which calls for more attention in this area.


### Different is Good: Detecting the Use of Uninitialized Variables through Differential Replay (CCS 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#COkNh)

* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/131312498)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS19_Different.pdf)

**Abstract:** The use of uninitialized variables is a common issue. It could cause kernel information leak, which defeats the widely deployed security defense, i.e., kernel address space layout randomization (KASLR). Though a recent system called Bochspwn Reloaded reported multiple memory leaks in Windows kernels, how to effectively detect this issue is still largely behind.

In this paper, we propose a new technique, i.e., differential replay, that could effectively detect the use of uninitialized variables. Specifically, it records and replays a program's execution in multiple instances. One instance is with the vanilla memory, the other one changes (or poisons) values of variables allocated from the stack and the heap. Then it compares program states to find references to uninitialized variables. The idea is that if a variable is properly initialized, it will overwrite the poisoned value and program states in two running instances should be the same. After detecting the differences, our system leverages the symbolic taint analysis to further identify the location where the variable was allocated. This helps us to identify the root cause and facilitate the development of real exploits. We have implemented a prototype called TimePlayer. After applying it to both Windows 7 and Windows 10 kernels (x86/x64), it successfully identified 34 new issues and another 85 ones that had been patched (some of them were publicly unknown.) Among 34 new issues, 17 of them have been confirmed as zero-day vulnerabilities by Microsoft.


### Differential Program Analysis with Fuzzing and Symbolic Execution (ASE 2018)

* <img src="image/pdf_24px.png">[Paper](./Paper/ASE18_Differential.pdf)

**Abstract:** Differential program analysis means to identify the behavioral divergences in one or multiple programs, and it can be classified into two categories: identify the behavioral divergences (1) between two program versions for the same input (aka regression analysis), and (2) for the same program with two different inputs (e.g, side-channel analysis). Most of the existent approaches for both subproblems try to solve it with single techniques, which suffer from its weaknesses like scalability issues or imprecision. This research proposes to combine two very strong techniques, namely fuzzing and symbolic execution to tackle these problems and provide scalable solutions for real-world applications. The proposed approaches will be implemented on top of state-of-the-art tools like AFL and Symbolic PathFinder to evaluate them against existent work.


### NEZHA: Efficient Domain-Independent Differential Testing (S&P 2017)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=7e602068c641217947c97b287291c9c7&sub=32BD04EB8C00424E87FA7B948D38EC96)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP17_NEZHA.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/SP17_NEZHA_Slides.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/nezha-dt/nezha)

**Abstract:** Differential testing uses similar programs as cross-referencing oracles to find semantic bugs that do not exhibit explicit erroneous behaviors like crashes or assertion failures. Unfortunately, existing differential testing tools are domain-specific and inefficient, requiring large numbers of test inputs to find a single bug. In this paper, we address these issues by designing and implementing NEZHA, an efficient input-format-agnostic differential testing framework. The key insight behind NEZHA's design is that current tools generate inputs by simply borrowing techniques designed for finding crash or memory corruption bugs in individual programs (e.g., maximizing code coverage). By contrast, NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger semantic bugs. We introduce the notion of Î´-diversity, which summarizes the observed asymmetries between the behaviors of multiple test applications. Based on Î´-diversity, we design two efficient domain-independent input generation mechanisms for differential testing, one gray-box and one black-box. We demonstrate that both of these input generation schemes are significantly more efficient than existing tools at finding semantic bugs in real-world, complex software.


### Coverage-Directed Differential Testing of JVM Implementations (PLDI 2016)

* <img src="image/pdf_24px.png">[Paper](./Paper/PLDI16_JVM.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/PLDI16_JVM.pptx)
  

Java virtual machine (JVM) is a core technology, whose reliability is critical. Testing JVM implementations requires painstaking effort in designing test classfiles (*.class) along with their test oracles. An alternative is to employ binary fuzzing to differentially test JVMs by blindly mutating seeding classfiles and executing the resulting mutants on different JVMs for revealing inconsistent behaviors. However, this blind approach is not cost effective in practice because (1) most of the mutants are invalid and redundant, and (2) the discovered JVM discrepancies, if any, mostly only concern compatibility issues, rather than actual defects.
This paper tackles this challenge by introducing classfuzz, a coverage-directed fuzzing approach that focuses on representative classfiles for differential JVM testing. Our core insight is to (1) mutate seeding classfiles using a set of predefined mutation operators and employ Markov Chain Monte Carlo (MCMC) sampling to guide mutator selection, and (2) execute the mutants on a reference JVM implementation and use coverage uniqueness as a discipline for accepting representative ones. The accepted classfiles are used as inputs to differentially test JVMs and find defects.
We have implemented classfuzz and conducted an extensive evaluation of it against existing fuzz testing algorithms. Our evaluation results show that classfuzz can enhance the ratio of discrepancy-triggering classfiles from 1.7% to 11.9%. We have also reported 62 defect-indicative discrepancies, along with the test classfiles, to JVM developers. A number of our reported issues have already been confirmed as JVM defects, and some even match recent clarifications and changes to the JVM specification, Java SE 8 Edition.



# Evaluate Fuzzing

### FIXREVERTER: A Realistic Bug Injection Methodology for Benchmarking Fuzz Testing (USENIX Security2022)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22-zhang-zenong.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/SlaterLatiao/RevBugBench)

**Abstract:** Fuzz testing is an active area of research with proposed improvements published at a rapid pace. Such proposals are assessed empirically: Can they be shown to perform better than the status quo? Such an assessment requires a benchmark of target programs with well-identified, realistic bugs. To ease the construction of such a benchmark, this paper presents FIXREVERTER, a tool that automatically injects realistic bugs in a program. FIXREVERTER takes as input a bugfix pattern which contains both code syntax and semantic conditions. Any code site that matches the specified syntax is undone if the semantic conditions are satisfied, as checked by static analysis, thus (re)introducing a likely bug. This paper focuses on three bugfix patterns, which we call conditional-abort, conditional-execute, and conditional-assign, based on a study of fixes in a corpus of Common Vulnerabilities and Exposures (CVEs). Using FIXREVERTER we have built REVBUGBENCH, which consists of 10 programs into which we have injected nearly 8,000 bugs; the programs are taken from FuzzBench and Binutils, and represent common targets of fuzzing evaluations. We have integrated REVBUGBENCH into the FuzzBench service, and used it to evaluate five fuzzers. Fuzzing performance varies by fuzzer and program, as desired/expected. Overall, 219 unique bugs were reported, 19% of which were detected by just one fuzzer.

### On the Reliability of Coverage-Based Fuzzer Benchmarking (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://mboehme.github.io/paper/ICSE22.pdf)

**Abstract:** In one of the largest studies of measures of fuzzer effectiveness, involving over 13 million lines of code, 10 fuzzers, and 24 CPU years worth of fuzzing campaigns, we identify a \emph{very strong correlation} between the coverage achieved and the number of bugs found by a fuzzer: A fuzzer that covers more code also finds more bugs. Because bug-based benchmarking is expensive and subject to several threats to validity, it might seem reasonable to compare fuzzers in terms of the coverage achieved, and from that derive empirical claims about a fuzzerâs superiority at finding bugs.

Curiously enough, however, we find \emph{no strong agreement} on which fuzzer is superior if we compared multiple fuzzers in terms of coverage achieved instead of the number of bugs found. The fuzzer best at achieving coverage, may \emph{not} be best at finding bugs.


### Mutation Analysis: Answering the Fuzzing Challenge (2022)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2201.11303)

**Abstract:** Fuzzing is one of the fastest growing fields in software testing. The idea behind fuzzing is to check the behavior of software against a large number of randomly generated inputs, trying to cover all interesting parts of the input space, while observing the tested software for anomalous behaviour. One of the biggest challenges facing fuzzer users is how to validate software behavior, and how to improve the quality of oracles used. While mutation analysis is the premier technique for evaluating the quality of software test oracles, mutation score is rarely used as a metric for evaluating fuzzer quality. Unless mutation analysis researchers can solve multiple problems that make applying mutation analysis to fuzzing challenging, mutation analysis may be permanently sidelined in one of the most important areas of testing and security research. This paper attempts to understand the main challenges in applying mutation analysis for evaluating fuzzers, so that researchers can focus on solving these challenges.


### Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph (Access 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9618942)

**Abstract:** The OS kernel, which has full system privileges, is an attractive attack surface. A kernel fuzzer that targets system calls in fuzzing is a popular tool for discovering kernel bugs that can induce kernel privilege escalation attacks. To the best of our knowledge, the relevance of code coverage, which is obtained by fuzzing, to the system call has not been studied yet. For instance, modern coverage-guided kernel fuzzers, such as Syzkaller, estimate code coverage by comparing the entire set of executed basic blocks (or edges) regardless of the system call relevancy. Our insight is that the system call relevancy could be an essential performance indicator for realizing kernel fuzzing. In this regard, this study aims to assess the system call-related code coverage of kernel fuzzers. For this purpose, we have developed a practical assessment system that leverages the Intel PT and KCOV and assessed the Linux kernel fuzzers, such as Syzkaller, Trinity, and ext4 fuzzer. The experiments on different kernel versions demonstrated that approximately 32,000â?47,000 functions are implemented in the Linux kernel, and approximately 9.7â?15.2% are related to the system call. Our finding is that fuzzers that achieve higher code coverage in conventional metrics do not execute more basic blocks related to system calls. Thus, we recommend that kernel fuzzers use both system call-related functions and regular basic blocks in coverage metrics to assess fuzzing performance or to improve coverage feedback.


### FuzzBench: An Open Fuzzer Benchmarking Platform and Service (FSE 2021)

* <img src="image/pdf_24px.png">[Paper](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/64574e1a7cfc9456c273ce03574dacfcd6ad9d54.pdf)

**Abstract:** Fuzzing is a key tool used to reduce bugs in production software. At Google, fuzzing has uncovered tens of thousands of bugs. Fuzzing is also a popular subject of academic research. In 2020 alone, over
120 papers were published on the topic of improving, developing, and evaluating fuzzers and fuzzing techniques. Yet, proper evaluation of fuzzing techniques remains elusive. The community has struggled to converge on methodology and standard tools for fuzzer evaluation.

To address this problem, we introduce FuzzBench as an opensource turnkey platform and free service for evaluating fuzzers. It aims to be easy to use, fast, reliable, and provides reproducible experiments. Since its release in March 2020, FuzzBench has been widely used both in industry and academia, carrying out more than 150 experiments for external users. It has been used by several published and in-the-work papers from academic groups, and has had real impact on the most widely used fuzzing tools in industry. The presented case studies suggest that FuzzBench is on its way to becoming a standard fuzzer benchmarking platform.


### An Empirical Study of OSS-Fuzz Bugs (MSR 2021)

* <img src="image/pdf_24px.png">[Paper](https://squareslab.github.io/materials/DingOSSFuzz21.pdf)

**Abstract:** Continuous fuzzing is an increasingly popular technique for automated quality and security assurance. Google maintains OSS-Fuzz: a continuous fuzzing service for open source software. We conduct the first empirical study of OSS-Fuzz, analyzing 23,907 bugs found in 316 projects. We examine the characteristics of fuzzer-found faults, the lifecycles of such faults, and the evolution of fuzzing campaigns over time. We find that OSS-Fuzz is often effective at quickly finding bugs, and developers are often quick to patch them. However, flaky bugs, timeouts, and out of memory errors are problematic, people rarely file CVEs for security vulnerabilities, and fuzzing campaigns often exhibit punctuated equilibria, where developers might be surprised by large spikes in bugs found. Our findings have implications on future fuzzing research and practice.

### Industrial Oriented Evaluation of Fuzzing Techniques (ICST 2021)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/icst21.pdf)

**Abstract:** Fuzzing is a promising method for discovering vulnerabilities. Recently, various techniques are developed to improve the efficiency of fuzzing, and impressive gains are observed in evaluation results. However, evaluation is complex, as many factors affect the results, for example, test suites, baseline and metrics. Even more, most experiment setups are lab-oriented, lacking industrial settings such as large code-base and parallel runs. The correlation between the academic evaluation results and the bug-finding ability in real industrial settings has not been sufficiently studied.

In this paper, we test representative fuzzing techniques to reveal their efficiency in industrial settings. First, we apply typical fuzzers on academic widely used small projects from LAVAM suite. We also apply the same fuzzers on large practical projects from Googleâs fuzzer-test-suite, which is rarely used in academic settings. Both experiments are performed in both single and parallel run. By analyzing the results, we found that most optimizations working well on LAVA-M suite fail to achieve satisfying results on Googleâs fuzzer-test-suite (e.g. compared to AFL, QSYM detects 82x more synthesized bugs in LAVA-M, but only detects 26% real bugs in Googleâs fuzzer-test-suite), and the original AFL even outperforms most academic optimization variants in industry widely used parallel runs (e.g. AFL covers 13% more paths than AFLFast). Then, we summarize common pitfalls of those optimizations, analyze the corresponding root causes, and propose potential directions such as orchestrations and synchronization to overcome the problems. For example, when running in parallel on those large practical projects, the proposed horizontal orchestration could cover 36%-82% more paths, and discover 46%-150% more unique crashes or bugs, compared to fuzzers such as AFL, FairFuzz and QSYM.

### UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers (USENIX Security2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2010.01785.pdf)

**Abstract:** A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the benchmarks, performance metrics, and/or environments for evaluation, which buries the useful insights and thus impedes the discovery of promising fuzzing primitives. In this paper, we design and develop UNIFUZZ, an open-source and metrics-driven platform for assessing fuzzers in a comprehensive and quantitative manner. Specifically, UNIFUZZ to date has incorporated 35 usable fuzzers, a benchmark of 20 real-world programs, and six categories of performance metrics. We first systematically study the usability of existing fuzzers, find and fix a number of flaws, and integrate them into UNIFUZZ. Based on the study, we propose a collection of pragmatic performance metrics to evaluate fuzzers from six complementary perspectives. Using UNIFUZZ, we conduct in-depth evaluations of several prominent fuzzers including AFL [1], AFLFast [2], Angora [3], Honggfuzz [4], MOPT [5], QSYM [6], T-Fuzz [7] and VUzzer64 [8]. We find that none of them outperforms the others across all the target programs, and that using a single metric to assess the performance of a fuzzer may lead to unilateral conclusions, which demonstrates the significance of comprehensive metrics. Moreover, we identify and investigate previously overlooked factors that may significantly affect a fuzzer's performance, including instrumentation methods and crash analysis tools. Our empirical results show that they are critical to the evaluation of a fuzzer. We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.


### My Fuzzer Beats Them All! Developing a Framework for Fair Evaluation and Comparison of Fuzzers (2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2108.07076)

**Abstract:** Fuzzing has become one of the most popular techniques to identify bugs in software. To improve the fuzzing process, a plethora of techniques have recently appeared in academic literature. However, evaluating and comparing these techniques is challenging as fuzzers depend on randomness when generating test inputs. Commonly, existing evaluations only partially follow best practices for fuzzing evaluations. We argue that the reason for this are twofold. First, it is unclear if the proposed guidelines are necessary due to the lack of comprehensive empirical data in the case of fuzz testing. Second, there does not yet exist a framework that integrates statistical evaluation techniques to enable fair comparison of fuzzers. To address these limitations, we introduce a novel fuzzing evaluation framework called SENF (Statistical EvaluatioN of Fuzzers). We demonstrate the practical applicability of our framework by utilizing the most wide-spread fuzzer AFL as our baseline fuzzer and exploring the impact of different evaluation parameters (e.g., the number of repetitions or run-time), compilers, seeds, and fuzzing strategies. Using our evaluation framework, we show that supposedly small changes of the parameters can have a major influence on the measured performance of a fuzzer.


### A Quantitative Comparison of Covera (AST 2020)

* <img src="image/pdf_24px.png">[Paper](https://sites.google.com/site/yoshidaatnu/TsuzukiAST2020.pdf)

**Abstract:** In recent years, many tools have been developed for fuzz testing that generates and executes test cases repeatedly. However, many studies use different fuzzing targets and evaluation criteria and then it is difficult to compare the performance of the existing tools for fuzz testing. Therefore, we prepared a unified collection of fuzzing targets and then compared 8 fuzzers with the benchmark. In comparison, we compared the fuzzers based on the number of execution paths and branch coverage. The result shows that the number of execution paths is significantly different between the fuzzers. On the other hand, the statistical difference is not confirmed between the branch converges of the fuzzers.


### Fuzzing: On the Exponential Cost of Vulnerability Discovery (FSE 2020)

* <img src="image/pdf_24px.png">[Paper](https://mboehme.github.io/paper/FSE20.EmpiricalLaw.pdf)

**Abstract:** We present counterintuitive results for the scalability of fuzzing. Given the same non-deterministic fuzzer, finding the same bugs linearly faster requires linearly more machines. Yet, finding linearly more bugs in the same time requires exponentially more machines. Similarly, with exponentially more machines, we can cover the same code exponentially faster, but uncovered code only linearly faster. In other words, re-discovering the same vulnerabilities (or achieving the same coverage) is cheap but finding new vulnerabilities (or achieving more coverage) is expensive. This holds even under the simplifying assumption of no parallelization overhead. 

We derive these observations from over four CPU years worth of fuzzing campaigns involving almost three hundred open source programs, two state-of-the-art greybox fuzzers, four measures of code coverage, and two measures of vulnerability discovery. We provide a probabilistic analysis and conduct simulation experiments to explain this phenomenon.



### A Feature-Oriented Corpus for understanding, Evaluating and Improving Fuzz Testing (ASIACCS 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/ASIACCS19_Feature-Oriented.pdf)

**Abstract:** Fuzzing is a promising technique for detecting security vulnerabilities. Newly developed fuzzers are typically evaluated in terms of the number of bugs found on vulnerable programs/binaries. However,existing corpora usually do not capture the features that prevent fuzzers from finding bugs, leading to ambiguous conclusions on the pros and cons of the fuzzers evaluated. A typical example is that Driller detects more bugs than AFL, but its evaluation cannot establish if the advancement of Driller stems from the concolic execution or not, since, for example, its ability in resolving a dataset`s magic values is unclear. In this paper, we propose to address the above problem by generating corpora based on search-hampering features. As a proof-of-concept, we have designed FEData, a prototype corpus that currently focuses on four search-hampering features to generate vulnerable programs for fuzz testing. Unlike existing corpora that can only answer "how", FEData can also further answer "why" by exposing (or understanding) the reasons for the identified weaknesses in a fuzzer. The "why" information serves as the key to the improvement of fuzzers.


### Be Sensitive and Collaborative: Analyzing Impact of Coverage Metrics in Greybox Fuzzing (RAID 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/RAID19_Coverage.pdf)

**Abstract:** Coverage-guided greybox fuzzing has become one of the most common techniques for finding software bugs. Coverage metric, which decides how a fuzzer selects new seeds, is an essential parameter of fuzzing and can significantly affect the results. While there are many existing works on the effectiveness of different coverage metrics on software testing, little is known about how different coverage metrics could actually affect the fuzzing results in practice. More importantly, it is unclear whether there exists one coverage metric that is superior to all the other metrics. In this paper, we report the first systematic study on the impact of different coverage metrics in fuzzing. To this end, we formally define and discuss the concept of sensitivity, which can be used to theoretically compare different coverage metrics. We then present several coverage metrics with their variants. We conduct a study on these metrics with the DARPA CGC dataset, the LAVA-M dataset, and a set of real-world applications (a total of 221 binaries). We find that because each fuzzing instance has limited resources (time and computation power), (1) each metric has its unique merit in terms of flipping certain types of branches (thus vulnerability finding) and (2) there is no grand slam coverage metric that defeats all the others. We also explore combining different coverage metrics through cross-seeding, and the result is very encouraging: this pure fuzzing based approach can crash at least the same numbers of binaries in the CGC dataset as a previous approach (Driller) that combines fuzzing and concolic execution. At the same time, our approach uses fewer computing resources.


### Study and Comparison of General Purpose Fuzzers

* <img src="image/pdf_24px.png">[Paper](./Paper/J19_Study.pdf)

**Abstract:** Fuzz testing is a widely used technique for the detection of vulnerabilities whose popularity has led to the development of various tools that do fuzz testing. General-purpose fuzzers work in all domains while some other fuzzers are targeted towards some specific domain. Evaluation of these tools is not an easy task since different fuzzing tools excel in dierent domains. In this paper, we evaluate 3 such general-purpose fuzzing tools namely libFuzzer, American Fuzzy Lop(AFL) and honggfuzz on 2 metrics, i.e. their bug finding capability and their code coverage. We use the google fuzzer-test-suite which has 24 applications spanning several domains. libFuzzer performs best out of the three in finding memory leaks and out-of-memory related bugs but for other kinds of bugs, all three perform at par. honggfuzz seems to be the best in terms of coverage, though libFuzzer is not far behind, which we believe is because of our runs being of short duration.


### Evaluating Fuzz Testing (CCS 2018)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS18_Evaluating.pdf)

**Abstract:** Fuzz testing has enjoyed great success at discovering security critical bugs in real software. Recently, researchers have devoted significant effort to devising new fuzzing techniques, strategies, and algorithms. Such new ideas are primarily evaluated experimentally so an important question is: What experimental setup is needed to produce trustworthy results? We surveyed the recent research literature and assessed the experimental evaluations carried out by 32 fuzzing papers. We found problems in every evaluation we considered. We then performed our own extensive experimental evaluation using an existing fuzzer. Our results showed that the general problems we found in existing experimental evaluations can indeed translate to actual wrong or misleading assessments. We conclude with some guidelines that we hope will help improve experimental evaluations of fuzz testing algorithms, making reported results more robust.


# Instrumentation

### InstruGuard: Find and Fix Instrumentation Errors for Coverage-based Greybox Fuzzing (ASE 2021)

* <img src="image/pdf_24px.png">[Paper](https://ajax4sec.github.io/papers/ASE_2021.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/Marsman1996/instruguard)

**Abstract:** As one of the most successful methods at vulnerability discovery, coverage-based greybox fuzzing relies on the lightweight compiler-level instrumentation to achieve the finegrained coverage feedback of the target program. Researchers improve it by optimizing the coverage metrics without questioning the correctness of the instrumentation. However, instrumentation errors, including missed instrumentation locations and redundant instrumentation locations, harm the ability of fuzzers. According to our experiments, it is a common and severe problem in various coverage-based greybox fuzzers and
at different compiler optimization levels.

In this paper, we design and implement InstruGuard, an open-source and pragmatic platform to find and fix instrumentation errors. It detects instrumentation errors by static analysis on target binaries, and fixes them with a general solution based on binary rewriting. To study the impact of instrumentation
errors and test our solutions, we built a dataset of 15 real-world rograms and selected 6 representative fuzzers as targets. We used InstruGuard to check and repair the instrumented binaries with different fuzzers and different compiler optimization options. To evaluate the effectiveness of the repair, we ran the
fuzzers with original instrumented programs and the repaired ones, and compared the fuzzing results from aspects of execution paths, line coverage, and real bug findings. The results showed that InstruGuard had corrected the instrumentation errors of different fuzzers and helped to find more bugs in the dataset.
Moreover, we discovered one new zero-day vulnerability missed by other fuzzers with fixed instrumentation but without any changes to the fuzzers.


### RIFF: Reduced Instruction Footprint for Coverage-Guided Fuzzing (USENIX ATC 2021)

* <img src="image/youtube.png">[Video](https://youtu.be/x3KQjjlteL8)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/system/files/atc21_slides_wang-mingzhe.pdf)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/atc21.pdf)

**Abstract:** Coverage-guided fuzzers use program coverage measurements to explore different program paths efficiently. The coverage pipeline consists of runtime collection and post-execution processing procedures. First, the target program executes instrumentation code to collect coverage information. Then the fuzzer performs an expensive analysis on the collected data, yet most program executions lead to no increases in coverage. Inefficient implementations of these steps significantly reduce the fuzzer's overall throughput.

In this paper, we propose RIFF, a highly efficient program coverage measurement mechanism to reduce fuzzing overhead. For the target program, RIFF moves computations originally done at runtime to instrumentation-time through static program analysis, thus reducing instrumentation code to a bare minimum. For the fuzzer, RIFF processes coverage with different levels of granularity and utilizes vector instructions to improve throughput.

We implement RIFF in state-of-the-art fuzzers such as AFL and MOpt and evaluate its performance on real-world programs in Google's FuzzBench and fuzzer-test-suite. The results show that RIFF improves coverage measurement efficiency of fuzzers by 23Ã and 6Ã during runtime collection and post-execution processing, respectively. As a result, the fuzzers complete 147% more executions, and use only 6.53 hours to reach the 24-hour coverage of baseline fuzzers on average.


### Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection (TSE 2021)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP20_RetroWrite.pdf)

**Abstract:** The utility of a test set of program inputs is strongly influenced by its diversity and its size. Syntax coverage has become a standard proxy for diversity. Although more sophisticated measures exist, such as proximity of a sample to a uniform distribution, methods to use them tend to be type dependent. We use r-wise hash functions to create a novel, semantics preserving, testability transformation for C programs that we call HashFuzz. Use of HashFuzz improves the diversity of test sets produced by instrumentation-based fuzzers. We evaluate the effect of the HashFuzz transformation on eight programs from the Google Fuzzer Test Suite using four state-of-the-art fuzzers that have been widely used in previous research. We demonstrate pronounced improvements in the performance of the test sets for the transformed programs across all the fuzzers that we used. These include strong improvements in diversity in every case, maintenance or small improvement in branch coverage -- up to 4.8% improvement in the best case, and significant improvement in unique crash detection numbers -- between 28% to 97% increases compared to test sets for untransformed programs.


### RetroWrite: Statically Instrumenting COTS Binaries for Fuzzing and Sanitization (S&P 2020)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2019/11/05/retrowrite-statically-instrumenting-cots-binaries-for-fuzzing-and-sanitization/)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP20_RetroWrite.pdf)

**Abstract:** Analyzing the security of closed source binaries is currently impractical for end-users, or even developers who rely on third-party libraries. Such analysis relies on automatic vulnerability discovery techniques, most notably fuzzing with sanitizers enabled. The current state of the art for applying fuzzing or sanitization to binaries is a dynamic binary translation, which has a prohibitive performance overhead. The alternate technique, static binary rewriting, cannot fully recover symbolization information and hence has difficulty modifying binaries to track code coverage for fuzzing or to add security checks for sanitizers.

The ideal solution for binary security analysis would be a static rewriter that can intelligently add the required instrumentation as if it were inserted at compile time. Such instrumentation requires an analysis to statically disambiguate between references and scalars, a problem known to be undecidable in the general case. We show that recovering this information is possible in practice for the most common class of software and libraries: 64-bit, position-independent code. Based on this observation, we develop RetroWrite, binary-rewriting instrumentation to support American Fuzzy Lop (AFL) and Address Sanitizer (ASan), and show that it can achieve compiler-level performance while retaining precision. Binaries rewritten for coverage guided fuzzing using RetroWrite are identical in performance to compiler-instrumented binaries and outperform the default QEMU-based instrumentation by 4.5x while triggering more bugs. Our implementation of binary-only Address Sanitizer is 3x faster than Valgrindâs memcheck, the state-of-the-art binary-only memory checker, and detects 80% more bugs in our evaluation.


### INSTRCR: Lightweight instrumentation optimization based on coverage-guided fuzz testing (CCET 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCET19_INSTRCR.pdf)

**Abstract:** In Fuzzing facing binary coverage, the main role of instrumentation is feedback code coverage (in the case of Fuzz for binary, instrumentation can provide coverage information, which plays an important role in guiding the operation of seeds in Fuzz) . The current instrumentation optimization technique mainly relies on the control flow graph (CFG) to select key basic blocks at the basic block level, but the accuracy of this method is not high enough. Considering that the actual path in the actual operation of the binary may be different from the CFG generated in advance, this paper is based on the indirect jump that cannot be accurately analyzed in the CFG, and some of the basic blocks that can be optimized for high-frequency interpolation. According to the algorithm proposed in this paper, The combination of static analysis and dynamic analysis is used to continuously adjust and select key basic block nodes for instrumentation. It is verified by experiments that this kind of instrumentation method can effectively improve the coverage rate and reduce the overhead, and provide effective guidance for Fuzzing, which can effectively reduce the Fuzzerâs false negatives.


### Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing (S&P 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#PouZd)

* <img src="image/wechat_24px.png">[Reading Note](https://mp.weixin.qq.com/s/Cf_0F2bPmK8Gmp5JWnPOiA)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP19_Full-speed.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/FoRTE-Research/UnTracer-AFL)
  

**Abstract:** Coverage-guided fuzzing is one of the most successful approaches for discovering software bugs and security vulnerabilities. Of its three main components: (1) test case generation, (2) code coverage tracing, and (3) crash triage, code coverage tracing is a dominant source of overhead. Coverage-guided fuzzers trace every test case's code coverage through either static or dynamic binary instrumentation, or more recently, using hardware support. Unfortunately, tracing all test cases incurs significant performance penalties--even when the overwhelming majority of test cases and their coverage information are discarded because they do not increase code coverage. To eliminate needless tracing by coverage-guided fuzzers, we introduce the notion of coverage-guided tracing. Coverage-guided tracing leverages two observations: (1) only a fraction of generated test cases increase coverage, and thus require tracing; and (2) coverage-increasing test cases become less frequent over time. Coverage-guided tracing encodes the current frontier of coverage in the target binary so that it self-reports when a test case produces new coverage--without tracing. This acts as a filter for tracing; restricting the expense of tracing to only coverage-increasing test cases. Thus, coverage-guided tracing trades increased time handling coverage-increasing test cases for decreased time handling non-coverage-increasing test cases. To show the potential of coverage-guided tracing, we create an implementation based on the static binary instrumentor Dyninst called UnTracer. We evaluate UnTracer using eight real-world binaries commonly used by the fuzzing community. Experiments show that after only an hour of fuzzing, UnTracer's average overhead is below 1%, and after 24-hours of fuzzing, UnTracer approaches 0% overhead, while tracing every test case with popular white- and black-box-binary tracers AFL-Clang, AFL-QEMU, and AFL-Dyninst incurs overheads of 36%, 612%, and 518%, respectively. We further integrate UnTracer with the state-of-the-art hybrid fuzzer QSYM and show that in 24-hours of fuzzing, QSYM-UnTracer executes 79% and 616% more test cases than QSYM-Clang and QSYM-QEMU, respectively.


### INSTRIM Lightweight Instrumentation for Coverage-guided Fuzzing (NDSS 2018 workshop)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS18_INSTRIM.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/csienslab/instrim)

**Abstract:** Empowered by instrumentation, coverage-guided fuzzing monitors the program execution path taken by an input, and prioritizes inputs based on their contribution to code coverage. Although instrumenting every basic block ensures full visibility, it slows down the fuzzer and thus the speed of vulnerability discovery. This paper shows that thanks to common program structures (e.g., directed acyclic subgraphs and simple loops) and compiler optimization (e.g., knowledge of incoming edges), it is possible to accurately reconstruct coverage information by instrumenting only a small fraction of basic blocks. Specifically, we formulate the problem as a path differentiation problem on the control flow graph, and propose an efficient algorithm to select basic blocks that need to be instrumented so that different execution paths remain differentiable. We extend AFL to support such CFG-aware instrumentation. Our experiment results confirm that, compared with full instrumentation, our CFG-aware instrumentation only needs to instrument about 20% of basic blocks while offering 1.04â?1.78x speedup during fuzzing. Finally, we highlight several technical challenges and promising research directions to further improve instrumentation for fuzzing.


### SyzGen: Automated Generation of Syscall Specification of Closed-Source macOS Drivers (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](./Paper/ccs21_syzgen.pdf)

**Abstract:** Kernel drivers are a critical part of the attack surface since they constitute a large fraction of kernel codebase and oftentimes lack proper vetting, especially for those closed-source ones. Unfortunately, the complex input structure and unknown relationships/dependencies among interfaces make them very challenging to understand. Thus, security analysts primarily rely on manual audit for interface recovery to generate meaningful fuzzing test cases. In this paper, we present SyzGen, a first attempt to automate the generation ofbsyscall specifications for closed-source macOS drivers and facilitate interface-aware fuzzing. We leverage two insights to overcome the challenges of binary analysis: (1) iterative refinement of syscall knowledge and (2) extraction and extrapolation of ependencies from a small number of execution traces. We evaluated our approach on 25 targets. The results show that SyzGen can effectively produce high-quality specifications, leading to 34 bugs, including one that attackers can exploit to escalate privilege, and 2 CVEs to date.



# IoT or protocols fuzzing

### PrIntFuzz: Fuzzing Linux Drivers via Automated Virtual Device Simulation (ISSTA 2022)

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1145/3533767.3534226)

**Abstract:** Linux drivers share the same address space and privilege with the core of the kernel but have a much larger code base and attack surface. The Linux drivers are not well tested and have weaker security guarantees than the kernel. Missing support from hardware devices, existing fuzzing solutions fail to cover a large portion of the driver code, e.g., the initialization code and interrupt handlers. In this paper, we present PrIntFuzz, an efficient and universal fuzzing framework that can test the overlooked driver code, including the PRobing code and INTerrupt handlers. PrIntFuzz first extracts knowledge from the driver through inter-procedural field-sensitive, path-sensitive, and flow-sensitive static analysis. Then it utilizes the information to build a flexible and efficient simulator, which supports device probing, hardware interrupts emulation and device I/O interception. Lastly, PrIntFuzz applies a multi-dimension fuzzing strategy to explore the overlooked code. We have developed a prototype of PrIntFuzz and successfully simulated 311 virtual PCI (Peripheral Component Interconnect) devices, 472 virtual I2C (Inter-Integrated Circuit) devices, 169 virtual USB (Universal Serial Bus) devices, and found 150 bugs in the corresponding device drivers. We have submitted patches for these bugs to the Linux kernel community, and 59 patches have been merged so far. In a control experiment of Linux 5.10-rc6, PrIntFuzz found 99 bugs, while the state-of-the-art fuzzer only found 50. PrIntFuzz covers 11,968 basic blocks on the latest Linux kernel, while the state-of-the-art fuzzer Syzkaller only covers 2,353 basic blocks.


### SnapFuzz: High-Throughput Fuzzing of Network Applications (ISSTA 2022)

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1145/3533767.3534376)

**Abstract:** In recent years, fuzz testing has benefited from increased computational power and important algorithmic advances, leading to systems that have discovered many critical bugs and vulnerabilities in production software. Despite these successes, not all applications can be fuzzed efficiently. In particular, stateful applications such as network protocol implementations are constrained by their low fuzzing throughput and the need to develop fuzzing harnesses that reset their state and isolate their side effects.

In this paper, we present SnapFuzz, a novel fuzzing framework for network applications. SnapFuzz offers a robust architecture that transforms slow asynchronous network communication into fast synchronous communication, snapshots the target at the latest point at which it is safe to do so, speeds up all file operations by redirecting them to a custom in-memory filesystem, and removes the need for many fragile modifications, such as configuring time delays or writing clean-up scripts, together with several other improvements.

Using SnapFuzz, we fuzzed five popular networking applications: LightFTP, TinyDTLS, Dnsmasq, LIVE555 and Dcmqrscp. We report impressive performance speedups of 62.8x, 41.2x, 30.6x, 24.6x, and 8.4x, respectively, with significantly simpler fuzzing harnesses in all cases. Through its performance advantage, SnapFuzz has also found 12 extra crashes compared to AFLNet in these applications.


### Efficient Greybox Fuzzing of Applications in Linux-based IoT Devices via Enhanced User-mode Emulation (ISSTA 2022)

* <img src="image/youtube.png">[Video](https://youtu.be/BFXH9uTbr5M)

* <img src="image/blog_24px.jpg">[Reading Note](https://hackmd.io/@Zero871015/EQUAFL)

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1145/3533767.3534414)

**Abstract:** Greybox fuzzing has become one of the most effective vulnerability discovery techniques. However, greybox fuzzing techniques cannot be directly applied to applications in IoT devices. The main reason is that executing these applications highly relies on specific system environments and hardware. To execute the applications in Linux based IoT devices, most existing fuzzing techniques use full-system emulation for the purpose of maximizing compatibility. However, compared with user-mode emulation, full-system emulation suffers from great overhead. Therefore, some previous works, such as Firm-AFL, propose to combine full-system emulation and user-mode emulation to speed up the fuzzing process. Despite the attempts of trying to shift the application towards user-mode emulation, no existing technique supports to execute these applications fully in the user-mode emulation.

To address this issue, we propose EQUAFL, which can automatically setup the execution environment to execute embedded application under user-mode emulation. EQUAFL first executes the application under full-system emulation and observe for the key points where the program may get stuck or even crash during user-mode emulation. With the observed information, EQUAFL can migrate the needed environment for user-mode emulation. Then, EQUAFL uses an enhanced user-mode emulation to replay system calls of network, and resource management behaviors to fulfill the needs of the embedded application during its execution.

We evaluate EQUAFL on 70 network applications from different series of IoT devices. The result shows EQUAFL outperforms the state-of-the-arts in fuzzing efficiency (on average, 26 times faster than AFL-QEMU with full-system emulation, 14 times than Firm-AFL). We have also discovered ten vulnerabilities including six CVEs from the tested firmware images.


### FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks (FSE 2022)

* <img src="image/youtube.png">[Video](https://youtu.be/nSt-XSsOycE)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/document/9833593)

**Abstract:** Universal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smart phones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FuzzUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FuzzUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. We have implemented FuzzUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 8 CVEs. Furthermore, compared to the baseline, FuzzUSB has also demonstrated different improvements, including 3Ã higher code coverage, 50Ã improved bug-finding efficiency for Linux USB gadget stacks, 2Ã higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FuzzUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope.


### BrakTooth: Causing Havoc on Bluetooth Link Manager via Directed Fuzzing (USENIX SEC 2022)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/system/files/sec22_slides-garbelini.pdf)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22-garbelini.pdf)

**Abstract:** In this paper we propose, design and evaluate a systematic directed fuzzing framework to automatically discover implementation bugs in arbitrary Bluetooth Classic (BT) devices. The core of our fuzzer is the first over-the-air approach that takes full control of the BT controller baseband from the host. This enables us to intercept and modify arbitrary packets, as well as to inject packets out-of-order in lower layers of closed-source BT stack, i.e., Link Manager Protocol (LMP) and Baseband. To systematically guide our fuzzing process, we propose an extensible and novel rule-based approach to automatically construct the protocol state machine during normal over-the-air communication. In particular, by writing a simple set of rules to identify protocol messages, we can dynamically construct an abstracted protocol state machine, fuzz packets resulting from a state and validate responses from target devices. As of today, we have fuzzed 13 BT devices from 11 vendors and we have discovered a total of 18 unknown implementation flaws, with 24 common vulnerability exposures (CVEs) assigned. Furthermore, our discoveries were awarded with six bug bounties from certain vendors. Finally, to show the broader applicability of our framework beyond BT, we have extended our approach to fuzz other wireless protocols, which additionally revealed 6 unknown bugs in certain Wi-Fi and BLE Host stacks.


### Drifuzz: Harvesting Bugs in Device Drivers from Golden Seeds (USENIX SEC 2022)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/system/files/sec22_slides-shen_zekun.pdf)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22-shen-zekun.pdf)

**Abstract:** Peripheral hardware in modern computers is typically assumed to be secure and not malicious, and device drivers are implemented in a way that trusts inputs from hardware. However, recent vulnerabilities such as Broadpwn have demonstrated that attackers can exploit hosts through vulnerable peripherals, highlighting the importance of securing the OS-peripheral boundary. In this paper, we propose a hardware-free concolic-augmented fuzzer targeting WiFi and Ethernet drivers, and a technique for generating high-quality initial seeds, which we call golden seeds, that allow fuzzing to bypass difficult code constructs during driver initialization. Compared to prior work using symbolic execution or greybox fuzzing, Drifuzz is more successful at automatically finding inputs that allow network interfaces to be fully initialized, and improves fuzzing coverage by 214% (3.1¡Á) in WiFi drivers and 60% (1.6¡Á) for Ethernet drivers. During our experiments with fourteen PCI and USB network drivers, we find twelve previously unknown bugs, two of which were assigned CVEs.


### StateFuzz: System Call-Based State-Aware Linux Driver Fuzzing (USENIX SEC 2022)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/system/files/sec22_slides-zhao_bodong.pdf)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22-zhao-bodong.pdf)

**Abstract:** Coverage-guided fuzzing has achieved great success in finding software vulnerabilities. Existing coverage-guided fuzzers generally favor test cases that hit new code, and discard ones that exercise the same code. However, such a strategy is not optimum. A new test case exercising the same code could be better than a previous test case, as it may trigger new program states useful for code exploration and bug discovery.

In this paper, we assessed the limitation of coverage-guided fuzzing solutions and proposed a state-aware fuzzing solution StateFuzz to address this issue. First, we model program states with values of state-variables and utilize static analysis to recognize such variables. Then, we instrument target programs to track such variables' values and infer program state transition at runtime. Lastly, we utilize state information to prioritize test cases that can trigger new states, and apply a three-dimension feedback mechanism to fine-tune the evolutionary direction of coverage-guided fuzzers. We have implemented a prototype of StateFuzz, and evaluated it on Linux upstream drivers and Android drivers. Evaluation results show that StateFuzz is effective at discovering both new code and vulnerabilities. It finds 18 unknown vulnerabilities and 2 known but unpatched vulnerabilities, and reaches 19% higher code coverage and 32% higher state coverage than the state-of-the-art fuzzer Syzkaller.


### SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols using Snapshots (2022)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2201.04048)

**Abstract:** Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most state-of-the-art greybox fuzzers generally have the problems of slow speed and shallow state depth coverage in the process of fuzzing stateful network protocol programs which are able to remember and store details of the interactions. The existing greybox fuzzers for network protocol programs send a series of well-defined prefix sequences of input messages first and then send mutated messages to test the target state of a stateful network protocol. The process mentioned above causes a high time cost. In this paper, we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocol using snapshots. SNPSFuzzer dumps the context information when the network protocol program is under a specific state and restores it when the state needs to be fuzzed. Furthermore, we design a message chain analysis algorithm to explore more and deeper network protocol states. Our evaluation shows that, compared with the state-of-the-art network protocol greybox fuzzer AFLNET, SNPSFuzzer increases the speed of network protocol fuzzing by 112.0%-168.9% and improves path coverage by 21.4%-27.5% within 24 hours. Moreover, SNPSFuzzer exposes a previously unreported vulnerability in program Tinydtls.


### SnapFuzz: An Efficient Fuzzing Framework for Network Applications (2022)

* <img src="image/pdf_24px.png">[Paper]()

**Abstract:** In recent years, fuzz testing has benefited from increased computational power and important algorithmic advances, leading to systems that have discovered many critical bugs and vulnerabilities in production software. Despite these successes, not all applications can be fuzzed efficiently. In particular, stateful applications such as network protocol implementations are constrained by their low fuzzing throughput and the need to develop fuzzing harnesses that reset their state and isolate their side effects. In this paper, we present SnapFuzz, a novel fuzzing framework for network applications. SnapFuzz offers a robust architecture that transforms slow asynchronous network communication into fast synchronous communication based on UNIX domain sockets, speeds up all file operations by redirecting them to an in-memory filesystem, and removes the need for many fragile modifications, such as configuring time delays or writing cleanup scripts, together with several other improvements. Using SnapFuzz, we fuzzed five popular networking applications: LightFTP, Dnsmasq, LIVE555, TinyDTLS and Dcmqrscp. We report impressive performance speedups of 72.4x, 49.7x, 24.8x, 23.9x, and 8.5x, respectively, with significantly simpler fuzzing harnesses in all cases. Through its performance advantage, SnapFuzz has also found 12 previously-unknown crashes in these applications.


### State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing (2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2112.15498)

**Abstract:** The statefulness property of network protocol implementations poses a unique challenge for testing and verification techniques, including Fuzzing. Stateful fuzzers tackle this challenge by leveraging state models to partition the state space and assist the test generation process. Since not all states are equally important and fuzzing campaigns have time limits, fuzzers need effective state selection algorithms to prioritize progressive states over others. Several state selection algorithms have been proposed but they were implemented and evaluated separately on different platforms, making it hard to achieve conclusive findings. In this work, we evaluate an extensive set of state selection algorithms on the same fuzzing platform that is AFLNet, a state-of-the-art fuzzer for network servers. The algorithm set includes existing ones supported by AFLNet and our novel and principled algorithm called AFLNetLegion. The experimental results on the ProFuzzBench benchmark show that (i) the existing state selection algorithms of AFLNet achieve very similar code coverage, (ii) AFLNetLegion clearly outperforms these algorithms in selected case studies, but (iii) the overall improvement appears insignificant. These are unexpected yet interesting findings. We identify problems and share insights that could open opportunities for future research on this topic.


### ICS3Fuzzer: A Framework for Discovering Protocol Implementation Bugs in ICS Supervisory Software by Fuzzing (ACSAC 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/10.1145/3485832.3488028)

**Abstract:** The supervisory software is widely used in industrial control systems (ICSs) to manage field devices such as PLC controllers. Once compromised, it could be misused to control or manipulate these physical devices maliciously, endangering manufacturing process or even human lives. Therefore, extensive security testing of supervisory software is crucial for the safe operation of ICS. However, fuzzing ICS supervisory software is challenging due to the prevalent use of proprietary protocols. Without the knowledge of the program states and packet formats, it is difficult to enter the deep states for effective fuzzing.

In this work, we present a fuzzing framework to automatically discover implementation bugs residing in the communication protocols between the supervisory software and the field devices. To avoid heavy human efforts in reverse-engineering the proprietary protocols, the proposed approach constructs a state-book based on the readily-available execution trace of the supervisory software and the corresponding inputs. Then, we propose a state selection algorithm to find the protocol states that are more likely to have bugs. Our fuzzer distributes more budget on those interesting states. To quickly reach the interesting states, traditional snapshot-based method does not work since the communication protocols are time sensitive. We address this issue by synchronously managing external events (GUI operations and network traffic) during the fuzzing loop. We have implemented a prototype and used it to fuzz the supervisory software of four popular ICS platforms. We have found 13 bugs and received 3 CVEs, 2 are classified as critical (CVSS3.x score CRITICAL 9.8) and affected 40 different products.


### Westworld: Fuzzing-Assisted Remote Dynamic Symbolic Execution of Smart Apps on IoT Cloud Platforms (ACSAC 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/abs/10.1145/3485832.3488022)

**Abstract:** Existing symbolic execution typically assumes the analyzer can control the I/O environment and/or access the library code, which, however, is not the case when programs run on a remote proprietary execution environment managed by another party. For example, SmartThings, one of the most popular IoT platforms, is such a cloud-based execution environment. For programmers who write automation applications to be deployed on IoT cloud platforms, it raises significant challenges when they want to systematically test their code and find bugs. We propose fuzzing-assisted remote dynamic symbolic execution, which uses dynamic symbolic execution as backbone and utilizes fuzzing when necessary to automatically test programs running in a remote proprietary execution environment over which the analyzer has little control. As a case study, we enable it for analyzing smart apps running on SmartThings. We have developed a prototype and the evaluation shows that it is effective in testing smart apps and finding bugs.


### ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing (ISSTA 2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2101.05102.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/profuzzbench/profuzzbench)

**Abstract:** We present a new benchmark (ProFuzzBench) for stateful fuzzing of network protocols. The benchmark includes a suite of representative open-source network servers for popular protocols, and tools to automate experimentation. We discuss challenges and potential directions for future research based on this benchmark.


### TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing (USENIX ATC 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/atc21-zou.pdf)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/system/files/atc21_slides_zou.pdf)

**Abstract:** TCP stacks provide reliable data transmission in network, and thus they should be correctly implemented and well tested to ensure reliability and security. However, testing TCP stacks is difficult. First, a TCP stack accepts packets and system calls that have dependencies between each other, and thus generating effective test cases is challenging. Second, a TCP stack has various complex state transitions, but existing testing approaches target covering states instead of covering state transitions, and thus their testing coverage is limited. Finally, our study of TCP stack commits shows that 87% of bug-fixing commits are related to semantic bugs (such as RFC violations), but existing bug sanitizers can detect only memory bugs not semantic bugs.

In this paper, we design a novel fuzzing framework named TCP-Fuzz, to effectively test TCP stacks and detect bugs. TCP-Fuzz consists of three key techniques: (1) a dependency-based strategy that considers dependencies between packets and system calls, to generate effective test cases; (2) a transition-guided fuzzing approach that uses a new coverage metric named branch transition as program feedback, to improve the coverage of state transitions; (3) a differential checker that compares the outputs of multiple TCP stacks for the same inputs, to detect semantic bugs. We have evaluated TCP-Fuzz on five widely-used TCP stacks (TLDK, F-Stack, mTCP, FreeBSD TCP and Linux TCP), and find 56 real bugs (including 8 memory bugs and 48 semantic bugs). 40 of these bugs have been confirmed by related developers.


### ICPFuzzer: proprietary communication protocol fuzzing by using machine learning and feedback strategies (Cybersecurity 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9489311)

**Abstract:** The fuzzing test is able to discover various vulnerabilities and has more chances to hit the zero-day targets. And ICS(Industrial control system) is currently facing huge security threats and requires security standards, like ISO 62443, to ensure the quality of the device. However, some industrial proprietary communication protocols can be customized and have complicated structures, the fuzzing system cannot quickly generate test data that adapt to various protocols. It also struggles to define the mutation field without having prior knowledge of the protocols. Therefore, we propose a fuzzing system named ICPFuzzer that uses LSTM(Long short-term memory) to learn the features of a protocol and generates mutated test data automatically. We also use the responses of testing and adjust the weight strategies to further test the device under testing (DUT) to find more data that cause unusual connection status. We verified the effectiveness of the approach by comparing with the open-source and commercial fuzzers. Furthermore, in a real case, we experimented with the DLMS/COSEM for a smart meter and found that the test data can cause a unusual response. In summary, ICPFuzzer is a black-box fuzzing system that can automatically execute the testing process and reveal vulnerabilities that interrupt and crash industrial control communication. Not only improves the quality of ICS but also improves safety.


### Fuzzing With Optimized Grammar-Aware Mutation Strategies (Access 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9489311)

**Abstract:** Fuzzing is a widely used technique to discover vulnerabilities in software. However, for programs requiring highly structured inputs, the byte-based mutation strategies in existing fuzzers have difficulties in generating valid inputs. To resolve this challenge, Grammar-Based Fuzzing (GBF) utilizes existing grammar specifications to generate new inputs. Some GBFs perform mutation based on Abstract Syntax Trees (ASTs), which can generate inputs conforming to grammars. However, the existing GBFs neglect using feedback to optimize mutation strategies, and blindly generate inputs without considering the effectiveness of those inputs. In this paper, we use the power schedule and the subtree pool to optimize mutation strategies. Specifically, we first translate input files into ASTs, and extract subtrees from ASTs into a subtree pool. Then, we optimize the power schedule on AST nodes based on a probabilistic model. That is, we adaptively determine the time budget for mutating an AST node. Finally, we replace AST nodes along with their subtrees using the ones we select from the subtree pool. We implement a fuzzing tool to demonstrate our strategies. The experiment results show that our method outperforms the state-of-the-art methods in fuzzing efficiency.


### FIRM-COV: High-Coverage Greybox Fuzzing for IoT Firmware via Optimized Process Emulation (Access 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9489311)

**Abstract:** With the growing prevalence of the Internet of Things (IoT), related security threats have kept pace. The need to dynamically detect vulnerabilities in IoT devices cannot be overstated. In this work, we present FIRM-COV, the first high coverage-oriented greybox fuzzer for IoT firmware. FIRM-COV leverages newly optimized process emulation by targeting IoT programs and mining real-world vulnerabilities. FIRM-COV focuses on solving problems of IoT fuzzing based on empirical analyses, using the required structured input, the inaccuracy and instability of emulation, and the required high code coverage. By optimizing the existing emulation technique, FIRM-COV always maintains a stable state and achieves high accuracy when detecting vulnerabilities. We also implement a dictionary generation algorithm to provide structured input values and synergy scheduling to achieve high coverage and throughput. We compare FIRM-COV with other IoT fuzzing frameworks for eight real-world IoT devices. As a result, FIRM-COV achieves the highest coverage and throughput, finding the fastest and most 1-day vulnerabilities with almost no false-positives. It also found two 0-day vulnerabilities in real-world IoT devices within 24 h.


### DIANE: Identifying Fuzzing Triggers in Apps to Generate Under-constrained Inputs for IoT Devices (S&P 2020)

* <img src="image/youtube.png">[Video](https://youtu.be/mLyFoGIPFUY)

* <img src="image/pdf_24px.png">[Paper](https://conand.me/publications/redini-diane-2021.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/ucsb-seclab/diane)

**Abstract:** Internet of Things (IoT) devices have rooted themselves in the everyday life of billions of people. Thus, researchers have applied automated bug finding techniques to improve their overall security. However, due to the difficulties in extracting and emulating custom firmware, black-box fuzzing is often the only viable analysis option. Unfortunately, this solution mostly produces invalid inputs, which are quickly discarded by the targeted IoT device and do not penetrate its code. Another proposed approach is to leverage the companion app (i.e., the mobile app typically used to control an IoT device) to generate well-structured fuzzing inputs. Unfortunately, the existing solutions produce fuzzing inputs that are constrained by app-side validation code, thus significantly limiting the range of discovered vulnerabilities. In this paper, we propose a novel approach that overcomes these limitations. Our key observation is that there exist functions inside the companion app that can be used to generate optimal (i.e., valid yet under-constrained) fuzzing inputs. Such functions, which we call fuzzing triggers, are executed before any data-transforming functions (e.g., network serialization), but after the input validation code. Consequently, they generate inputs that are not constrained by app-side sanitization code, and, at the same time, are not discarded by the analyzed IoT device due to their invalid format. We design and develop Diane, a tool that combines static and dynamic analysis to find fuzzing triggers in Android companion apps, and then uses them to fuzz IoT devices automatically. We use Diane to analyze 11 popular IoT devices, and identify 11 bugs, 9 of which are zero days. Our results also show that without using fuzzing triggers, it is not possible to generate bug-triggering inputs for many devices.


### Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2105.05445.pdf)

**Abstract:** The proliferation of Internet of Things (IoT) devices has made people's lives more convenient, but it has also raised many security concerns. Due to the difficulty of obtaining and emulating IoT firmware, the black-box fuzzing of IoT devices has become a viable option. However, existing black-box fuzzers cannot form effective mutation optimization mechanisms to guide their testing processes, mainly due to the lack of feedback. It is difficult or even impossible to apply existing grammar-based fuzzing strategies. Therefore, an efficient fuzzing approach with syntax inference is required in the IoT fuzzing domain. To address these critical problems, we propose a novel automatic black-box fuzzing for IoT firmware, termed Snipuzz. Snipuzz runs as a client communicating with the devices and infers message snippets for mutation based on the responses. Each snippet refers to a block of consecutive bytes that reflect the approximate code coverage in fuzzing. This mutation strategy based on message snippets considerably narrows down the search space to change the probing messages. We compared Snipuzz with four state-of-the-art IoT fuzzing approaches, i.e., IoTFuzzer, BooFuzz, Doona, and Nemesys. Snipuzz not only inherits the advantages of app-based fuzzing (e.g., IoTFuzzer, but also utilizes communication responses to perform efficient mutation. Furthermore, Snipuzz is lightweight as its execution does not rely on any prerequisite operations, such as reverse engineering of apps. We also evaluated Snipuzz on 20 popular real-world IoT devices. Our results show that Snipuzz could identify 5 zero-day vulnerabilities, and 3 of them could be exposed only by Snipuzz. All the newly discovered vulnerabilities have been confirmed by their vendors.


### Learning-Based Fuzzing of IoT Message Brokers (ICST 2021)

* <img src="image/pdf_24px.png">[Paper](https://graz.pure.elsevier.com/en/publications/learning-based-fuzzing-of-iot-message-brokers)

**Abstract:** The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.


### RiverFuzzRL - an open-source tool to experiment with reinforcement learning for fuzzing (ICST 2021)

* <img src="image/pdf_24px.png">[Paper](http://scholar.google.com.sg/scholar_url?url=https://www.researchgate.net/profile/Alin-Stefanescu/publication/350789915_RiverFuzzRL_-_an_open-source_tool_to_experiment_with_reinforcement_learning_for_fuzzing/links/607213bc299bf1c911c1f61e/RiverFuzzRL-an-open-source-tool-to-experiment-with-reinforcement-learning-for-fuzzing.pdf&hl=en&sa=X&d=8691306607121847918&ei=vKd-YN6rHJD_mAGorriYDA&scisig=AAGBfm1x8hqCl6MSOMq2nNmzMdKU89-8aQ&nossl=1&oi=scholaralrt&hist=5D-EiWoAAAAJ:11619674075933241424:AAGBfm2WuunMRs1akVK27Fttx6PhkTPyfg&html=&folt=rel&fols=)

**Abstract:** Combining fuzzing techniques and reinforcement learning could be an important direction in software testing. However, there is a gap in support for experimentation in this field, as there are no open-source tools to let academia and industry to perform experiments easily. The purpose of this paper is to fill this gap by introducing a new framework, named RiverFuzzRL, on top of our already mature framework for AI-guided fuzzing, River. We provide out-of-the-box implementations for users to choose from or customize for their test target. The work presented here is performed on testing binaries and does not require access to the source code, but it can be easily adapted to other types of software testing as well. We also discuss the challenges faced, opportunities, and factors that are important for performance, as seen in the evaluation.



### Vulnerability Detection in SIoT Applications: A Fuzzing Method on their Binaries (IEEE Transactions on Network Science and Engineering 2020)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9259242)

**Abstract:** SIoT enables devices to communicate with each other automatically, which is not reliable when applications in SIoT are vulnerable. To improve the security of SIoT, different techniques have been employed so far, mainly to detect vulnerabilities in SIoT applications. Among the detection techniques, fuzzing is one of the most effective ones that can significantly improve the security of SIoT applications. However, the existing fuzzing methods have three problems. First of all, the schemes to instrument target binaries cause high memory overhead because they instrument at all edges to obtain the coverage information. Moreover, they introduce a severe problem called edge collision, i.e., two different edges are deemed the same during fuzzing. Thirdly, none of the existing fuzzers conduct fuzzing using path coverage because path coverage has high memory overhead. In this paper, we propose BECFuzz to resolve the above three problems. BECFuzz instruments at specific edges, and conducts fuzzing based on both edge coverage and path coverage, which greatly improves its effectiveness. We implement our BECFuzz based on two typical fuzzers which are widely recognised as baselines, AFL and AFLFast, and run experiments on 18 real-world programs. The results demonstrate that our method suppresses the state-of-art fuzzers in performance.


### Analysis of DTLS Implementations Using Protocol State Fuzzing (USENIX Security2020)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec20fall_fiterau-brostean_prepub.pdf)

**Abstract:** Recent years have witnessed an increasing number of protocols relying on UDP. Compared to TCP, UDP offers performance advantages such as simplicity and lower latency. This has motivated its adoption in Voice over IP, tunneling technologies, IoT, and novel Web protocols. To protect sensitive data exchange in these scenarios, the DTLS protocol has been developed as a cryptographic variation of TLS. DTLSâs main challenge is to support the stateless and unreliable transport of UDP. This has forced protocol designers to make choices that affect the complexity of DTLS, and to incorporate features that need not be addressed in the numerous TLS analyses.

We present the first comprehensive analysis of DTLS implementations using protocol state fuzzing. To that end, we extend TLS-Attacker, an open source framework for analyzing TLS implementations, with support for DTLS tailored to the stateless and unreliable nature of the underlying UDP layer. We build a framework for applying protocol state fuzzing on DTLS servers, and use it to learn state machine models for thirteen DTLS implementations. Analysis of the learned state models reveals four serious security vulnerabilities, including a full client authentication bypass in the latest JSSE version, as well as several functional bugs and non-conformance issues. It also uncovers considerable differences between the models, confirming the complexity of DTLS state machines.


### Frankenstein: Advanced Wireless Fuzzing to Exploit New Bluetooth Escalation Targets (USENIX Security2020)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2006.09809.pdf)

**Abstract:** Wireless communication standards and implementations have a troubled history regarding security. Since most implementations and firmwares are closed-source, fuzzing remains one of the main methods to uncover Remote Code Execution (RCE) vulnerabilities in deployed systems. Generic over-the-air fuzzing suffers from several shortcomings, such as constrained speed, limited repeatability, and restricted ability to debug. In this paper, we present Frankenstein, a fuzzing framework based on advanced firmware emulation, which addresses these shortcomings. Frankenstein brings firmware dumps "back to life", and provides fuzzed input to the chip's virtual modem. The speed-up of our new fuzzing method is sufficient to maintain interoperability with the attached operating system, hence triggering realistic full-stack behavior. We demonstrate the potential of Frankenstein by finding three zero-click vulnerabilities in the Broadcom and Cypress Bluetooth stack, which is used in most Apple devices, many Samsung smartphones, the Raspberry Pis, and many others.
Given RCE on a Bluetooth chip, attackers may escalate their privileges beyond the chip's boundary. We uncover a Wi-Fi/Bluetooth coexistence issue that crashes multiple operating system kernels and a design flaw in the Bluetooth 5.2 specification that allows link key extraction from the host. Turning off Bluetooth will not fully disable the chip, making it hard to defend against RCE attacks. Moreover, when testing our chip-based vulnerabilities on those devices, we find BlueFrag, a chip-independent Android RCE.



### A deep convolution generative adversarial networks based fuzzing framework for industry control protocols

* <img src="image/pdf_24px.png">[Paper](https://link.springer.com/content/pdf/10.1007/s10845-020-01584-z.pdf)

**Abstract:** A growing awareness is brought that the safety and security of industrial control systems cannot be dealt with in isolation, and the safety and security of industrial control protocols (ICPs) should be considered jointly. Fuzz testing (fuzzing) for the ICP is a common way to discover whether the ICP itself is designed and implemented with flaws and network security vulnerability. Traditional fuzzing methods promote the safety and security testing of ICPs, and many of them have practical applications. However, most traditional fuzzing methods rely heavily on the specification of ICPs, which makes the test process a costly, time-consuming, troublesome and boring task. And the task is hard to repeat if the specification does not exist. In this study, we propose a smart and automated protocol fuzzing methodology based on improved deep convolution generative adversarial network and give a series of performance metrics. An automated and intelligent fuzzing framework BLSTM-DCNNFuzz for application is designed. Several typical ICPs, including Modbus and EtherCAT, are applied to test the effectiveness and efficiency of our framework. Experiment results show that our methodology outperforms the existing ones like General Purpose Fuzzer and other deep learning based fuzzing methods in convenience, effectiveness, and efficiency.


### ICS Protocol Fuzzing: Coverage Guided Packet Crack and Generation (DAC 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/DAC20_ICSProtocol.pdf)

**Abstract:** Industrial Control System (ICS) protocols play an essential role in building communications among system components. Recently, many severe vulnerabilities, such as Stuxnet and DragonFly, exposed in ICS protocols have affected a wide distribution of devices. Therefore, it is of vital importance to ensure their correctness. However, the vulnerability detection efficiency of traditional techniques such as fuzzing is challenged by the complexity and diversity of the protocols.

In this paper, we propose to equip the traditional protocol fuzzing with coverage-guided packet crack and generation. We collect the coverage information during testing procedure, save those valuable packets that trigger new path coverage and crack them into pieces, based on which, we can construct higher quality new packets for further testing. For evaluation, we build Peach* on top of Peach, which is one of the most widely used protocol fuzzers, and conduct experiments on several ICS protocols such as Modbus and DNP3. Results show that, compared with the original Peach, Peach* achieves the same code coverage and bug detection numbers at the speed of 1.2X-25X. It also gains final increase with 8.35%-36.84% more paths within 24 hours, and has exposed 9 previously unknown vulnerabilities.


### AFLNET: A Greybox Fuzzer for Network Protocols (ICST 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICST20_AFLNet.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/aflnet/aflnet)

**Abstract:** Server fuzzing is difficult. Unlike simple command-line tools, servers feature a massive state space that can be traversed effectively only with well-defined sequences of input messages. Valid sequences are specified in a protocol. In this paper, we present AFLNET, the first grey-box fuzzer for protocol implementations. Unlike existing protocol fuzzers, AFLNET takes a mutational approach and uses state-feedback to guide the fuzzing process. AFLNET is seeded with a corpus of recorded message exchanges between the server and an actual client. No protocol specification or message grammars are required. AFLNET acts as a client and replays variations of the original sequence of messages sent to the server and retains those variations that were effective at increasing the coverage of the code or state space. To identify the server states that are exercised by a message sequence, AFLNET uses the serverâs response codes. From this feedback, AFLNET identifies progressive regions in the state space, and systematically steers towards such regions. The case studies with AFLNET on two popular protocol implementations demonstrate a substantial performance boost over the state-of-the-art. AFLNET discovered two new CVEs that are classified as critical (CVSS score CRITICAL 9.8).


### Finding Security Vulnerabilities in Network Protocol Implementations (Arxiv 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv20_Protocols.pdf)

**Abstract:** Implementations of network protocols are often prone to vulnerabilities caused by developersâ? mistakes when accessing memory regions and dealing with arithmetic operations. Finding practical approaches for checking the security of network protocol implementations has proven to be a challenging problem. The main reason is that the protocol software state-space is too large to be explored. Here we propose a novel verification approach that combines fuzzing with symbolic execution to verify intricate properties in network protocol implementations. We use fuzzing for an initial exploration of the network protocol, while symbolic execution explores both the program paths and protocol states, which were uncovered by fuzzing. From this combination, we automatically generate high-coverage test input packets for a network protocol implementation.We surveyed various approaches based on fuzzing and symbolic execution to understand how these techniques can be effectively combined and then choose a suitable tool to develop further our model on top of it. In our preliminary evaluation, we used ESBMC, Map2Check, and KLEE as software verifiers and SPIKE as fuzzer to check their suitability to verify our network protocol implementations. Our experimental results show that ESBMC can be further developed within our verification framework called FuSeBMC, to efficiently and effectively detect intricate security vulnerabilities in network protocol implementations.


### Smart seed selection-based effective black box fuzzing for IIoT protocol (2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/20_SmartSeedSelection.pdf)

**Abstract:** Connections of cyber-physical system (CPS) components are gradually increasing owing to the introduction of the Industrial Internet of Things (IIoT). IIoT vulnerability analysis has become a major issue because complex skillful cyber-attacks on CPS systems exploit their zero-day vulnerabilities. However, current white box techniques for vulnerability analysis are difficult to use in real heterogeneous environments, where devices supplied by various manufacturers and diverse firmware versions are used. Therefore, we herein propose a novel protocol fuzzing test technique that can be applied in a heterogeneous environment. As seed configuration can significantly influence the test result in a black box test, we update the seed pool using test cases that travel different program paths compared to the seed. The input, output, and Delta times are used to determine if a new program area has been searched in the black box environment. We experimentally verified the effectiveness of the proposed.


### Fwâfuzz: A code coverageâguided fuzzing framework for network protocols on firmware (2020)

* <img src="image/pdf_24px.png">[Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.5756)

**Abstract:** Fuzzing is an effective approach to detect software vulnerabilities utilizing changeable generated inputs. However, fuzzing the network protocol on the firmware of IoT devices is limited by inefficiency of test case generation, crossâarchitecture instrumentation, and fault detection. In this article, we propose the Fwâfuzz, a coverageâguided and crossplatform framework for fuzzing network services running in the context of firmware on embedded architectures, which can generate more valuable test cases by introspecting program runtime information and using a genetic algorithm model. Specifically, we propose novel dynamic instrumentation in Fwâfuzz to collect the running state of the firmware program. Then Fwâfuzz adopts a genetic algorithm model to guide the generation of inputs with high code coverage. We fully implement the prototype system of Fwâfuzz and conduct evaluations on network service programs of various architectures in MIPS, ARM, and PPC. By comparing with the protocol fuzzers Boofuzz and Peach in metrics of edge coverage, our prototype system achieves an average growth of 33.7% and 38.4%, respectively. We further verify six known vulnerabilities and discover 5 0âday vulnerabilities with the Fwâfuzz, which prove the validity and utility of our framework. The overhead of our system expressed as an additional 5% of memory growth.


### BaseSAFE: Baseband SAnitized Fuzzing through Emulation (WiSec 2020)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2005.07797.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/fgsect/BaseSAFE)

**Abstract:** Rogue base stations are an effective attack vector. Cellular basebands represent a critical part of the smartphoneâs security: they parse large amounts of data even before authentication. They can, therefore, grant an attacker a very stealthy way to gather information about calls placed and even to escalate to the main operating system, over-the-air. In this paper, we discuss a novel cellular fuzzing framework that aims to help security researchers find critical bugs in cellular basebands and similar embedded systems. BaseSAFE allows partial rehosting of cellular basebands for fast instrumented fuzzing off-device, even for closed-source firmware blobs. BaseSAFEâs sanitizing drop-in allocator, enables spotting heap-based buffer-overflows quickly. Using our proof-of-concept harness, we fuzzed various parsers of the Nucleus RTOS-based MediaTek cellular baseband that are accessible from rogue base stations. The emulator instrumentation is highly optimized, reaching hundreds of executions per second on each core for our complex test case, around 15k test-cases per second in total. Furthermore, we discuss attack vectors for baseband modems. To the best of our knowledge, this is the first use of emulation-based fuzzing for security testing of commercial cellular basebands. Most of the tooling and approaches of BaseSAFE are also applicable for other low-level kernels and firmware. Using BaseSAFE, we were able to find memory corruptions including heap out-of-bounds writes using our proof-of-concept fuzzing harness in the MediaTek cellular baseband. BaseSAFE, the harness, and a large collection of LTE signaling message test cases will be released open-source upon publication of this paper.

### Poster: Fuzzing IoT Firmware via Multi-stage Message Generation (CCS 2019)

* <img src="image/pdf_24px.png">[Paper](https://wpengfei.github.io/iothunter-final.pdf)

**Abstract:** In this work, we present IoTHunter, the first grey-box fuzzer for fuzzing stateful protocols in IoT firmware. IoTHunter addresses the state scheduling problem based on a multi-stage message generation mechanism on runtime monitoring of IoT firmware. We evaluate IoTHunter with a set of real-world programs, and the result shows that IoTHunter outperforms black-box fuzzer boofuzz, which has a 2.2x, 2.0x, and 2.5x increase for function coverage, block coverage, and edge coverage, respectively. IoTHunter also found five new vulnerabilities in the firmware of home router Mikrotik, which have been reported to the vendor.


### SeqFuzzer: An Industrial Protocol Fuzzing Framework in Deep Learning Perspective (ICST 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICST19_SeqFuzzer.pdf)

**Abstract:** Industrial networks are the cornerstone of modern industrial control systems. Performing security checks of industrial communication processes help detect unknown risks and vulnerabilities. Fuzz testing is a widely used method for performing security checks that takes advantage of automation. However, there is a big challenge to carry out security checks on the industrial networks due to the increasing variety and complexity of industrial communication protocols. In this case, existing approaches usually take a long time to model the protocol for generating test cases, which is labor-intensive and timeconsuming. This becomes even worse when the target protocol is stateful. To help in addressing this problem, we employed a deep learning model to learn the structures of protocol frames and deal with the temporal features of stateful protocols. We propose a fuzzing framework named SeqFuzzer which automatically learns the protocol frame structures from communication traffic and generates fake but plausible messages as test cases. For proving the usability of our approach, we applied SeqFuzzer to widelyused Ethernet for Control Automation Technology (EtherCAT) devices and successfully detected several security vulnerabilities


###  SPFuzz: A Hierarchical Scheduling Framework for Stateful Network Protocol Fuzzing (IEEE Access 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/.pdf)

**Abstract:** In recent years, the fuzzing technology is widely used to detect the software vulnerabilities owing to the coverage improvement in the target program and the easiness of use. However, it is less efficient to fuzz the stateful protocols due to the difficulties like maintaining states and dependencies of messages. To address these challenges, we present SPFuzz, a framework for building flexible, coverage guided stateful protocol fuzzing. We define a language in SPFuzz to describe the protocol specifications, protocol states transitions and dependencies for generating valuable test cases, maintaining correct messages in session states and handling protocol dependencies by updating message data in time. The SPFuzz adopts a three-level mutation strategy, namely head, content, and sequence mutation strategy to drive the fuzzing process to cover more paths, in conjunction with the method to randomly assign weights to messages and strategies. We use the following metrics to evaluate the performance of SPFuzz and other frameworks upon three protocol implementations, i.e., Proftpd, Oftpd, and OpenSSL, which are three-granularity coverages specifically function, basic block, and edge. In experiments, the SPFuzz framework outperforms the existing
stateful protocol fuzzing tool Boofuzz by an average of 69.12% in three granularities coverage tests. This demonstrates that the SPFuzz has the ability to explore more and deeper paths of the target program. We further triggered CVE-2015-0291 in OpenSSL 1.0.2 with the SPFuzz, which proves the validity and
utility of our framework.


### HFuzz: Towards automatic fuzzing testing of NB-IoT core network protocols implementations (FGCS 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/FGCS19_HFuzz.pdf)

**Abstract:** Narrowband Internet of Things (NB-loT) is widely deployed in the cellular network of operators, yet implementations of its core network protocols are suffering from bugs. Due to the complexity of the frame structure of NB-IoT core network protocols, testing the protocols in this field is notoriously difficult. In this paper, we propose a novel fuzzing framework, named HFuzz, to generate a great many high-quality test inputs automatically. HFuzz is an automatic hierarchy-aware fuzzing framework and can allocate computing resources efficiently. We put forward the concept of Message Structure Tree to transform the seed file and generate mutated data of the tested protocols and optimize the resource allocation for each hierarchy of the transformed structure by a novel scheduling algorithm. Therefore HFuzz can get a balance between breadth and depth in finding new paths. Compared to traditional fuzzing tools, HFuzz can easily pass the early verification and induce a better coverage of the target implementations by taking full advantage of format information of NB-IoT core network protocols. Our framework applies to various protocols, and we evaluate the performance of HFuzz on GPRS Tunnelling Protocol version 2(GTPv2) in this paper and conduct experiments with two protocol implementations, Open Air Interface (OAI) and B*(a development system). The experimental results show HFuzz yields higher coverage than American Fuzzy Lop (AFL) and Peach, and we further find a real implementation bug in OAI.


### FIRM-AFL: High-Throughput Greybox Fuzzing of IoT Firmware via Augmented Process Emulation (USENIX Security2019)

* <img src="image/youtube.png">[Video](https://youtu.be/0ychOUFoDbM)

* <img src="image/csdn_24px.png">[Reading Note](https://blog.csdn.net/weixin_39945679/article/details/111370554)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX19_FirmAFL.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/zyw-200/FirmAFL)

**Abstract:** Cyber attacks against IoT devices are a severe threat. These attacks exploit software vulnerabilities in IoT firmware. Fuzzing is an effective software testing technique for vulnerability discovery. In this work, we present FIRM-AFL, the first high-throughput grey box fuzzer for IoT firmware. FIRMAFL addresses two fundamental problems in IoT fuzzing. First, it addresses compatibility issues by enabling fuzzing for POSIX-compatible firmware that can be emulated in a system emulator. Second, it addresses the performance bottleneck caused by system-mode emulation with a novel technique called augmented process emulation. By combining system mode emulation and user-mode emulation in a novel way, augmented process emulation provides high compatibility as system-mode emulation and high throughput as user-mode emulation. Our evaluation results show that (1) FIRM-AFL is fully functional and capable of finding real-world vulnerabilities in IoT programs; (2) the throughput of FIRM-AFL is on average 8.2 times higher than system-mode emulation based fuzzing; and (3) FIRM-AFL is able to find 1-day vulnerabilities much faster than system-mode emulation based fuzzing, and is able to find 0-day vulnerabilities.

### Exploring Effective Fuzzing Strategies to Analyze Communication Protocols (FEAST 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/FEAST19_Exploring.pdf)

**Abstract:** In recent years, coverage-based greybox fuzzing has become popular forvulnerability detection due to its simplicity and efficiency. However, it is less powerful when applied directly to protocol fuzzing due to the unique challenges involved in fuzzing communication protocols. In particular, the communication among multiple ends contains more than one packet, which are not necessarily dependent upon each other, i.e., fuzzing single (usually the first) packet can only achieve extremely limited code coverage. In this paper, we study such challenges and demonstrate the limitation of current non-stateful greybox fuzzer. In order to achieve higher code coverage, we design stateful protocol fuzzing strategies for communication protocols to explore the code related to different protocol states. Our approach contains a state switching engine, together with a multi-state forkserver to consistently and flexibly fuzz different states of an compiler-instrumented protocol program. Our experimental results on OpenSSL show that our approach achieves an improvement of 73% more code coverage and 2Ã unique crashes when comparing against fuzzing the first packet during a protocol handshake.


### Leveraging Textual Specifications for Grammar-Based Fuzzing of Network Protocols (AAAI 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/AAAi19_Leveraging.pdf)

**Abstract:** Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.


### MTF-Storm: a high performance fuzzer for Modbus-TCP (ETFA 2018)

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1109/ETFA.2018.8502600)

* <img src="image/github_24px.png">[Code](https://github.com/ntinosk-mtf/etfa2018)

**Abstract:**  MTF-Storm is a highly effective fuzzer for industrial systems employing Modbus/TCP connectivity. It achieves high fault coverage, while offering high performance and quick testing of the System-Under-Test (SUT). Analogously to its predecessor MTF, MTF-Storm operates in 3 phases: a) reconnaissance b) fuzz testing and failure detection. Reconnaissance identifies the memory organization of the SUT and the supported functionality, enabling selection and synthesis of fuzz testing sequences that are effective for the specific SUT. MTF-Storm develops its test sequences systematically, starting  with single field tests and proceeding with combined field tests, adopting techniques for automated combinatorial software testing and reducing the test space through partitioning field value ranges. MTF-Storm has been used to evaluate 9 different Modbus/TCP implementations and has identified issues with all of them, ranging from out-of-spec responses to successful denial-of-service attacks and crashes.


### Advancing Protocol Fuzzing for Industrial Automation and Control Systems (ICISSP 2018)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICISSP18_Advancing.pdf)

**Abstract:** Testing for security vulnerabilities is playing an important role in the changing domain of industrial automation and control systems. These systems are increasingly connected to each other via networking technology and are faced with new cyber threats. To improve the security properties of such systems, their robustness must be ensured. Security testing frameworks aim at enabling the assurance of robustness even at the time of development and can play a key role in bringing security into the industrial domain.

Fuzzing describes a technique to discover vulnerabilities in technical systems and is best known from its usage in IT security testing. It uses randomly altered data to provoke unexpected behaviour and can be used in combination with regular unit testing. Combined with the power of fuzzing, the effectiveness of security testing frameworks can be increased. 

In this work, different fuzzing tools were evaluated for their properties and then compared with the requirements for an application in the industrial domain. As no fuzzer was fully satisfying these requirements, a new fuzzer, combining the strength of different others, was designed and implemented, and then evaluated. The evaluation includes a real-world application where multiple vulnerabilities in industrial automation components could be identified.


### IoTFuzzer: Discovering Memory Corruptions in IoT Through App-based Fuzzing (NDSS 2018)

* <img src="image/GoSSIP_note.jpg">[Reading Note](https://securitygossip.com/blog/2018/06/15/iotfuzzer-discovering-memory-corruptions-in-iot-through-app-based-fuzzing/)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS18_IoTfuzzer.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/NDSS18_IoTFuzzer_Slides.pdf)

**Abstract:** With more IoT devices entering the consumer market, it becomes imperative to detect their security vulnerabilities before an attacker does. Existing binary analysis based approaches only work on firmware, which is less accessible except for those equipped with special tools for extracting the code from the device. To address this challenge in IoT security analysis, we present in this paper a novel automatic fuzzing framework, called IOTFUZZER, which aims at finding memory corruption vulnerabilities in IoT devices without access to their firmware images. The key idea is based upon the observation that most IoT devices are controlled through their official mobile apps, and such an app often contains rich information about the protocol it uses to communicate with its device. Therefore, by identifying and reusing program-specific logic (e.g., encryption) to mutate the test case (particularly message fields), we are able to effectively probe IoT targets without relying on any knowledge about its protocol specifications. In our research, we implemented IOTFUZZER and evaluated 17 real-world IoT devices running on different protocols, and our approach successfully identified 15 memory corruption vulnerabilities (including 8 previously unknown ones).


### Bbuzz: A Bit-aware Fuzzing Framework for Network Protocol Systematic Reverse Engineering and Analysis (MCC 2017)

* <img src="image/pdf_24px.png">[Paper](./Paper/MCC17-Binfuzz.pdf)

**Abstract:** Fuzzing is a critical part of secure software development life-cycle, for finding vulnerabilities, developing exploits, and reverse engineering. This relies on appropriate approaches, tools and frameworks. File and protocol fuzzing is well covered, multiple approaches and implementations exist. Unfortunately, assessed tools do not posses the required capabilities for working with protocols, where constructing bit groups are not byte aligned. In this paper, a systematic approach is proposed and tool prototype developed for the cyber red teaming purposes. In a case study, the developed Bbuzz tool is used to reverse engineer a proprietary NATO Link-1 network protocol allowing to inject rogue airplane tracks into air operations command and control system.


### Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine (2016)

* <img src="image/pdf_24px.png">[Paper](./Paper/2016_Protocol.pdf)

**Abstract:** To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.


### Protocol State Fuzzing of TLS Implementations (USENIX Security2015)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=eb0ec80133498dd0b2e073a5174c5f0d&sub=F972E69029854C398925A5F8D5E91484)

* <img src="image/pdf_24px.png">[Slides](./Paper/USENIX15_Protocol_Slides.pdf)

* <img src="image/ppt_24px.png">[Paper](./Paper/USENIX15_Protocol.pdf)

**Abstract:** We describe a largely automated and systematic analysis of TLS implementations by what we call âprotocol state fuzzingâ?: we use state machine learning to infer state machines from protocol implementations, using only blackbox testing, and then inspect the inferred state machines to look for spurious behaviour which might be an indication of flaws in the program logic. For detecting the presence of spurious behaviour the approach is almost fully automatic: we automatically obtain state machines and any spurious behaviour is then trivial to see. Detecting whether the spurious behaviour introduces exploitable security weaknesses does require manual investigation. Still, we take the point of view that any spurious functionality in a security protocol implementation is dangerous and should be removed.

We analysed both server- and client-side implementations with a test harness that supports several key exchange algorithms and the option of client certificate authentication. We show that this approach can catch an interesting class of implementation flaws that is apparently common in security protocol implementations: in three of the TLS implementations analysed new security flaws were found (in GnuTLS, the Java Secure Socket Extension, and OpenSSL). This shows that protocol state fuzzing is a useful technique to systematically analyse security protocol implementations. As our analysis of different TLS implementations resulted in different and unique state machines for each one, the technique can also be used for fingerprinting TLS implementations.


### A Modbus-TCP Fuzzer for testing internetworked industrial systems (ETFA 2015)

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1109/ETFA.2015.7301400)

* <img src="image/github_24px.png">[Code](https://github.com/artemiosv/etfa2015)

**Abstract:** Modbus/TCP is a network protocol for industrial communications encapsulated in TCP/IP network packets. There is an increasing need to test existing Modbus protocol implementations for security vulnerabilities, as devices become accessible even from the Internet. Fuzz testing can be used to discover implementation bugs in a fast and economical way. We present the design and implementation of MTF, a Modbus/TCP Fuzzer. The MTF incorporates a reconnaissance phase in the testing procedure so as to assist mapping the capabilities of the tested device and to adjust the attack vectors towards a more guided and informed testing rather than plain random testing. The MTF was used to test eight implementations of the Modbus protocol and revealed bugs and vulnerabilities that crash the execution, effectively resulting in denial of service attacks using only a few network packets.


### PULSAR: Stateful Black-Box Fuzzing of Proprietary Network Protocols (Springer, Cham, 2015)

* <img src="image/pdf_24px.png">[Paper](./Paper/Springer15_PULSAR.pdf)

**Abstract:** The security of network services and their protocols critically depends on minimizing their attack surface. A single flaw in an implementation can suffice to compromise a service and expose sensitive data to an attacker. The discovery of vulnerabilities in protocol implementations, however, is a challenging task: While for standard protocols this process can be conducted with regular techniques for auditing, the situation becomes difficult for proprietary protocols if neither the program code nor the specification of the protocol are easily accessible. As a result, vulnerabilities in closed-source implementations can often remain undiscovered for a longer period of time. In this paper, we present PULSAR, a method for stateful black-box fuzzing of proprietary network protocols. Our method combines concepts from fuzz testing with techniques for automatic protocol reverse engineering and simulation. It proceeds by observing the traffic of a proprietary protocol and inferring a generative model for message formats and protocol states that can not only analyze but also simulate communication. During fuzzing this simulation can effectively explore the protocol state space and thereby enables uncovering vulnerabilities deep inside the protocol implementation. We demonstrate the efficacy of PULSAR in two case studies, where it identifies known as well as unknown vulnerabilities.



### SECFUZZ: Fuzz-testing Security Protocols (AST 2012)

* <img src="image/pdf_24px.png">[Paper](./Paper/AST12_SECFUZZ.pdf)

**Abstract:** We propose a light-weight, yet effective, technique for fuzz-testing security protocols. Our technique is modular, it exercises (stateful) protocol implementations in depth, and handles encrypted traffic. We use a concrete implementation of the protocol to generate valid inputs, and mutate the inputs using a set of fuzz operators. A dynamic memory analysis tool monitors the execution as an oracle to detect he vulnerabilities exposed by fuzz-testing. We provide the fuzzer with the necessary keys and cryptographic algorithms in order to properly mutate encrypted messages. We present a case study on two widely used, mature implementations of the Internet Key Exchange (IKE) protocol and report on two
new vulnerabilities discovered by our fuzz-testing tool. We also compare the effectiveness of our technique to two existing model-based fuzz-testing tools for IKE.


### Extension of SPIKE for Encrypted Protocol Fuzzing (2011)

* <img src="image/pdf_24px.png">[Paper](./Paper/IEEE2011_protocol.pdf)

**Abstract:** A fuzzer is a program that attempts to find security vulnerabilities in an application by sending random or semi-random input. Fuzzers
have been widely used to find vulnerabilities in protocol implementations. The implementations may conform to the design of the protocol, but most of the times some glitches might remain. As a result vulnerabilities might remain unnoticed. Consequently, different implementations of the same protocol may be vulnerable to different kind of attacks. Fuzzers help us discover such implementation flaws. Among the currently available and popular ones, SPIKE is one recognized open-source fuzzing framework. However, SPIKE has a limitation of fuzzing only non-encrypted protocols. This paper presents the extension of SPIKE, called ESPIKE, for fuzzing of encrypted protocols. ESPIKE will facilitate testing implementations of
SSL encrypted protocols. As a proof of concept for efficiency of ESPIKE we demonstrate its usage on sftp and https protocol.


### AutoFuzz: Automated Network Protocol Fuzzing Framework (IJCSNS 2010)

* <img src="image/pdf_24px.png">[Paper](./Paper/IJCSNS10_Autofuzz.pdf)

**Abstract:** Assessing software security involves steps such as code review, risk analysis, penetration testing and fuzzing. During the fuzzing phase, the testerâs goal is to find flaws in software by sending unexpected input to the target application and monitoring its behavior. In this paper we introduce the AutoFuzz [1] - extendable, open source framework used for testing network protocol implementations. AutoFuzz is a âsmartâ?, man-in-the-middle, semi deterministic network protocol fuzzing framework. AutoFuzz learns a protocol implementation by constructing a Finite State Automaton (FSA) which captures the observed communications between a client and a server [5]. In addition, AutoFuzz learns individual message syntax, including fields and probable types, by applying the bioinformatics techniques of [2]. Finally, AutoFuzz can fuzz client or server protocol implementations by intelligently modifying the communication sessions between them using the FSA as a guide. AutoFuzz was applied to a variety of File Transfer Protocol (FTP) server implementations, confirming old and discovering new vulnerabilities.



# SMT Fuzzing

### Alt-Ergo-Fuzz: A fuzzer for the Alt-Ergo SMT solver (JFLA 2022)

* <img src="image/pdf_24px.png">[Paper](https://hal.inria.fr/hal-03626861/)

* <img src="image/github_24px.png">[Code](https://github.com/hra687261/alt-ergo-fuzz)

Alt-Ergo is an open source Satisfiability Modulo Theories (SMT) solver programmed in OCaml. It was designed for program verification and itâs used as a back end by other software verification tools such as Frama-C, SPARK, Why3, Atelier-B and Caveat, the reliability of which depends on the soundness of Alt-Ergoâs answers and the absence of bugs in it.
Fuzzing is an efficient technique to test programs and find bugs. It works by quickly and automatically generating input data with which to test the software. American Fuzzy Lop (AFL) is one of the most well-known and most used fuzzers in both academia and the industry. It has managed to find many bugs in various programs thanks to its grey box fuzzing technique that uses genetic algorithms and program instrumentation to generate test data that maximizes code and execution path coverage in the targeted software.
In this paper we present Alt-Ergo-Fuzz, a fuzzer for Alt-Ergo that we developed with the aim of finding faults and unsoundness bugs to solve and improve its reliability. By using AFL as a back end, the Crowbar OCaml library for test case generation and the CVC5 SMT solver as a reference solver of which the answers will be used to determine whether or not Alt-Ergoâs answers are correct, we managed to develop Alt-Ergo-Fuzz, which even as a work in progress and in only twenty days of testing managed to find four never found before bugs in Alt-Ergo.

### BanditFuzz: Fuzzing SMT Solvers with Multi-agent Reinforcement Learning (FM 2021)

* <img src="image/pdf_24px.png">[Paper](https://link.springer.com/chapter/10.1007/978-3-030-90870-6_6)

**Abstract:** We present BanditFuzz, a multi-agent reinforcement learning (RL) guided performance fuzzer for state-of-the-art Satisfiability Modulo Theories (SMT) solvers. BanditFuzz constructs inputs that expose performance issues in a set of target solvers relative to a set of reference solvers, and is the first performance fuzzer that supports the entirety of the theories in the SMT-LIB initiative. Another useful feature of BanditFuzz is that users can specify the size of inputs they want, thus enabling developers to construct very small inputs that zero-in on a performance problem in their SMT solver relative to other competitive solvers. We evaluate BanditFuzz across 52 logics from SMT-COMP â?20 targeting competition-winning solvers against runner-ups. We baseline BanditFuzz against random fuzzing and a single agent algorithm and observe a significant improvement, with up to a 82.6% improvement in the margin of PAR-2 scores across baselines on their respective benchmarks. Furthermore, we reached out to developers and contributors of the CVC4, Z3, and Bitwuzla solvers and provide case studies of how BanditFuzz was able to expose surprising performance deficiencies in each of these tools.


### Skeletal Approximation Enumeration for SMT Solver Testing (FSE 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/abs/10.1145/3468264.3468540)

**Abstract:** Ensuring the equality of SMT solvers is critical due to its broad spectrum of applications in academia and industry, such as symbolic execution and program verification. Existing approaches to testing SMT solvers are either too costly or find difficulties generalizing to different solvers and theories, due to the test oracle problem. To complement existing approaches and overcome their weaknesses, this paper introduces skeletal approximation enumeration (SAE), a novel lightweight and general testing technique for all first-order theories. To demonstrate its practical utility, we have applied the SAE technique to test Z3 and CVC4, two comprehensively tested, state-of-the-art SMT solvers. By the time of writing, our approach had found 71 confirmed bugs in Z3 and CVC4,55 of which had already been fixed.


### Fuzzing SMT Solvers via Two-Dimensional Input Space Exploration (ISSTA 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/10.1145/3460319.3464803)

**Abstract:** Satisfiability Modulo Theories (SMT) solvers serve as the core engine of many techniques, such as symbolic execution. Therefore, ensuring the robustness and correctness of SMT solvers is critical. While fuzzing is an efficient and effective method for validating the quality of SMT solvers, we observe that prior fuzzing work only focused on generating various first-order formulas as the inputs but neglected the algorithmic configuration space of an SMT solver, which leads to under-reporting many deeply-hidden bugs. In this paper, we present Falcon, a fuzzing technique that explores both the formula space and the configuration space. Combining the two spaces significantly enlarges the search space and makes it challenging to detect bugs efficiently. We solve this problem by utilizing the correlations between the two spaces to reduce the search space, and introducing an adaptive mutation strategy to boost the search efficiency. During six months of extensive testing, Falcon finds 518 confirmed bugs in CVC4 and Z3, two state-of-the-art SMT solvers, 469 of which have already been fixed. Compared to two state-of-the-art fuzzers, Falcon detects 38 and 44 more bugs and improves the coverage by a large margin in 24 hours of testing.


### Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing (FSE 2020)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2004.05934.pdf)

**Abstract:** Formal methods use SMT solvers extensively for deciding formula satisfiability, for instance, in software verification, systematic test generation, and program synthesis. However, due to their complex implementations, solvers may contain critical bugs that lead to unsound results. Given the wide applicability of solvers in software reliability, relying on such unsound results may have detrimental consequences. In this paper, we present STORM, a novel blackbox mutational fuzzing technique for detecting critical bugs in SMT solvers. We run our fuzzer on seven mature solvers and find 29 previously unknown critical bugs. STORM is already being used in testing new features of popular solvers before deployment.


### On the Unusual Effectiveness of Type-aware Mutations for Testing SMT Solvers

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2004.08799.pdf)

**Abstract:** We propose type-aware operator mutation, a simple, but unusually effective approach for testing SMT solvers.
The key idea is to mutate operators of conforming types within the seed formulas to generate well-typed mutant formulas. These mutant formulas are then used as the test cases for SMT solvers. We realized typeaware operator mutation within the OpFuzz tool and used it to stress-test Z3 and CVC4, two state-of-the-art SMT solvers. Type-aware operator mutations are unusually effective: During nine months of extensive testing with OpFuzz, we reported 909 bugs in Z3 and CVC4,1 out of which 632 bugs were confirmed and 531 of the confirmed bugs were fixed by the developers. The detected bugs are highly diverse â? we found bugs of many different types (soundness bugs, invalid model bugs, crashes, etc.), logics and solver configurations. We have further conducted an in-depth study on the bugs found by OpFuzz. The study results show that the bugs found by OpFuzz are of high quality. Many of them affect core components of the SMT solversâ? codebases, and some required major changes for the developers to fix. Among the 909 bugs found by OpFuzz, 130 were soundness bugs, the most critical bugs in SMT solvers, and 501 were in the default modes of the solvers. Notably, OpFuzz found 16 critical soundness bugs in CVC4, which has proved to be a very stable SMT solver


### BanditFuzz: Fuzzing SMT Solvers with Reinforcement Learning (2020)

* <img src="image/pdf_24px.png">[Paper](https://uwspace.uwaterloo.ca/handle/10012/15753)

Satisfiability Modulo Theories (SMT) solvers are fundamental tools in the broad context of software engineering and security research. If SMT solvers are to continue to have an impact, it is imperative we develop efficient and systematic testing methods for them. To this end, we present a reinforcement learning driven fuzzing system BanditFuzz that zeroes in on the grammatical constructs of well-formed solver inputs that are the root cause of performance or correctness issues in solvers-under-test. To the best of our knowledge, BanditFuzz is the first machine-learning based fuzzer for SMT solvers. BanditFuzz takes as input a grammar G describing the well-formed inputs to a set of distinct solvers (say, P_1 and P_2) that implement the same specification and a fuzzing objective (e.g., maximize the relative performance difference between P_1 and P_2), and outputs a ranked list of grammatical constructs that are likely to maximize performance differences between P_1 and P_2 or are root causes of errors in these solvers. Typically, mutation fuzzing is implemented as a set of random mutations applied to a given input. By contrast, the key innovation behind BanditFuzz is the modeling of a grammar-preserving fuzzing mutator as a reinforcement learning (RL) agent that, via blackbox interactions with programs-under-test, learns which grammatical constructs are most likely the cause of an error or performance issue. Using BanditFuzz, we discovered 1700 syntactically unique inputs resulting in inconsistent answers across state-of-the-art SMT solvers Z3, CVC4, Colibri, MathSAT, and Z3str3 over the floating-point and string SMT theories. Further, using BanditFuzz, we constructed two benchmark suites (with 400 floating-point and 110 string instances) that expose performance issues in all considered solvers. We also performed a comparison of BanditFuzz against random, mutation, and evolutionary fuzzing methods. We observed up to a 31% improvement in performance fuzzing and up to 81% improvement in the number of bugs found by BanditFuzz relative to these other methods for the same amount of time provided to all methods.


### Validating SMT Solvers via Semantic Fusion (PLDI 2020)

* <img src="image/youtube.png">[Video](https://youtu.be/hEWpbO5yXPw)

* <img src="image/pdf_24px.png">[Paper](https://wintered.github.io/papers/winterer-zhang-su-pldi20.pdf)

* <img src="image/pdf_24px.png">[Slides](https://testsmt.github.io/slides/pldi20-semantic-fusion.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/testsmt/yinyang)


**Abstract:** We introduce Semantic Fusion, a general, effective methodology for validating Satisfiability Modulo Theory (SMT) solvers. Our key idea is to fuse two existing equisatisfiable (i.e., both satisfiable or unsatisfiable) formulas into a new formula that combines the structures of its ancestors in a novel manner and preserves the satisfiability by construction. This fused formula is then used for validating SMT solvers. We realized Semantic Fusion as YinYang, a practical SMT solver testing tool. During four months of extensive testing, YinYang has found 45 confirmed, unique bugs in the default arithmetic and string solvers of Z3 and CVC4, the two state-of-the-art SMT solvers. Among these, 41 have already been fixed by the developers. The majority (29/45) of these bugs expose critical soundness issues. Our bug reports and testing effort have been well-appreciated by SMT solver developers.


### Automatically Testing String Solvers (ICSE 2020)

* <img src="image/pdf_24px.png">[Paper](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/397450/paper.pdf?sequence=1&isAllowed=y)
  

**Abstract:** SMT solvers are at the basis of many applications, such as program verification, program synthesis, and test case generation. For all these applications to provide reliable results, SMT solvers must answer queries correctly. However, since they are complex, highly-optimized software systems, ensuring their correctness is challenging. In particular, state-of-the-art testing techniques do not reliably detect when an SMT solver is unsound.

In this paper, we present an automatic approach for generating test cases that reveal soundness errors in the implementations of string solvers, as well as potential completeness and performance issues. We synthesize input formulas that are satisfiable or unsatisfiable by construction and use this ground truth as test oracle. We automatically apply satisfiability-preserving transformations to generate increasingly-complex formulas, which allows us to detect many errors with simple inputs and, thus, facilitates debugging.

The experimental evaluation shows that our technique effectively reveals bugs in the implementation of widely-used SMT solvers and applies also to other types of solvers, such as automata-based solvers. We focus on strings here, but our approach carries over to other theories and their combinations.


### StringFuzz: A fuzzer for string solvers (CAV 2018)

* <img src="image/pdf_24px.png">[Paper](https://uwspace.uwaterloo.ca/bitstream/handle/10012/13564/Blotsky_Dmitry.pdf?sequence=6&isAllowed=y)
  

**Abstract:** In this paper, we introduce StringFuzz: a modular SMT-LIB problem instance transformer and generator for string solvers. We supply a repository of instances generated by StringFuzz in SMT-LIB 2.0/2.5 format. We systematically compare Z3str3, CVC4, Z3str2, and Norn on groups of such instances, and identify those that are particularly challenging for some solvers. We briefly explain our observations and show how StringFuzz helped discover causes of performance degradations in Z3str3.



# Anti Fuzzing

### Antifuzz: impeding fuzzing audits of binary executables (USENIX Security2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX19_Antifuzz.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/RUB-SysSec/antifuzz)

**Abstract:** A general defense strategy in computer security is to increase the cost of successful attacks in both computational resources as well as human time. In the area of binary security, this is commonly done by using obfuscation methods to hinder reverse engineering and the search for software vulnerabilities. However, recent trends in automated bug finding changed the modus operandi. Nowadays it is very common for bugs to be found by various fuzzing tools. Due to ever-increasing amounts of automation and research on better fuzzing strategies, large-scale, dragnet-style fuzzing of many hundreds of targets becomes viable. As we show, current obfuscation techniques are aimed at increasing the cost of human understanding and do little to slow down fuzzing. In this paper, we introduce several techniques to protect a binary executable against an analysis with automated bug finding approaches that are based on fuzzing, symbolic/concolic execution, and taint-assisted fuzzing (commonly known as hybrid fuzzing). More specifically, we perform a systematic analysis of the fundamental assumptions of bug finding tools and develop general countermeasures for each assumption. Note that these techniques are not designed to target specific implementations of fuzzing tools, but address general assumptions that bug finding tools necessarily depend on. Our evaluation demonstrates that these techniques effectively impede fuzzing audits, while introducing a negligible performance overhead. Just as obfuscation techniques increase the amount of human labor needed to find a vulnerability, our techniques render automated fuzzing-based approaches futile.


### FUZZIFICATION: Anti-Fuzzing Technique (USENIX Security2019)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2019/11/08/fuzzification-anti-fuzzing-techniques/)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX19_FUZZIFICATION.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/sslab-gatech/fuzzification)

**Abstract:** Fuzzing is a software testing technique that quickly and automatically explores the input space of a program without knowing its internals. Therefore, developers commonly use fuzzing as part of test integration throughout the software development process. Unfortunately, it also means that such a blackbox and the automatic natures of fuzzing are appealing to adversaries who are looking for zero-day vulnerabilities.
To solve this problem, we propose a new mitigation approach, called Fuzzification , that helps developers protect the released, binary-only software from attackers who are capable of applying state-of-the-art fuzzing techniques. Given a performance budget, this approach aims to hinder the fuzzing process from adversaries as much as possible. We propose three Fuzzification techniques: 1) SpeedBump, which amplifies the slowdown in normal executions by hundreds of times to the fuzzed execution, 2) BranchTrap, interfering with feedback logic by hiding paths and polluting coverage maps, and 3) AntiHybrid, hindering taint-analysis and symbolic execution. Each technique is designed with best-effort, defensive measures that attempt to hinder adversaries from bypassing Fuzzification.
Our evaluation on popular fuzzers and real-world applications shows that Fuzzification effectively reduces the number of discovered paths by 70.3% and decreases the number of identified crashes by 93.0% from real-world binaries, and decreases the number of detected bugs by 67.5% from LAVA-M dataset while under user-specified overheads for common workloads. We discuss the robustness of Fuzzification techniques against adversarial analysis techniques. We open-source our Fuzzification system to foster future research.



# Kernel Fuzzing

### Demystifying the Dependency Challenge in Kernel Fuzzing (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/89/Demystifying-the-Dependency-Challenge-in-Kernel-Fuzzing)

**Abstract:** Fuzz testing operating system kernels remains a daunting task to date. One known challenge is that much of the kernel code is locked under specific kernel states and current kernel fuzzers are not effective in exploring such an enormous state space. We refer to this problem as the dependency challenge. Though there are some efforts trying to address the dependency challenge, the prevalence and categorization of dependencies have never been studied. Most prior work simply attempted to recover dependencies opportunistically whenever they are relatively easy to recognize. In this paper, we undertake a substantial measurement study to systematically understand the real challenge behind dependencies. To our surprise, we show that even for well-fuzzed kernel modules, unresolved dependencies still account for 59% - 88% of the uncovered branches. Furthermore, we show that the dependency challenge is only a symptom rather than the root cause of failing to achieve more coverage. By distilling and summarizing our findings, we believe the research provides valuable guidance to future research in kernel fuzzing. Finally, we propose a number of novel research directions directly based on the insights gained from the measurement study.


### Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and the Emulators (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://www-users.cse.umn.edu/~kjlu/papers/drfuzz.pdf)

**Abstract:** Device drivers are security-critical. In monolithic kernels like Linux, there are hundreds of thousands of drivers which run in the same privilege as the core kernel. Consequently, a bug in a driver can compromise the whole system. More critically, drivers are particularly buggy. First, drivers receive complex and untrusted inputs from not only the user space but also the hardware. Second, the driver code can be developed by less-experienced third parties, and is less tested because running a driver requires the corresponding hardware device or the emulator. Therefore, existing studies show that drivers tend to have a higher bug density and have become a major security threat. Existing testing techniques have to focus the fuzzing on a limited number of drivers that have the corresponding devices or the emulators, thus cannot scale.

In this paper, we propose a device-free driver fuzzing system, DR. FUZZ, that does not require hardware devices to fuzztest drivers. The core of DR. FUZZ is a semantic-informed mechanism that efficiently generates inputs to properly construct relevant data structures to pass the âvalidation chainâ? in driving initialization, which enables subsequent device-free driver fuzzing. The elimination of the needs for the hardware devices and the emulators removes the bottleneck in driver testing. The semanticinformed mechanism incorporates multiple new techniques to make device-free driver fuzzing practical: inferring valid input values for passing the validation chain in initialization, inferring the temporal usage order of input bytes to minimize mutation space, and employing error states as a feedback to direct the fuzzing going through the validation chain. Moreover, the semantic-informed mechanism is generic; we can also instruct it to generate semi-malformed inputs for a higher code coverage. We evaluate DR. FUZZ on 214 Linux drivers. With an only 24-hour time budget, DR. FUZZ can successfully initialize and enable most of the drivers without the corresponding devices, whereas existing fuzzers like syzkaller cannot succeed in any case. DR. FUZZ also significantly outperforms existing driver fuzzers that are even equipped with the device or emulator in other aspects: it increases the code coverage by 70% and the throughput by 18%. With DR. FUZZ, we also find 46 new bugs in these Linux drivers.


### SyzScope: Revealing High-Risk Security Impacts of Fuzzer-Exposed Bugs in Linux kernel  (USENIX SEC'22)

* <img src="image/youtube.png">[Video](https://youtu.be/MJbqeo5qtQ0)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22summer_zou.pdf)

**Abstract:** Fuzzing has become one of the most effective bug finding approach for software. In recent years, 24*7 continuous fuzzing platforms have emerged to test critical pieces of software, e.g., Linux kernel. Though capable of discovering many bugs and providing reproducers (e.g., proof-of-concepts), a major problem is that they neglect a critical function that should have been built-in, i.e., evaluation of a bugâs security impact. It is well-known that the lack of understanding of security impact can lead to delayed bug fixes as well as patch propagation. In this paper, we develop SyzScope, a system that can automatically uncover new âhigh-riskâ? impacts given a bug with seemingly âlow-riskâ? impacts. From analyzing over a thousand low-risk bugs on syzbot, SyzScope successfully determined that 183 low-risk bugs (more than 15%) in fact contain high-risk impacts, e.g., control flow hijack and arbitrary memory write, some of which still do not have patches available yet.


### HEALER: Relation Learning Guided Kernel Fuzzing (SOSP 2021)

* <img src="image/youtube.png">[Video](https://www.youtube.com/watch?v=2jZkBxNrIGg)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/healer-sosp21.pdf)

**Abstract:** Modern operating system kernels are too complex to be free of bugs. Fuzzing is a promising approach for vulnerability detection and has been applied to kernel testing. However, existing work does not consider the influence relations between system calls when generating and mutating inputs, resulting in difficulties when trying to reach into the kernelâs deeper logic effectively.

In this paper, we propose HEALER, a kernel fuzzer that improves fuzzingâs effectiveness by utilizing system call relation learning. HEALER learns the influence relations between system calls by dynamically analyzing minimized test cases. Then, HEALER utilizes the learned relations to guide input generation and mutation, which improves the quality of test cases and the effectiveness of fuzzing. We implemented HEALER and evaluated its performance on recent versions of the Linux kernel. Compared to state-of-the-art kernel fuzzers such as Syzkaller and Moonshine, HEALER improves branch coverage by 28% and 21%, while achieving a speedup of 2.2Ã and 1.8Ã, respectively. In addition, HEALER detected 218 vulnerabilities, 33 of which are previously unknown and have been confirmed by the corresponding kernel maintainers.


### NTFUZZ: Enabling Type-Aware Kernel Fuzzing on Windows with Static Binary Analysis(S&P 2021)

* <img src="image/youtube.png">[Video](https://youtu.be/3zYzuRJlIdw)

* <img src="image/blog_24px.jpg">[Reading Note](https://ycdxsb.cn/84f38a6d.html)

* <img src="image/pdf_24px.png">[Paper](https://softsec.kaist.ac.kr/~jschoi/data/oakland2021.pdf)

**Abstract:** Although it is common practice for kernel fuzzers to leverage type information of system calls, current Windows kernel fuzzers do not follow the practice as most system calls are private and largely undocumented. In this paper, we present a practical static binary analyzer that automatically infers system call types on Windows at scale. We incorporate our analyzer to NTFUZZ, a type-aware Windows kernel fuzzing framework. To our knowledge, this is the first practical fuzzing system that utilizes scalable binary analysis on a COTS OS. With NTFUZZ, we found 11 previously unknown kernel bugs, and earned $25,000 through the bug bounty program offered by Microsoft. All these results confirm the practicality of our system as a kernel fuzzer.


### Finding Bugs in File Systems with an Extensible Fuzzing Framework (TOS 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/TOS20_FileSys.pdf)

**Abstract:** File systems are too large to be bug free. Although handwritten test suites have been widely used to stress file systems, they can hardly keep up with the rapid increase in file system size and complexity, leading to new bugs being introduced. These bugs come in various flavors: buffer overflows to complicated semantic bugs. Although bug-specific checkers exist, they generally lack a way to explore file system states thoroughly. More importantly, no turnkey solution exists that unifies the checking effort of various aspects of a file system under one umbrella. 

In this article, to highlight the potential of applying fuzzing to find any type of file system bugs in a generic way, we propose Hydra, an extensible fuzzing framework. Hydra provides building blocks for file system fuzzing, including input mutators, feedback engines, test executors, and bug post-processors. As a result, developers only need to focus on building the core logic for finding bugs of their interests. We showcase the effectiveness of Hydra with four checkers that hunt crash inconsistency, POSIX violations, logic assertion failures, and memory errors. So far, Hydra has discovered 157 new bugs in Linux file systems, including three in verified file systems (FSCQ and Yxv6).


### Finding race conditions in Kernels: from fuzzing to symbolic exection (2020)

* <img src="image/pdf_24px.png">[Paper](https://smartech.gatech.edu/bitstream/handle/1853/63668/XU-DISSERTATION-2020.pdf)

**Abstract:** The scale and pervasiveness of concurrent software pose challenges for security researchers: race conditions are more prevalent than ever, and the growing software complexity keeps exacerbating the situation â? expanding the arms race between security practitioners and attackers beyond memory errors. As a consequence, we need a new generation of bug hunting tools that not only scale well with increasingly larger codebases but also catch up with the growing importance of race conditions.

In this thesis, two complementary race detection frameworks for OS kernels are presented: multi-dimensional fuzz testing and symbolic checking. Fuzz testing turns bug finding into a probabilistic search, but current practices restrict themselves to one dimension only (sequential executions). This thesis illustrates how to explore the concurrency dimension and extend the bug scope beyond memory errors to the broad spectrum of concurrency bugs. On the other hand, conventional symbolic executors face challenges when applied to OS kernels, such as path explosions due to branching and loops. They also lack a systematic way of modeling and tracking constraints in the concurrency dimension (e.g., to enforce a particular schedule for thread interleavings) The gap can be partially filled with novel techniques for symbolic execution in this thesis.


### Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems (SOSP 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/SOSP19_Hydra.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/sslab-gatech/hydra)

* <img src="image/ppt_24px.png">[Slides](https://squizz617.github.io/slides/hydra-sosp19-slides.pdf)

**Abstract:** File systems are too large to be bug free. Although handwritten test suites have been widely used to stress file systems, they can hardly keep up with the rapid increase in file system size and complexity, leading to new bugs being introduced and reported regularly. These bugs come in various flavors: simple buffer overflows to sophisticated semantic bugs. Although bug-specific checkers exist, they generally lack a way to explore file system states thoroughly. More importantly, no turnkey solution exists that unifies the checking effort of various aspects of a file system under one umbrella.

In this paper, we highlight the potential of applying fuzzing to find not just memory errors but, in theory, any type of file system bugs with an extensible fuzzing framework: Hydra. Hydra provides building blocks for file system fuzzing, including input mutators, feedback engines, a libOS-based executor, and a bug reproducer with test case minimization. As a result, developers only need to focus on building the core logic for finding bugs of their own interests. We showcase the effectiveness of Hydra with four checkers that hunt crash inconsistency, POSIX violations, logic assertion failures, and memory errors. So far, Hydra has discovered 91 new bugs in Linux file systems, including one in a verified file system (FSCQ), as well as four POSIX violations.


### Fuzzing File Systems via Two-Dimensional Input Space Exploration (S&P 2019)

* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/113426778)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/d2VEQTLtHV5Z-li6M3KBFA)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP19_Fuzzing_File.pdf)

* <img src="image/ppt_24px.png">[Slides](https://taesoo.kim/pubs/2019/xu:janus-slides.pdf)

**Abstract:** File systems, a basic building block of an OS, are too big and too complex to be bug free. Nevertheless, file systems rely on regular stress-testing tools and formal checkers to find bugs, which are limited due to the ever-increasing complexity of both file systems and OSes. Thus, fuzzing, proven to be an effective and a practical approach, becomes a preferable choice, as it does not need much knowledge about a target. However, three main challenges exist in fuzzing file systems: mutating a large image blob that degrades overall performance, generating image-dependent file operations, and reproducing found bugs, which is difficult for existing OS fuzzers. Hence, we present JANUS, the first feedback-driven fuzzer that explores the two-dimensional input space of a file system, i.e., mutating metadata on a large image, while emitting image-directed file operations. In addition, JANUS relies on a library OS rather than on traditional VMs for fuzzing, which enables JANUS to load a fresh copy of the OS, thereby leading to better reproducibility of bugs. We evaluate JANUS on eight file systems and found 90 bugs in the upstream Linux kernel, 62 of which have been acknowledged. Forty-three bugs have been fixed with 32 CVEs assigned. In addition, JANUS achieves higher code coverage on all the file systems after fuzzing 12 hours, when compared with the state-of-the-art fuzzer Syzkaller for fuzzing file systems. JANUS visits 4.19x and 2.01x more code paths in Btrfs and ext4, respectively. Moreover, JANUS is able to reproduce 88-100% of the crashes, while Syzkaller fails on all of them.


### Unicorefuzz: On the Viability of Emulation for Kernelspace Fuzzing (USENIX WOOT'19)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/woot19-paper_maier.pdf)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/sites/default/files/conference/protected-files/woot19_slides_maier.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/fgsect/unicorefuzz)

**Abstract:** Fuzzing uncovers an ever-growing number of critical vulnerabilities. Despite the simple conceptâexecute the target until it crashesâsetting up fuzz tests can pose complex challenges. This is especially true for code that cannot run as part of a userland process on desktop operating systemsâfor example device drivers and kernel components. In this paper, we explore the use of CPU emulation to fuzz arbitrary parsers in kernelspace with coverage-based feedback. We propose and open-source Unicorefuzz and explain merits and pitfalls of emulation-based fuzzing approaches. The viability of the approach is evaluated against artificial Linux kernel modules, the Open vSwitch network virtualization component as well as bugs originally uncovered by syzcaller. Emulator-based fuzzing of kernel code is not very complex to set up and can even be used to fuzz operating systems and devices for which no source code is available.

### Razzer: Finding Kernel Race Bugs through Fuzzing (S&P 2019)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/nq1A12Dra2vUiQWbtLXctg)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2019/03/06/razzer-finding-kernel-race-bugs-through-fuzzing/)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.jianshu.com/p/43ced9660257)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP19_Razzer.pdf)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/Razzer%EF%BC%9AFinding%20Kernel%20Race%20Bugs%20through%20Fuzzing-SP-2019.png)

* <img src="image/ppt_24px.png">[Slides](https://lifeasageek.github.io/papers/jeong-razzer-slides.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/compsec-snu/razzer)

**Abstract:** A data race in a kernel is an important class of bugs, critically impacting the reliability and security of the associated system. As a result of a race, the kernel may become unresponsive. Even worse, an attacker may launch a privilege escalation attack to acquire root privileges. In this paper, we propose Razzer, a tool to find race bugs in kernels. The core of Razzer is in guiding fuzz testing towards potential data race spots in the kernel. Razzer employs two techniques to find races efficiently: a static analysis and a deterministic thread interleaving technique. Using a static analysis, Razzer identifies over-approximated potential data race spots, guiding the fuzzer to search for data races in the kernel more efficiently. Using the deterministic thread interleaving technique implemented at the hypervisor, Razzer tames the non-deterministic behavior of the kernel such that it can deterministically trigger a race. We implemented a prototype of Razzer and ran the latest Linux kernel (from v4.16-rc3 to v4.18-rc3) using Razzer. As a result, Razzer discovered 30 new races in the kernel, with 16 subsequently confirmed and accordingly patched by kernel developers after they were reported.


### MoonShine: Optimizing OS Fuzzer Seed Selection with Trace Distillation (USENIX Security2018)

* <img src="image/wechat_24px.png">[Reading Note](https://mp.weixin.qq.com/s/L2XgaQB7BZzcptvOrhMvXg)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.jianshu.com/p/7e90ad222acf)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/MoonShine%EF%BC%9AOptimizing%20OS%20Fuzzer%20Seed%20Selection%20with%20Trace%20Distillation-USENUX2018.png)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX18_MoonShine.pdf)

**Abstract:** OS fuzzers primarily test the system call interface between the OS kernel and user-level applications for security vulnerabilities. The effectiveness of evolutionary OS fuzzers depends heavily on the quality and diversity of their seed system call sequences. However, generating good seeds for OS fuzzing is a hard problem as the behavior of each system call depends heavily on the OS kernel state created by the previously executed system calls. Therefore, popular evolutionary OS fuzzers often rely on hand-coded rules for generating valid seed sequences of system calls that can bootstrap the fuzzing process. Unfortunately, this approach severely restricts the diversity of the seed system call sequences and therefore limits the effectiveness of the fuzzers.
In this paper, we develop MoonShine, a novel strategy for distilling seeds for OS fuzzers from system call traces of real-world programs while still maintaining the dependencies across the system calls. MoonShine leverages light-weight static analysis for efficiently detecting dependencies across different system calls.
We designed and implemented MoonShine as an extension to Syzkaller, a state-of-the-art evolutionary fuzzer for the Linux kernel. Starting from traces containing 2.8 million system calls gathered from 3,220 real-world programs, MoonShine distilled down to just over 14,000 calls while preserving 86% of the original code coverage. Using these distilled seed system call sequences, MoonShine was able to improve Syzkaller's achieved code coverage for the Linux kernel by 13% on average. MoonShine also found 14 new vulnerabilities in the Linux kernel that were not found by Syzkaller.


### FUZE: Towards Facilitating Exploit Generation for Kernel Use-After-Free Vulnerabilities (USENIX Security2018)

* <img src="image/because.png">[Reading Note](https://www.jianshu.com/p/cfe7c9f7e852?utm_campaign=hugo)
  
* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/conference/usenixsecurity18/sec18-wu_0.pdf)

* <img src="image/ppt_24px.png">[Slides](https://www.usenix.org/sites/default/files/conference/protected-files/security18_slides_wu.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/ww9210/Linux_kernel_exploits)

**Abstract:** Software vendors usually prioritize their bug remediation based on ease of their exploitation. However, accurately determining exploitability typically takes tremendous hours and requires significant manual efforts. To address this issue, automated exploit generation techniques can be adopted. In practice, they however exhibit an insufficient ability to evaluate exploitability particularly for the kernel Use-After-Free (UAF) vulnerabilities. This is mainly because of the complexity of UAF exploitation as well as the scalability of an OS kernel.

In this paper, we therefore propose FUZE, a new framework to facilitate the process of kernel UAF exploitation. The design principle behind this technique is that we expect the ease of crafting an exploit could augment a security analyst with the ability to expedite exploitability evaluation. Technically, FUZE utilizes kernel fuzzing along with symbolic execution to identify, analyze and evaluate the system calls valuable and useful for kernel UAF exploitation.

To demonstrate the utility of FUZE, we implement FUZE on a 64-bit Linux system by extending a binary analysis framework and a kernel fuzzer. Using 15 real-world kernel UAF vulnerabilities on Linux systems, we then demonstrate FUZE could not only escalate kernel UAF exploitability and but also diversify working exploits. In addition, we show that FUZE could facilitate security mitigation bypassing, making exploitability evaluation less labor-intensive and more efficient.


### kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels (Usenix Security2017)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/46J-kU37SerZd2H3L2GiTw)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.anquanke.com/post/id/188783#h3-12)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/kAFL-%20Hardware-Assisted%20Feedback%20Fuzzing%20for%20OS%20Kernels-USENIX-2017.png)

* <img src="image/youdao_note_24px.png">[Reading Note](https://github.com/ckxckx/aos_course/blob/f146e1f2091c0e8c4916da4b935423903aa4831f/review_kafl.md)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX18_Kafl.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/USENIX18_Kafl_Slides.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/RUB-SysSec/kAFL)

**Abstract:** Many kinds of memory safety vulnerabilities have been endangering software systems for decades. Amongst other approaches, fuzzing is a promising technique to unveil various software faults. Recently, feedback-guided fuzzing demonstrated its power, producing a steady stream of security-critical software bugs. Most fuzzing effortsâespecially feedback fuzzingâare limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. Unfortunately, kernel components are difficult to fuzz as feedback mechanisms (i.e., guided code coverage) cannot be easily applied. Additionally, non-determinism due to interrupts, kernel threads, statefulness, and similar mechanisms poses problems. Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot.

In this paper, we approach the problem of coverage-guided kernel fuzzing in an OS-independent and hardware-assisted way: We utilize a hypervisor and Intelâs Processor Trace (PT) technology. This allows us to remain independent of the target OS as we just require a small user space component that interacts with the targeted OS. As a result, our approach introduces almost no performance overhead, even in cases where the OS crashes, and performs up to 17,000 executions per second on an off-the-shelf laptop. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. Among many crashes, we uncovered several flaws in the ext4 driver for Linux, the HFS and APFS file system of macOS, and the NTFS driver of Windows.


### DIFUZE: Interface aware fuzzing for kernel drivers (CCS 2017)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/5-2PDBlRSW_kTJb1SW1spQ)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.anquanke.com/post/id/188783#h3-11)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/DIFUZE-%20Interface%20Aware%20Fuzzing%20for%20Kernel%20Drivers-CCS-2017.png)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS17_DIFUZE.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/ucsb-seclab/difuze)

**Abstract:** Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results showthat DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.


### IMF: Inferred Model-based Fuzzer (CCS 2017)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS17_IMF.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/SoftSec-KAIST/IMF)

**Abstract:** Kernel vulnerabilities are critical in security because they naturally allow attackers to gain unprivileged root access. Although there has been much research on finding kernel vulnerabilities from source code, there are relatively few research on kernel fuzzing, which is a practical bug finding technique that does not require any source code. Existing kernel fuzzing techniques involve feeding in random input values to kernel API functions. However, such a simple approach does not reveal latent bugs deep in the kernel code, because many API functions are dependent on each other, and they can quickly reject arbitrary parameter values based on their calling context. In this paper, we propose a novel fuzzing technique for commodity OS kernels that leverages inferred dependence model between API function calls to discover deep kernel bugs. We implement our technique on a fuzzing system, called IMF. IMF has already found 32 previously unknown kernel vulnerabilities on the latest macOS version 10.12.3 (16D32) at the time of this writing.



# Hybrid Fuzzing:

### TensileFuzz: Facilitating Seed Input Generation in Fuzzing via String Constraint Solving (ISSTA 2022) 

* <img src="image/pdf_24px.png">[Paper](https://doi.org/10.1145/3533767.3534403)

**Abstract** Seed inputs are critical to the performance of mutation based fuzzers. Existing techniques make use of symbolic execution and gradient descent to generate seed inputs. However, these techniques are not particular suitable for input growth (i.e., making input longer and longer), a key step in seed input generation. Symbolic execution models very low level constraints and prefer fix-sized inputs whereas gradient descent only handles cases where path conditions are arithmetic functions of inputs. We observe that growing an input requires considering a number of relations: length, offset, and count, in which a field is the length of another field, the offset of another field, and the count of some pattern in another field, respective. Theory of string solver is particularly suitable for addressing these relations. We hence propose a novel technique called TensileFuzz, in which we identify input fields and denote them as string variables such that a seed input is the concatenation of these string variables. Additional padding string variables are inserted in between field variables. The aforementioned relations are reverse-engineered and lead to string constraints, solving which instantiates the padding variables and hence grows the input. Our technique also integrates linear regression and gradient descent to ensure the grown inputs satisfy path constraints that lead to path exploration. Our comparison with AFL, and a number of state-of-the-art fuzzers that have similar target applications, including Qsym, Angora, and SLF, shows that TensileFuzz substantially outperforms the others, by 39% - 98% in terms of path coverage.


### CONFETTI: Amplifying Concolic Guidance for Fuzzers (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/190/CONFETTI-Amplifying-Concolic-Guidance-for-Fuzzers)

**Abstract** Fuzz testing (fuzzing) allows developers to detect bugs and vulnerabilities in code by automatically generating defect-revealing inputs. Most fuzzers operate by generating inputs for applications and mutating the bytes of those inputs, guiding the fuzzing process with branch coverage feedback via instrumentation. Whitebox guidance (e.g., taint tracking or concolic execution) is sometimes integrated with coverage-guided fuzzing to help cover tricky-to-reach branches that are guarded by complex conditions (so-called magic values). This integration typically takes the form of a targeted input mutation, eg placing particular byte values at a specific offset of some input in order to cover a branch. However, these dynamic analysis techniques are not perfect in practice, which can result in the loss of important relationships between input bytes and branch predicates, thus reducing the effective power of the technique. We introduce a new, surprisingly simple, but effective technique, global hinting, which allows the fuzzer to insert these interesting bytes not only at a targeted position, but in any position of any input. We implemented this idea in Java, creating CONFETTI, which uses both targeted and global hints for fuzzing. In an empirical comparison with two baseline approaches, a state-of-the-art greybox Java fuzzer and a version of CONFETTI without global hinting, we found that CONFETTI covers more branches and finds 15 previously unreported bugs, including 9 that neither baseline could find. By conducting a forensic analysis of CONFETTIâs execution, we determined that global hinting was at least as effective at revealing new coverage as traditional, targeted hinting.


### FuSeBMC v. 4: Smart Seed Generation for Hybrid Fuzzing (2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2112.10627)

**Abstract** FuSeBMC is a test generator for finding security vulnerabilities in C programs. In earlier work [4], we described a previous version that incrementally injected labels to guide Bounded Model Checking (BMC) and Evolutionary Fuzzing engines to produce test cases for code coverage and bug finding. This paper introduces a new version of FuSeBMC that utilizes both engines to produce smart seeds. First, the engines are run with a short time limit on a lightly instrumented version of the program to produce the seeds. The BMC engine is particularly useful in producing seeds that can pass through complex mathematical guards. Then, FuSeBMC runs its engines with more extended time limits using the smart seeds created in the previous round. FuSeBMC manages this process in two main ways using its Tracer subsystem. Firstly, it uses shared memory to record the labels covered by each test case. Secondly, it evaluates test cases, and those of high impact are turned into seeds for subsequent test fuzzing. As a result, we significantly increased our code coverage score from last year, outperforming all tools that participated in this year's competition in every single category.


### A Tight Integration of Symbolic Execution and Fuzzing (short paper 2021)

* <img src="image/pdf_24px.png">[Paper](https://binsec.github.io/assets/publications/papers/2021-fps.pdf)

**Abstract** Most bug finding tools rely on either fuzzing or symbolic execution. While they both work well in some situations, fuzzing struggles with complex conditions and symbolic execution suffers from path explosion and high constraint solving costs. In order to enjoy the advantages from both techniques, we propose a new approach called Lightweight Symbolic Execution (LSE) that integrates well with fuzzing. Especially, LSE does not require any call to a constraint solver and allows for quickly enumerating inputs. In this short paper, we present the basic concepts of LSE together with promising preliminary experiments.


### Symbolic Security Predicates: Hunt Program Weaknesses (ISPRAS Open 2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2111.05770.pdf)

* <img src="image/ppt_24px.png">[Slides](https://vishnya.xyz/vishnyakov-isprasopen2021.pdf)

**Abstract** Dynamic symbolic execution (DSE) is a powerful method for path exploration during hybrid fuzzing and automatic bug detection. We propose security predicates to effectively detect undefined behavior and memory access violation errors. Initially, we symbolically execute program on paths that don't trigger any errors (hybrid fuzzing may explore these paths). Then we construct a symbolic security predicate to verify some error condition. Thus, we may change the program data flow to entail null pointer dereference, division by zero, out-of-bounds access, or integer overflow weaknesses. Unlike static analysis, dynamic symbolic execution does not only report errors but also generates new input data to reproduce them. Furthermore, we introduce function semantics modeling for common C/C++ standard library functions. We aim to model the control flow inside a function with a single symbolic formula. This assists bug detection, speeds up path exploration, and overcomes overconstraints in path predicate. We implement the proposed techniques in our dynamic symbolic execution tool Sydr. Thus, we utilize powerful methods from Sydr such as path predicate slicing that eliminates irrelevant constraints.

We present Juliet Dynamic to measure dynamic bug detection tools accuracy. The testing system also verifies that generated inputs trigger sanitizers. We evaluate Sydr accuracy for 11 CWEs from Juliet test suite. Sydr shows 95.59% overall accuracy. We make Sydr evaluation artifacts publicly available to facilitate results reproducibility.


### Towards Symbolic Pointers Reasoning in Dynamic Symbolic Execution (IVMEM 2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2109.03698.pdf)

* <img src="image/ppt_24px.png">[Slides](https://vishnya.xyz/mirror/kuts-ivmem2021.pdf)

**Abstract** Dynamic symbolic execution is a widely used technique for automated software testing, designed for execution paths exploration and program errors detection. A hybrid approach has recently become widespread, when the main goal of symbolic execution is helping fuzzer increase program coverage. The more branches symbolic executor can invert, the more useful it is for fuzzer. A program control flow often depends on memory values, which are obtained by computing address indexes from user input. However, most DSE tools don't support such dependencies, so they miss some desired program branches.
We implement symbolic addresses reasoning on memory reads in our dynamic symbolic execution tool Sydr. Possible memory access regions are determined by either analyzing memory address symbolic expressions, or binary searching with SMT-solver. We propose an enhanced linearization technique to model memory accesses.

Different memory modeling methods are compared on the set of programs. Our evaluation shows that symbolic addresses handling allows to discover new symbolic branches and increase the program coverage.


### FUZZOLIC: Mixing fuzzing and concolic execution (Computers&Security 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.sciencedirect.com/science/article/pii/S0167404821001929)

**Abstract:** In the last few years, a large variety of approaches and methodologies have been explored in the context of software testing, ranging from black-box techniques, such as fuzzing, to white-box techniques, such as concolic execution, with a full spectrum of instances in between. Using these techniques, developers and security researchers have been able to identify in the last decade a large number of critical vulnerabilities in thousands of software projects.

In this article, we investigate how to improve the performance and effectiveness of concolic execution, proposing two main enhancements to the original approach. On one side, we devise a novel concolic executor that can analyze complex binary programs while running under QEMU and efficiently produce symbolic queries, which could generate valuable program inputs when solved. On the other side, we investigate whether techniques borrowed from the fuzzing domain can be applied to solve the symbolic queries generated by concolic execution, providing a viable alternative to accurate but expensive SMT solving techniques. We show that the combination of our concolic engine, Fuzzolic, and our approximate solver, Fuzzy-Sat, can perform better in terms of code coverage than popular state-of-the-art fuzzers on a variety of complex programs and can identify different unknown bugs in several real-world applications.

### Concolic-Fuzzing of JavaScript Programs using GraalVM and Truffle (SKILL 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.gi.de/bitstream/handle/20.500.12116/37771/A1-1.pdf?sequence=1&isAllowed=y)

**Abstract:** The scripting language JavaScript has established itself as a central component of the modern internet. However, the dynamic execution model of the language limits the support for source-code analysis, which leaves a developer without essential tools to maintain safety and security requirements. This paper describes a concolic-fuzzer based on the GraalVM to automatically test JavaScript programs. The fuzzer shows promising results in both code coverage and runtime evaluations and provides developers with additional features such as special analysis targets.


### SHFuzz: A hybrid fuzzing method assisted by static analysis for binary programs (China Communications 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9521142)

**Abstract:** Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of programs. To further discover vulnerabilities hidden in deep execution paths, the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch conditions. In general, we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security bug. Based on this observation, we propose a hybrid fuzzing method assisted by static analysis for binary programs. The basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution paths. For this purpose, we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs' weights. The key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid fuzzing. To evaluate the effectiveness of our method, we design and implement a prototype system, namely SHFuzz. The evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.


### A Priority Based Path Searching Method for Improving Hybrid Fuzzing (Computers & Security 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.sciencedirect.com/science/article/pii/S0167404821000663?casa_token=OUEFq5TSDv0AAAAA:jDit2FK_0vPqynepfWH__-mPOAQwfZaRP7Qv9G19x_t22z20N6C293JaNz2I16W2djytcOEFHrGM)

**Abstract:** Hybrid fuzzing which combines classical fuzzing with concolic execution to produce effective test suites is an advanced software vulnerability detection technique. Because fuzzing and concolic execution are complementary in nature, some researchers propose âoptimal strategyâ? and âdiscriminative dispatch strategyâ? to improve the performance of hybrid fuzzing. Although the ideas are interesting and useful, they have some limitations, such as high time overhead and difficulties in implementation. In this paper, we propose a Priority Based Path Searching method (PBPS) to utilize the capability of concolic execution better. PBPS evaluates each path's solving cost and solving demand, and prioritizes them based on two path characteristics, which are path lengths and sample-hits for concolic execution. The rationale is to keep the pipeline full by readily feeding the concolic engine with paths whose constraints are simpler to solve and are less likely to be explored by fuzz testing. We implement PBPS in Driller, which is a popular hybrid fuzzer and we evaluate our system âQuickFuzzâ? with the CQE dataset. Experimental results show that compared with DigFuzz and the original Driller, âQuickFuzzâ? discovers more vulnerabilities and achieves higher code coverage on the CQE dataset.


### Sydr: Cutting Edge Dynamic Symbolic Execution (ISPRAS Open 2020)

* <img src="image/youtube.png">[Video](https://www.ispras.ru/conf/2020/video/compiler-technology-11-december.mp4#t=6021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2011.09269.pdf)

* <img src="image/ppt_24px.png">[Slides](https://vishnya.xyz/vishnyakov-isprasopen2020.pdf)

**Abstract** The security development lifecycle (SDL) is becoming an industry standard. Dynamic symbolic execution (DSE) has enormous amount of applications in computer security (fuzzing, vulnerability discovery, reverse-engineering, etc.). We propose several performance and accuracy improvements for dynamic symbolic execution. Skipping non-symbolic instructions allows to build a path predicate 1.2--3.5 times faster. Symbolic engine simplifies formulas during symbolic execution. Path predicate slicing eliminates irrelevant conjuncts from solver queries. We handle each jump table (switch statement) as multiple branches and describe the method for symbolic execution of multi-threaded programs. The proposed solutions were implemented in Sydr tool. Sydr performs inversion of branches in path predicate. Sydr combines DynamoRIO dynamic binary instrumentation tool with Triton symbolic engine. We evaluated Sydr features on 64-bit Linux executables.


### CSEFuzz: Fuzz Testing Based on Symbolic Execution (Access 2020)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9222017)

**Abstract:** Fuzz testing has been successful in finding defects of various software packages. These defects include file parsing, image processing, Internet browsers, and network protocols. However, the quality of the initial seed test cases greatly influences the coverage and defect detection capability of fuzz testing. To address this issue, we propose CSEFuzz, a fuzz testing approach based on symbolic execution for defect detection. First, CSEFuzz generates candidate test cases by symbolic execution and collects coverage information of the test cases. Then, CSEFuzz extracts the test-case templates of the test cases and selects a set of test-case templates according to specific coverage criteria. Finally, CSEFuzz selects test cases according to the selected test-case templates, and the selected test cases are used as initial seed test cases for fuzz testing. Experiments are conducted on 11 open-source programs. The results show that in comparison with afl-cmin, which is the test-case selection command of Kelinci, CSEFuzz with a path coverage criterion reduces the time costs of the initial seed test selection and verification by 94.26%. In addition, compared with afl-cmin, 32 more paths are covered and 16 more defects are detected by CSEFuzz


### Sequence directed hybrid fuzzing (SANER 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/SANER20_Sequence.pdf)

**Abstract:** Existing directed grey-box fuzzers are effective compared with coverage-based fuzzers. However, they fail to achieve a balance between effectiveness and efficiency, and it is difficult to cover complex paths due to random mutation. To mitigate the issue, we propose a novel approach, sequence directed hybrid fuzzing (SDHF), which leverages a sequence-directed strategy and concolic execution technique to enhance the effectiveness of fuzzing. Given a set of target statement sequences of a program, SDHF aims to generate inputs that can reach the statements in each sequence in order and trigger potential bugs in the program. We implement the proposed approach in a tool called Berry and evaluate its capability on crash reproduction, true positive verification, and vulnerability detection. Experimental results demonstrate that Berry outperforms four state-of-the-art fuzzers, including directed fuzzers BugRedux, AFLGo and Lolly, and undirected hybrid fuzzer QSYM. Moreover, Berry found 7 new vulnerabilities in real-world programs such as UPX and GNU Libextractor, and 3 new CVEs were assigned.


### HFL: Hybrid Fuzzing on the Linux Kernel (NDSS 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#FalkC)
  
* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2020/05/09/hfl-hybrid-fuzzing-on-the-linux-kernel/)

* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/123373175)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS20_HFL.pdf)

* <img src="image/ppt_24px.png">[Slides](https://lifeasageek.github.io/papers/kim-hfl-slides.pdf)

**Abstract:** Hybrid fuzzing, combining symbolic execution and fuzzing, is a promising approach for vulnerability discovery because each approach can complement the other. However, we observe that applying hybrid fuzzing to kernel testing is challenging because the following unique characteristics of the kernel make a naive adoption of hybrid fuzzing inefficient: 1) having many implicit control transfers determined by syscall arguments, 2) controlling and matching internal system state via system calls, and 3) inferring nested argument type for invoking system calls. Failure to handling such challenges will render both fuzzing and symbolic execution inefficient, and thereby, will result in an inefficient hybrid fuzzing. Although these challenges are essential to both fuzzing and symbolic execution, however, to the best of our knowledge, existing kernel testing approaches either naively use each technique separately without handling such challenges or imprecisely handle a part of challenges only by static analysis.

To this end, this paper proposes HFL, which not only combines fuzzing with symbolic execution for hybrid fuzzing but also addresses kernel-specific fuzzing challenges via three distinct features: 1) converting implicit control transfers to explicit transfers, 2) inferring system call sequence to build a consistent system state, and 3) identifying nested arguments types of system calls. As a result, HFL found 24 previously unknown vulnerabilities in recent Linux kernels. Additionally, HFL achieves 14% higher code coverage than Syzkaller, and over S2E/TriforceAFL, achieving even eight times better coverage, using the same amount of resource (CPU, time, etc.). Regarding vulnerability discovery performance, HFL found 13 known vulnerabilities more than three times faster than Syzkaller.


### PANGOLIN: Incremental Hybrid Fuzzing with Polyhedral Path Abstraction (S&P 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#iztcu)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/SP20_PANGOLIN.pdf)

**Abstract:** Hybrid fuzzing, which combines the merits of both fuzzing and concolic execution, has become one of the most important trends in coverage-guided fuzzing techniques. Despite the tremendous research on hybrid fuzzers, we observe that existing techniques are still inefficient. One important reason is that these techniques, which we refer to as non-incremental fuzzers, cache and reuse few computation results and, thus, lose many optimization opportunities. To be incremental, we propose âpolyhedral path abstractionâ?, which preserves the exploration state in the concolic execution stage and allows more effective mutation and constraint solving over existing techniques. We have implemented our idea as a tool, namely PANGOLIN, and evaluated it using LAVA-M as well as nine real-world programs. The evaluation results showed that PANGOLIN outperforms the state-of-the-art fuzzing techniques with the improvement of coverage rate ranging from 10% to 30%. Moreover, PANGOLIN found 400 more bugs in LAVA-M and discovered 41 unseen bugs with 8 of them assigned with the CVE IDs.


### SAVIOR: Towards Bug-Driven Hybrid Testing (S&P 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#VwAnn)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP20_SAVIOR.pdf)

**Abstract:** Hybrid testing combines fuzz testing and concolic execution. It leverages fuzz testing to test easy-to-reach code regions and uses concolic execution to explore code blocks guarded by complex branch conditions. As a result, hybrid testing is able to reach deeper into program state space than fuzz testing or concolic execution alone. Recently, hybrid testing has seen significant advancement. However, its code coveragecentric design is inefficient in vulnerability detection. First, it blindly selects seeds for concolic execution and aims to explore new code continuously. However, as statistics shows, a large portion of the explored code is often invulnerable. Therefore, giving equal attention to every part of the code during hybrid testing is a non-optimal strategy. It also slows down the detection of real vulnerabilities by over 43%. Second, classic hybrid testing quickly moves on after reaching a chunk of code, rather than examining the hidden defects inside. It may frequently miss subtle yet exploitable vulnerabilities despite that it has already explored the vulnerable code paths.

We propose SAVIOR, a new hybrid testing framework pioneering a bug-driven principle. Unlike the existing hybrid testing tools, SAVIOR prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities. Moreover, SAVIOR verifies all vulnerable program locations along the executing program path. By modeling faulty situations using SMT constraints, SAVIOR reasons the feasibility of vulnerabilities and generates concrete test cases as proofs. Our evaluation shows that the bugdriven approach outperforms the mainstream automated testing techniques, including the state-of-the-art hybrid testing driven by code coverage. On average, SAVIOR detects vulnerabilities 43.4% faster than DRILLER and 44.3% faster than QSYM, leading to the discovery of 88 and 76 more security violations, respectively. According to the experimental result on 11 well-fuzzed benchmark programs, SAVIOR triggers 481 unique security violations within the first 24 hours.


### Deferred Concretization in Symbolic Execution via Fuzzing (ISSTA 2019)


* <img src="image/pdf_24px.png">[Paper](./Paper/ISSTA19_Deferred.pdf)

**Abstract:** Concretization is an effective weapon in the armory of symbolic execution engines. However, concretization can lead to loss in coverage, path divergence, and generation of test-cases on which the intended bugs are not reproduced. In this paper, we propose an algorithm, Deferred Concretization, that uses a new category for values within symbolic execution (referred to as the symcrete values) to pend concretization till they are actually needed. Our tool, COLOSSUS, built around these ideas, was able to gain an average coverage improvement of 66.94% and reduce divergence by more than 55% relative to the state-of-the-art symbolic execution engine, KLEE. Moreover, we found that KLEE loses about 38.60% of the states in the symbolic execution tree that COLOSSUS is able to recover, showing that COLOSSUS is capable of covering a much larger coverage space. 


### Send Hardest Problems My Way: Probabilistic Path Prioritization for Hybrid Fuzzing (NDSS 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#NHtzS)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS19_Probabilistic.pdf)

**Abstract:** Hybrid fuzzing which combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, the state-of-the-art hybrid fuzzing systems deploy "demand launch" and "optimal switch" strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to oversimplified assumptions. We then propose a novel "discriminative dispatch" strategy to better utilize the capability of concolic execution. We design a novel Monte Carlo based probabilistic path prioritization model to quantify each path's difficulty and prioritize them for concolic execution. This model treats fuzzing as a random sampling process. It calculates each path's probability based on the sampling information. Finally, our model prioritizes and assigns the most difficult paths to concolic execution. We implement a prototype system DigFuzz and evaluate our system with two representative datasets. Results show that the concolic execution in DigFuzz outperforms than that in a state-of-the-art hybrid fuzzing system Driller in every major aspect. In particular, the concolic execution in DigFuzz contributes to discovering more vulnerabilities (12 vs. 5) and producing more code coverage (18.9% vs. 3.8%) on the CQE dataset than the concolic execution in Driller.


### Intriguer: Field-Level Constraint Solving for Hybrid Fuzzing (CCS 2019)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/gYqamT3WXiyj79mjBQYRiA)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS19_Intriguer.pdf)

**Abstract:** Hybrid fuzzing, which combines fuzzing and concolic execution, is promising in light of the recent performance improvements in concolic engines. We have observed that there is room for further improvement: symbolic emulation is still slow, unnecessary constraints dominate solving time, resources are overly allocated, and hard-to-trigger bugs are missed. To address these problems, we present a new hybrid fuzzer named Intriguer. The key idea of Intriguer is field-level constraint solving, which optimizes symbolic execution with field-level knowledge. Intriguer performs instruction-level taint analysis and records execution traces without data transfer instructions like mov. Intriguer then reduces the execution traces for tainted instructions that accessed a wide range of input bytes, and infers input fields to build field transition trees. With these optimizations, Intriguer can efficiently perform symbolic emulation for more relevant instructions and invoke a solver for complicated constraints only. Our evaluation results indicate that Intriguer outperforms the state-of-the-art fuzzers: Intriguer found all the bugs in the LAVA-M(5h) benchmark dataset for ground truth performance, and also discovered 43 new security bugs in seven real-world programs. We reported the bugs and received 23 new CVEs.


### DeepFuzzer: Accelerated Deep Greybox Fuzzing (TDSC 2019)

* <img src="image/pdf_24px.png">[Paper](http://wingtecher.com/themes/WingTecherResearch/assets/papers/TDSC19.pdf)

**Abstract:** Fuzzing is one of the most effective vulnerability detection techniques, widely used in practice. However, the performance of fuzzers may be limited by their inability to pass complicated checks, inappropriate mutation frequency, arbitrary mutation strategy, or the variability of the environment.
In this paper, we present DeepFuzzer, an enhanced greybox fuzzer with qualified seed generation, balanced seed selection, and hybrid seed mutation. First, we use symbolic execution in a lightweight approach to generate qualified initial seeds which then guide the fuzzer through complex checks. Second, we apply a statistical seed selection algorithm to balance the mutation frequency between different seeds. Further, we develop a hybrid mutation strategy. The random and restricted mutation strategies are combined to maintain a dynamic balance between global exploration and deep search.
We evaluate DeepFuzzer on the widely used benchmark Google fuzzer-test-suite which consists of real-world programs. Compared with AFL, AFLFast, FairFuzz, QSYM, and MOPT in the 24-hour experiment, DeepFuzzer discovers 30%, 240%, 102%, 147%, and 257% more unique crashes, executes 40%, 36%, 36%, 98%, and 15% more paths, and covers 37%, 34%, 34%, 101%, and 11% more branches, respectively. Furthermore, we present the practice of fuzzing a message middleware from Huawei with DeepFuzzer, and 9 new vulnerabilities are reported.


### QSYM: A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing (USENIX Security2018)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=f086faa409a5c6dfabb382e6747381c5&sub=AD1D3F4E3EBC4DCD85694DCAC43EA32B)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX18_QSYM.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/sslab-gatech/qsym)

**Abstract:** Recently, hybrid fuzzing has been proposed to address the limitations of fuzzing and concolic execution by combining both approaches. The hybrid approach has shown its effectiveness in various synthetic benchmarks such as DARPA Cyber Grand Challenge (CGC) binaries, but it still suffers from scaling to find bugs in complex, real-world software. We observed that the performance bottleneck of the existing concolic executor is the main limiting factor for its adoption beyond a small-scale study.
To overcome this problem, we design a fast concolic execution engine, called QSYM, to support hybrid fuzzing. The key idea is to tightly integrate the symbolic emulation with the native execution using dynamic binary translation, making it possible to implement more fine-grained, so faster, instruction-level symbolic emulation. Additionally, QSYM loosens the strict soundness requirements of conventional concolic executors for better performance, yet takes advantage of a faster fuzzer for validation, providing unprecedented opportunities for performance optimizations, e.g., optimistically solving constraints and pruning uninteresting basic blocks.
Our evaluation shows that QSYM does not just outperform state-of-the-art fuzzers (i.e., found 14Ã more bugs than VUzzer in the LAVA-M dataset, and outperformed Driller in 104 binaries out of 126), but also found 13 previously unknown security bugs in eight real-world programs like Dropbox Lepton, ffmpeg, and OpenJPEG, which have already been intensively tested by the state-of-the-art fuzzers, AFL and OSS-Fuzz.


### Angora: Efficient Fuzzing by Principled Search (S&P 2018)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#A4ojR)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2019/01/03/angora-efficient-fuzzing-by-principled-search/)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/Angora%EF%BC%9AEfficient%20Fuzzing%20by%20principled%20Search.png)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP18_Angora.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/AngoraFuzzer/Angora)

**Abstract:** Abstract-Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration. On the LAVA-M data set, Angora found almost all the injected bugs, found more bugs than any other fuzzer that we compared with, and found eight times as many bugs as the second-best fuzzer in the program who. Angora also found 103 bugs that the LAVA authors injected but could not trigger. We also tested Angora on eight popular, mature open source programs. Angora found 6, 52, 29, 40 and 48 new bugs in file, jhead, nm, objdump and size, respectively. We measured the coverage of Angora and evaluated how its key techniques contribute to its impressive performance.


### SAFL: increasing and accelerating testing coverage with symbolic execution and guided fuzzing (ICSE 2018)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE18_SAFL.pdf)

**Abstract:** Mutation-based fuzzing is a widely used software testing technique for bug and vulnerability detection, and the testing performance is greatly affected by the quality of initial seeds and the effectiveness of mutation strategy. In this paper, we present SAFLÂ¹, an efficient fuzzing testing tool augmented with qualified seed generation and efficient coverage-directed mutation. First, symbolic execution is used in a lightweight approach to generate qualified initial seeds. Valuable explore directions are learned from the seeds, thus the later fuzzing process can reach deep paths in program state space earlier and easier. Moreover, we implement a fair and fast coverage-directed mutation algorithm. It helps the fuzzing process to exercise rare and deep paths with higher probability. We implement SAFL based on KLEE and AFL and conduct thoroughly repeated evaluations on real-world program benchmarks against state-of-the-art versions of AFL. After 24 hours, compared to AFL and AFLFast, it discovers 214% and 133% more unique crashes, covers 109% and 63% more paths and achieves 279% and 180% more covered branches. Video link: https://youtu.be/LkiFLNMBhVE


### CAB-Fuzz: Practical Concolic Testing Techniques for COTS Operating Systems (Usenix Security2017)

* <img src="image/pdf_24px.png">[Paper](https://lifeasageek.github.io/papers/kim-cab-fuzz.pdf)

* <img src="image/ppt_24px.png">[Slides](https://lifeasageek.github.io/papers/kim-cab-fuzz-slides.pdf)

**Abstract:** Discovering the security vulnerabilities of commercial off-the-shelf (COTS) operating systems (OSes) is challenging because they not only are huge and complex, but also lack detailed debug information. Concolic testing, which generates all feasible inputs of a program by using symbolic execution and tests the program with the generated inputs, is one of the most promising approaches to solve this problem. Unfortunately, the state-of-the-art concolic testing tools do not scale well for testing COTS OSes because of state explosion. Indeed, they often fail to find a single bug (or crash) in COTS OSes despite their long execution time. In this paper, we propose CAB-FUZZ (Context-Aware and Boundary-focused), a practical concolic testing tool to quickly explore interesting paths that are highly likely triggering real bugs without debug information. First, CAB-FUZZ prioritizes the boundary states of arrays and loops, inspired by the fact that many vulnerabilities originate from a lack of proper boundary checks. Second, CAB-FUZZ exploits real programs interacting with COTS OSes to construct proper contexts to explore deep and complex kernel states without debug information. We applied CAB-FUZZ to Windows 7 and Windows Server 2008 and found 21 undisclosed unique crashes, including two local privilege escalation vulnerabilities (CVE2015-6098 and CVE-2016-0040) and one information disclosure vulnerability in a cryptography driver (CVE2016-7219). CAB-FUZZ found vulnerabilities that are non-trivial to discover; five vulnerabilities have existed for 14 years, and we could trigger them even in the initial version of Windows XP (August 2001).


### Driller: Argumenting Fuzzing Through Selective Symbolic Execution (NDSS 2016)

* <img src="image/blog_24px.jpg">[Reading Note 1](https://firmianay.github.io/2018/06/13/driller.html)

* <img src="image/blog_24px.jpg">[Reading Note 2](https://cgnail.github.io/notes/driller/)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS16_Driller.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/shellphish/driller)

**Abstract:** Memory corruption vulnerabilities are an everpresent risk in software, which attackers can exploit to obtain unauthorized access to confidential information. As products with access to sensitive data are becoming more prevalent, the number of potentially exploitable systems is also increasing, resulting in a greater need for automated software vetting tools. DARPA recently funded a competition, with millions of dollars in prize money, to further research focusing on automated vulnerability finding and patching, showing the importance of research in this area. Current techniques for finding potential bugs include static, dynamic, and concolic analysis systems, which each having their own advantages and disadvantages. A common limitation of systems designed to create inputs which trigger vulnerabilities is that they only find shallow bugs and struggle to exercise deeper paths in executables. We present Driller, a hybrid vulnerability excavation tool which leverages fuzzing and selective concolic execution in a complementary manner, to find deeper bugs. Inexpensive fuzzing is used to exercise compartments of an application, while concolic execution is used to generate inputs which satisfy the complex checks separating the compartments. By combining the strengths of the two techniques, we mitigate their weaknesses, avoiding the path explosion inherent in concolic analysis and the incompleteness of fuzzing. Driller uses selective concolic execution to explore only the paths deemed interesting by the fuzzer and to generate inputs for conditions that the fuzzer cannot satisfy. We evaluate Driller on 126 applications released in the qualifying event of the DARPA Cyber Grand Challenge and show its efficacy by identifying the same number of vulnerabilities, in the same time, as the top-scoring team of the qualifying event.

### Hybrid Fuzz Testing - Discovering Software Bugs via Fuzzing and Symbolic Execution (2012)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE07_Hybrid.pdf)

**Abstract:** Random mutational fuzz testing (fuzzing) and symbolic executions are program testing techniques that have been gaining popularity in the security research community. Fuzzing finds bugs in a target program by natively executing it with random inputs while monitoring the execution for abnormal behaviors such as crashes. While fuzzing may have a reputation of being able to explore deep into a programâs state space efficiently, naÂ¨Ä±ve fuzzers usually have limited code coverage for typical programs since unconstrained random inputs are unlikely to drive the execution down many different paths. In contrast, symbolic execution tests a program by treating the programâs input as symbols and interpreting the program over such symbolic inputs. Although in theory symbolic execution is guaranteed to be effective in achieving code coverage if we explore all possible paths, this generally requires exponential resource and is thus not practical for many real-world programs.

This thesis presents our attempt to attain the best of both worlds by combining fuzzing with symbolic execution in a novel manner. Our technique, called hybrid fuzzing, first uses symbolic execution to discover frontier nodes that represent unique paths in the program. After collecting as many frontier nodes as possible under a user-specifiable resource constraint, it transits to fuzz the program with preconditioned random inputs, which are provably random inputs that respect the path predicate leading to each frontier node. Our current implementation supports programs with linear path predicates and can automatically generate preconditioned random inputs from a polytope model of the input space extracted from binaries. These preconditioned random inputs can then be used with any fuzzer. Experiments show that our implementation is efficient in both time and space, and the inputs generated by it are able to gain extra breadth and depth over previous approaches.


### Hybrid concolic testing (2007)

* <img src="image/pdf_24px.png">[Paper](./Paper/Thesis12_Hybrid.pdf)

**Abstract:** We present hybrid concolic testing, an algorithm that interleaves random testing with concolic execution to obtain both a deep and a wide exploration of program state space. Our algorithm generates test inputs automatically by interleaving random testing until saturation with bounded exhaustive symbolic exploration of program points. It thus combines the ability of random search to reach deep program states quickly together with the ability of concolic testing to explore states in a neighborhood exhaustively. We have implemented our algorithm on top of CUTE and applied it to obtain better branch coverage for an editor implementation (VIM 5.7, 150K lines of code) as well as a data structure implementation in C. Our experiments suggest that hybrid concolic testing can handle large programs and provide, for the same testing budget, almost 4Ã the branch coverage than random testing and almost 2Ã that of concolic testing.


# Mutation\Coverage\Path

### One Fuzzing Strategy to Rule Them All (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://shadowmydx.github.io/papers/icse22-main-1314.pdf)

**Abstract:** Coverage-guided fuzzing has become mainstream in fuzzing to automatically expose program vulnerabilities. Recently, a group of fuzzers are proposed to adopt a random search mechanism namely Havoc, explicitly or implicitly, to augment their edge exploration. However, they only tend to adopt the default setup of Havoc as an implementation option while none of them attempts to explore its power under diverse setups or inspect its rationale for potential improvement. In this paper, to address such issues, we conduct the first empirical study on Havoc to enhance the understanding of its characteristics. Specifically, we first find that applying the default setup of Havoc to fuzzers can significantly improve their edge coverage performance. Interestingly, we further observe that even simply executing Havoc itself without appending it to any fuzzer can lead to strong edge coverage performance and outperform most of our studied fuzzers. Moreover, we also extend the execution time of Havoc and find that most fuzzers can not only achieve significantly higher edge coverage, but also tend to perform similarly (i.e., their performance gaps get largely bridged). Inspired by the findings, we further propose Havocðð´ðµ, which models the Havoc mutation strategy as a multi-armed bandit problem to be solved by dynamically adjusting the mutation strategy. The evaluation result presents that Havocðð´ðµ can significantly increase the edge coverage by 11.1% on average for all the benchmark projects compared with Havoc and even slightly outperform state-of-the-art QSYM which augments its computing resource by adopting three parallel threads. We further execute Havocðð´ðµ with three parallel threads and result in 9% higher average edge coverage over QSYM upon all the benchmark projects.


### BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/144/BeDivFuzz-Integrating-Behavioral-Diversity-into-Generator-based-Fuzzing)

**Abstract:** A popular metric to evaluate the performance of fuzzers is branch coverage. However, we argue that focusing solely on covering many different branches (i.e., the richness) is not sufficient, since the majority of the covered branches may have been exercised only once, which does not inspire a high confidence in the reliability of the covered code. Instead, the distribution of the executed branches (i.e., the evenness) should be considered as well. That is, behavioral diversity is only given if the generated inputs not only trigger many different branches, but also trigger them evenly often with diverse inputs. We introduce BeDivFuzz, a feedback-driven fuzzing technique for generator-based fuzzers. BeDivFuzz distinguishes between structure-preserving and structure-changing mutations in the space of syntactically valid inputs, and biases its mutation strategy towards behavioral diversity based on the received program feedback. We have evaluated BeDivFuzz on Ant, Maven, Closure, Rhino, and Nashorn. The results show that BeDivFuzz achieves better behavioral diversity compared to the state of the art, measured by established biodiversity metrics from the field of ecology.


### FuzzingDriver: the Missing Dictionary to Increase Code Coverage in Fuzzers (SANER 2022)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/abs/2201.04853)

**Abstract:** We propose a tool, called FuzzingDriver, to generate dictionary tokens for coverage-based greybox fuzzers (CGF) from the codebase of any target program. FuzzingDriver does not add any overhead to the fuzzing job as it is run beforehand. We compared FuzzingDriver to Google dictionaries by fuzzing six open-source targets, and we found that FuzzingDriver consistently achieves higher code coverage in all tests. We also executed eight benchmarks on FuzzBench to demonstrate how utilizing FuzzingDriver's dictionaries can outperform six widely-used CGF fuzzers. In future work, investigating the impact of FuzzingDriver's dictionaries on improving bug coverage might prove important.


### EMS: History-Driven Mutation for Coverage-based Fuzzing (NDSS 2022)

* <img src="image/github_24px.png">[Code](https://github.com/puppet-meteor/EMS)

* <img src="image/pdf_24px.png">[Paper](https://nesa.zju.edu.cn/download/lcy_pdf_ems_ndss22.pdf)

**Abstract:** Mutation-based fuzzing is one of the most popular approaches to discover vulnerabilities in a program. To alleviate the inefficiency of mutation-based fuzzing incurred by high randomness in the mutation process, multiple solutions are developed in recent years, especially coverage-based fuzzing. They mainly employ adaptive mutation strategies or integrate constraint-solving techniques to make a good exploration of the test cases which trigger unique paths and crashes. However, they lack a fine-grained reusing of fuzzing history to construct these interesting test cases, i.e., they largely fail to properly utilize fuzzing history across different fuzzing trials. In fact, we discover that test cases in fuzzing history contain rich knowledge of the key mutation strategies that lead to the discovery of unique paths and crashes. Specifically, partial path constraint solutions implicitly carried in these mutation strategies can be reused to accelerate the discovery of new paths and crashes that share similar partial path constraints.

Therefore, we first propose a lightweight and efficient Probabilistic Byte Orientation Model (PBOM) that properly captures the byte-level mutation strategies from intra- and inter-trial history and thus can effectively trigger unique paths and crashes. We then present a novel history-driven mutation framework named EMS that employs PBOM as one of the mutation operators to probabilistically provide desired mutation byte values according to the input ones. We evaluate EMS against state-of-the-art fuzzers including AFL, QSYM, MOPT, MOPT-dict, EcoFuzz, and AFL++ on 9 real world programs. The results show that EMS discovers up to 4.91Ã more unique vulnerabilities than the baseline, and finds more line coverage than other fuzzers on most programs. We report all of the discovered new vulnerabilities to vendors and will open source the prototype of EMS on GitHub.


### OTA: An Operation-oriented Time Allocation Strategy for Greybox Fuzzing (SANER 2021)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9425969)

**Abstract:** Coverage-based greybox fuzzing (CGF) has been widely studied and commonly used for software vulnerability detection. Existing CGF fuzzers fairly allocate execution time for each mutation operation to generate test cases. However, the fair-time-allocation strategy is revealed to be inefficient by our significant experimental observation that different operations have heterogeneous effectiveness on coverage. Those ineffective operations with vast test cases thus occupy the majority of limited runtime, reducing the opportunities for effective operations to explore more paths and find potential vulnerabilities.In this paper, we propose a novel operation-oriented time allocation strategy OTA, which dynamically allocates operation execution time in real time to cope with the effectiveness variation per operation. OTA has three distinguishing advantages: (1) the execution time per operation is novelly initialized on demand and program-dependent; (2) the execution time for each operation is dynamically weighted by its real-time effectiveness on exploring new coverage; (3) the determination of the execution time per operation is well controlled to achieve a quick convergence. Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection abilities of OTA, which substantially outperforms 5 state-of-the-art fuzzers. In addition, OTA exposes 18 previously unknown vulnerabilities in 6 well-tested programs with 13 confirmed with new CVE IDs.


### MaxAFL: Maximizing Code Coverage with a Gradient-Based Optimization Technique (Electronics 2020)

* <img src="image/pdf_24px.png">[Paper](https://www.mdpi.com/2079-9292/10/1/11/pdf)

**Abstract:** Evolutionary fuzzers generally work well with typical software programs because of their simple algorithm. However, there is a limitation that some paths with complex constraints cannot be tested even after long execution. Fuzzers based on concolic execution have emerged to address this issue. The concolic execution fuzzers also have limitations in scalability. Recently, the gradient-based fuzzers that use a gradient to mutate inputs have been introduced. Gradient-based fuzzers can be applied to real-world programs and achieve high code coverage. However, there is a problem that the existing gradient-based fuzzers require heavyweight analysis or sufficient learning time. In this paper, we propose a new type of gradient-based fuzzer, MaxAFL, to overcome the limitations of existing gradient-based fuzzers. Our approach constructs an objective function through fine-grained static analysis. After constructing a well-made objective function, we can apply the gradient-based optimization algorithm. We use a modified gradient-descent algorithm to minimize our objective function and propose some probabilistic techniques to escape local optimum. We introduce an adaptive objective function which aims to explore various paths in the program. We implemented MaxAFL based on the original AFL. MaxAFL achieved increase of code coverage per time compared with three other fuzzers in six open-source Linux binaries. We also measured cumulative code coverage per total execution, and MaxAFL outperformed the other fuzzers in this metric. Finally, MaxAFL can also find more bugs than the other fuzzers.


### PathAFL: Path-Coverage Assisted Fuzzing (ASIA CCS 2020)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/pdf/10.1145/3320269.3384736)

**Abstract:** Fuzzing is an effective method to find software bugs and vulnerabilities. One of the most useful techniques is the coverage-guided fuzzing, whose key element is the tracing code coverage information. Existing coverage-guided fuzzers generally use the the number of basic blocks or edges explored to measure code coverage. Path-coverage can provide more accurate coverage information than basic block and edge coverage. However, the number of paths grows exponentially as the size of a program increases. It is almost impossible to trace all the paths of a real-world application. In this paper, we propose a fuzzing solution named PathAFL, which assists a fuzzer by path identification. It can effectively identify and utilize the important h-path, which is a new path but whose edges have all been touched previously. First, PathAFL only inserts one assembly instruction to AFL's original code to calculate the path hash, and uses a selective instrumentation strategy to reduce the tracing granularity of an execution path. Second, we design a fast filtering algorithm to choose higher weight paths from a large number of h-paths and add them to the seed queue. Third, both the seed selection algorithm and the power schedule are implemented based on the path weight. Finally we implemented PathAFL based on the popular fuzzer AFL and evaluated it on 10 well-fuzzed benchmark programs. In 24 hours, PathAFL explored 38% more paths and 9.3% more edges than AFL. Compared with CollAFL-x, the number is 25% and 5.9% correspondingly. Moreover, PathAFL found the more bugs on the LAVA-M dataset, even four unlisted bugs. The results show that PathAFL outperforms the previous fuzzers in terms of both code coverage and bug discovery. In well-tested programs, PathAFL found 8 new security bugs with 6 CVEs assigned.


### Zeror: Speed Up Fuzzing with Coverage-sensitive Tracing and Scheduling (ASE 2020)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/ase20.pdf)

**Abstract:** Coverage-guided fuzzing is one of the most popular software testing techniques for vulnerability detection. While effective, current fuzzing methods suffer from significant performance penalty due to instrumentation overhead, which limits its practical use. Existing solutions improve the fuzzing speed by decreasing instrumentation overheads but sacrificing coverage accuracy, which results in unstable performance of vulnerability detection.

In this paper, we propose a coverage-sensitive tracing and scheduling framework Zeror that can improve the performance of existing fuzzers, especially in their speed and vulnerability detection. The Zeror is mainly made up of two parts: (1) a self-modifying tracing mechanism to provide a zero-overhead instrumentation for more effective coverage collection, and (2) a real-time scheduling mechanism to support adaptive switch between the zero-overhead instrumented binary and the fully instrumented binary for better vulnerability detection. In this way, Zeror is able to decrease collection overhead and preserve fine-grained coverage for guidance. 

For evaluation, we implement a prototype of Zeror and evaluate it on Google fuzzer-test-suite, which consists of 24 widely-used applications. The results show that Zeror performs better than existing fuzzing speed-up frameworks such as Untracer and INSTRIM, improves the execution speed of the state-of-the-art fuzzers such as AFL and MOPT by 159.80%, helps them achieve better coverage (averagely 10.14% for AFL, 6.91% for MOPT) and detect vulnerabilities faster (averagely 29.00% for AFL, 46.99% for MOPT)


### Not All Coverage Measurements Are Equal: Fuzzing by Coverage Accounting for Input Prioritization (NDSS 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#kNyMv)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS20_Prioritization.pdf)

**Abstract:** Coverage-based fuzzing has been actively studied and widely adopted for finding vulnerabilities in real-world software applications. With code coverage, such as statement coverage and transition coverage, as the guidance of input mutation, coverage-based fuzzing can generate inputs that cover more code and thus find more vulnerabilities without prerequisite information such as input format. Current coverage-based fuzzing tools treat covered code equally. All inputs that contribute to new statements or transitions are kept for future mutation no matter what the statements or transitions are and how much they impact security. Although this design is reasonable from the perspective of software testing, which aims to full code coverage, it is inefficient for vulnerability discovery since that 1) current techniques are still inadequate to reach full coverage within a reasonable amount of time, and that 2) we always want to discover vulnerabilities early so that it can be patched promptly. Even worse, due to the non-discriminative code coverage treatment, current fuzzing tools suffer from recent anti-fuzzing techniques and become much less effective in finding real-world vulnerabilities.

To resolve the issue, we propose coverage accounting, an innovative approach that evaluates code coverage by security impacts. Based on the proposed metrics, we design a new scheme to prioritize fuzzing inputs and develop TortoiseFuzz, a greybox fuzzer for memory corruption vulnerabilities. We evaluated TortoiseFuzz on 30 real-world applications and compared it with 5 state-of-the-art greybox and hybrid fuzzers (AFL, AFLFast, FairFuzz, QSYM, and Angora). TortoiseFuzz outperformed all greybox fuzzers and most hybrid fuzzers. It also had comparative results for other hybrid fuzzers yet consumed much fewer resources. Additionally, TortoiseFuzz found 18 new real-world vulnerabilities and has got 8 new CVEs so far. We will open source TortoiseFuzz to foster future research.

### Matryoshka: fuzzing deeply nested branches (CCS 2019)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/gYqamT3WXiyj79mjBQYRiA)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS19_Matryoshka.pdf)

**Abstract:** Greybox fuzzing has made impressive progress in recent years, evolving from heuristics-based random mutation to approaches for solving individual path constraints. However, they have difficulty solving path constraints that involve deeply nested conditional statements, which are common in image and video decoders, network packet analyzers, and checksum tools. We propose an approach for addressing this problem. First, we identify all the control flow-dependent conditional statements of the target conditional statement. Next, we select the data flow-dependent conditional statements. Finally, we use three strategies to find an input that satisfies all conditional statements simultaneously. We implemented this approach in a tool called Matryoshka and compared its effectiveness on 13 open source programs against other state-of-the-art fuzzers. Matryoshka found significantly more unique crashes than AFL, QSYM, and Angora. We manually classified those crashes into 41 unique new bugs, and obtained 12 CVEs. Our evaluation also uncovered the key technique contributing to Matryoshka's impressive performance: it collects only the nesting constraints that may cause the target conditional statements unreachable, which greatly simplifies the constraints that it has to solve.


### REDQUEEN: Fuzzing with Input-to-State Correspondence (NDSS2019)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=6a4b00d912eab145d1c1f32f11bde3e0&sub=7DADC02169A14B33979BCCB2556E4526)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2019/06/05/redqueen-fuzzing-with-input-to-state-correspondence/)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS19_REDQUEEN.pdf)

**Abstract:** Automated software testing based on fuzzing has experienced a revival in recent years. Especially feedback-driven fuzzing has become well-known for its ability to efficiently perform randomized testing with limited input corpora. Despite a lot of progress, two common problems are magic numbers and (nested) checksums. Computationally expensive methods such as taint tracking and symbolic execution are typically used to overcome such roadblocks. Unfortunately, such methods often require access to source code, a rather precise description of the environment (e.g., behavior of library calls or the underlying OS), or the exact semantics of the platform's instruction set.
In this paper, we introduce a lightweight, yet very effective alternative to taint tracking and symbolic execution to facilitate and optimize state-of-the-art feedback fuzzing that easily scales to large binary applications and unknown environments. We observe that during the execution of a given program, parts of the input often end up directly (i.e., nearly unmodified) in the program state. This input-to-state correspondence can be exploited to create a robust method to
overcome common fuzzing roadblocks in a highly effective and efficient manner. Our prototype implementation, called REDQUEEN, is able to solve magic bytes and (nested) checksum tests automatically for a given binary executable. Additionally, we show that our techniques outperform various state-of-the-art tools on a wide variety of targets across different privilege levels (kernel-space and userland) with no platform-specific code. REDQUEEN is the first method to find more than 100% of the bugs planted in LAVA-M across all targets. Furthermore, we were able to discover 65 new bugs and obtained 16 CVEs in multiple programs and OS kernel drivers. Finally, our evaluation demonstrates that REDQUEEN is fast, widely applicable and outperforms concurrent approaches by up to three orders of magnitude.


### T-Fuzz: fuzzing by program transformation (S&P 2018)

* <img src="image/csdn_24px.png">[Reading Note](https://blog.csdn.net/Chen_zju/article/details/80934710)

* <img src="image/blog_24px.jpg">[Reading Note from dawuge](https://dawuge.github.io/2019/10/31/CodeAlchemistandT-Fuzz/)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP18_T-Fuzz.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/HexHive/T-Fuzz)

**Abstract:** Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes. To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage-guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzer-generated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation and false positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. We have evaluated T-Fuzz on the DARPA Cyber Grand Challenge dataset, LAVA-M dataset and 4 real-world programs (pngfix, tiffinfo, magick and pdftohtml). For the CGC dataset, T-Fuzz finds bugs in 166 binaries, Driller in 121, and AFL in 105. In addition, found 3 new bugs in previously-fuzzed programs and libraries.


### FairFuzz: A Targeted Mutation Strategy for Increasing Greybox Fuzz Testing Coverage (ASE 2018)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=5525c7a18e8681302229e9466290aac2&sub=C4769EA799584C5D89994FE397F76981)

* <img src="image/pdf_24px.png">[Paper](./Paper/ASE18_Fairfuzz.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/carolemieux/afl-rb)

**Abstract:** In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop (AFL), has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the bugs it can find since it simply does not cover large regions of code. If it does not cover parts of the code, it will not find bugs there. We propose a two-pronged approach to increase the coverage achieved by AFL. First, the approach automatically identifies branches exercised by few AFL-produced inputs (rare branches), which often guard code that is empirically hard to cover by naively mutating inputs. The second part of the approach is a novel mutation mask creation algorithm, which allows mutations to be biased towards producing inputs hitting a given rare branch. This mask is dynamically computed during fuzz testing and can be adapted to other testing targets. We implement this approach on top of AFL in a tool named FairFuzz. We conduct evaluation on real-world programs against state-of-the-art versions of AFL. We find that on these programs FairFuzz achieves high branch coverage at a faster rate that state-of-the-art versions of AFL. In addition, on programs with nested conditional structure, it achieves sustained increases in branch coverage after 24 hours (average 10.6% increase). In qualitative analysis, we find that FairFuzz has an increased capacity to automatically discover keywords.


### VUzzer: Application-aware Evolutionary Fuzzing (NDSS 2017)

* <img src="image/youdao_note_24px.png">[Reading Note](http://note.youdao.com/noteshare?id=b0505524143c5f9439c6a5d9dcff9b1d&sub=D8A73723348B4976BA191B235EB13307)

* <img src="image/csdn_24px.jpg">[Reading Note](https://blog.csdn.net/qq_32505207/article/details/104690747)

* <img src="image/because.png">[Reading Note](https://github.com/bsauce/Some-Papers-About-Fuzzing/blob/master/xmind_picture/VUzzer%20Application-aware%20Evolutionary%20Fuzzing-ndss2017.png)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS17_Vuzzer.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/vusec/vuzzer)

**Abstract:** Fuzzing is an effective software testing technique to find bugs. Given the size and complexity of real-world applications, modern fuzzers tend to be either scalable, but not effective in exploring bugs that lie deeper in the execution, or capable of penetrating deeper in the application, but not scalable.
In this paper, we present an application-aware evolutionary fuzzing strategy that does not require any prior knowledge of the application or input format. In order to maximize coverage and explore deeper paths, we leverage control- and data-flow features based on static and dynamic analysis to infer fundamental properties of the application. This enables much faster generation of interesting inputs compared to an application-agnostic approach. We implement our fuzzing strategy in VUzzer and evaluate it on three different datasets: DARPA Grand Challenge binaries (CGC), a set of real-world applications (binary input parsers), and the recently released LAVA dataset. On all of these datasets, VUzzer yields significantly better results than state-of-the-art fuzzers, by quickly finding several existing and new bugs.



# Grammars \ Semantic \ Context-aware Fuzzing

### FRAMESHIFTER: Manipulating HTTP/2 Frame Sequences with Fuzzing (Usenix Security2020)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec22-jabiyev.pdf)

**Abstract:** HTTP/2 adoption is rapidly climbing. However, in practice, Internet communications still rarely happen over end-to-end HTTP/2 channels. This is due to Content Delivery Networks and other reverse proxies, ubiquitous and necessary components of the Internet ecosystem, which only support HTTP/2 on the client's end, but not the forward connection to the origin server. Instead, proxy technologies predominantly rely on HTTP/2-to-HTTP/1 protocol conversion between the two legs of the connection.

We present the first systematic exploration of HTTP/2-to-HTTP/1 protocol conversion anomalies and their security implications. We develop a novel grammar-based fuzzer for HTTP/2, experiment with 12 popular reverse proxy technologies & CDNs through HTTP/2 frame sequence and content manipulation, and discover a plethora of novel web application attack vectors that lead to Request Blackholing, Denial-of-Service, Query-of-Death, and Request Smuggling attacks.


### SGXFuzz: Efficiently Synthesizing Nested Structures for SGX Enclave Fuzzing (Usenix Security2022)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/conference/usenixsecurity22/presentation/cloosters)

**Abstract:** Intel's Software Guard Extensions (SGX) provide a nonintrospectable trusted execution environment (TEE) to protect security-critical code from a potentially malicious OS. This protection can only be effective if the individual enclaves are secure, which is already challenging in regular software, and this becomes even more difficult for enclaves as the entire environment is potentially malicious. As such, many enclaves expose common vulnerabilities, e.g., memory corruption and SGXspecific vulnerabilities like null-pointer dereferences. While fuzzing is a popular technique to assess the security of software, dynamically analyzing enclaves is challenging as enclaves are meant to be non-introspectable. Further, they expect an allocated multi-pointer structure as input instead of a plain buffer.

In this paper, we present SGXFUZZ, a coverage-guided fuzzer that introduces a novel binary input structure synthesis method to expose enclave vulnerabilities even without source-code access. To obtain code coverage feedback from enclaves, we show how to extract enclave code from distribution formats. We also present an enclave runner that allows execution of the extracted enclave code as a user-space application at native speed, while emulating all relevant environment interactions of the enclave. We use this setup to fuzz enclaves using a state-of-the-art snapshot fuzzing engine that deploys our novel structure synthesis stage. This stage synthesizes multi-layer pointer structures and size fields incrementally on-the-fly based on fault signals. Furthermore, it matches the expected input format of the enclave without any prior knowledge. We evaluate our approach on 30 open- and closed-source enclaves and found a total of 79 new bugs and vulnerabilities.


### Unicorn: Detect Runtime Error in Time-Series Databases With Hybrid Input Synthesis (ISSTA 2022)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/issta22.pdf)

**Abstract:** The ubiquitous use of time-series databases in the safety-critical Internet of Things domain demands strict security and correctness. One successful approach in database bug detection is fuzzing, where hundreds of bugs have been detected automatically in relational databases. However, it cannot be easily applied to time-series databases: the bulk of time-series logic is unreachable because of mismatched query specifications, and serious bugs are undetectable because of implicitly handled exceptions.

In this paper, we propose Unicorn to secure time-series databases with automated fuzzing. First, we design hybrid input synthesis to generate high-quality queries which not only cover time-series features but also ensure grammar correctness. Then, Unicorn uses proactive exception detection to discover minuscule-symptom bugs which hide behind implicit exception handling. With the specialized design oriented to time-series databases, Unicorn outperforms the state-of-the-art database fuzzers in terms of coverage and bugs. Specifically, Unicorn outperforms SQLsmith and SQLancer on widely used time-series databases IoTDB, KairosDB, TimescaleDB, TDEngine, QuestDB, and GridDB in the number of basic blocks by 21%-199% and 34%-693%, respectively. More importantly, Unicorn has discovered 42 previously unknown bugs.


### Cooper: Testing the Binding Code of Scripting Languages with Cooperative Mutation (NDSS 2022)

* <img src="image/pdf_24px.png">[Paper](https://huhong789.github.io/papers/xu:cooper.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/TCA-ISCAS/Cooper)

**Abstract:** Scripting languages like JavaScript are being integrated into commercial software to support easy file modification. For example, Adobe Acrobat accepts JavaScript to dynamically manipulate PDF files. To bridge the gap between the high-level scripts and the low-level languages (like C/C++) used to implement the software, a binding layer is necessary to transfer data and transform representations. However, due to the complexity of two sides, the binding code is prone to inconsistent semantics and security holes, which lead to severe vulnerabilities. Existing efforts for testing binding code merely focus on the script side, and thus miss bugs that require special program native inputs.

In this paper, we propose cooperative mutation, which modifies both the script code and the program native input to trigger bugs in binding code. Our insight is that many bugs are due to the interplay between the program initial state and the dynamic operations, which can only be triggered through two-dimensional mutations. We develop three novel techniques to enable practical cooperative mutation on popular scripting languages: we first cluster objects into semantics similar classes to reduce the mutation space of native inputs; then, we statistically infer the relationship between script code and object classes based on a large number of executions; at last, we use the inferred relationship to select proper objects and related script code for targeted mutation. We applied our tool, COOPER, on three popular systems that integrate scripting languages, including Adobe Acrobat, Foxit Reader and Microsoft Word. COOPER successfully found 134 previously unknown bugs. We have reported all of them to the developers. At the time of paper publishing, 59 bugs have been fixed and 33 of them are assigned CVE numbers. We are awarded totally 22K dollars bounty for 17 out of all reported bugs.


### Fuzzing Class Specifications (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/83/Fuzzing-Class-Specifications)

**Abstract:** Expressing class specifications via executable constraints is important for various software engineering tasks such as test generation, bug finding and automated debugging, but developers rarely write them. Techniques that infer specifications from code exist to fill this gap, but they are designed to support specific kinds of assertions and are difficult to adapt to support different assertion languages, e.g., to add support for quantification, or additional comparison operators, such as membership or containment.

To address the above issue, we present SpecFuzzer, a novel technique that combines grammar-based fuzzing, dynamic invariant detection, and mutation analysis, to automatically produce class specifications. SpecFuzzer uses: \emph{(i)} a fuzzer as a generator of candidate assertions derived from a grammar that is automatically obtained from the class definition; \emph{(ii)} a dynamic invariant detector âDaikonâ? to filter out assertions invalidated by a test suite; and \emph{(iii)} a mutation-based mechanism to cluster and rank assertions, so that similar constraints are grouped and then the stronger prioritized. Grammar-based fuzzing enables SpecFuzzer to be straightforwardly adapted to support different specification languages, by manipulating the fuzzing grammar, e.g., to include additional operators.

We evaluate our technique on a benchmark of 43 Java methods employed in the evaluation of the state-of-the-art techniques GAssert and EvoSpex. Our results show that SpecFuzzer can easily support a more expressive assertion language, over which is more effective than GAssert and EvoSpex in inferring specifications, according to standard performance metrics.


### Efficient ECU Analysis Technology through Structure-aware CAN Fuzzing (Access 2022)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9713864)

**Abstract:** Modern vehicles are equipped with a number of electronic control units (ECUs), which control vehicles efficiently by communicating with each other through the controller area network (CAN). However, the CAN is known to be vulnerable to cyber attacks because it does not have any security mechanisms. To find vulnerable CAN messages that can control safety-critical functions in ECUs, researchers have studied CAN fuzzing methods. In existing CAN fuzzing methods, fuzzing input values are generally generated at random without considering the structure of CAN messages, resulting in non-negligible CAN fuzzing time. In addition, existing fuzzing solutions have limited monitoring capabilities of the fuzzing results. In this paper, we propose a Structure-aware CAN Fuzzing protocol, in which the structure of CAN messages is considered and fuzzing input values are systematically generated to locate vulnerable functions in ECUs. Our proposed Structure-aware CAN Fuzzing system takes less time to run than existing solutions, meaning that problematic CAN messages that may have originated from SW implementation errors or CAN DBC (database CAN) design errors can be found quickly and, subsequently, appropriate action can be taken. Finally, we evaluated the performance of our Structure-aware CAN Fuzzing system on two real vehicles. We proved that our proposed method can find CAN messages that control safety-critical functions in ECUs faster than existing fuzzing solutions.


### Semantic Image Fuzzing of AI Perception Systems

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/172/Semantic-Image-Fuzzing-of-AI-Perception-Systems)

**Abstract:** Perception systems enable autonomous systems to interpret sensor readings obtained from the physical world. Testing of such systems aims to uncover misinterpretations that can severely impact an autonomous systemâs behavior. Current testing methods for perception systems, however, are inadequate: (1) when testing on real-world input data, the cost of human interpretation and annotation is very high, so test suites tend to be small; (2) the simulation-reality gap reduces the validity of test results based on simulated worlds; and (3) methods for synthesizing test cases with realistic inputs are limited in scope and lack semantic interpretations. To address these limitations, we developed a novel approach to fuzz testing perception systems based on semantic mutation of real-world sensor readings and their corresponding ground-truth interpretations. This enables mutations like adding a car driving on the street to an existing image while providing an oracle that accounts for that semantic change. We implemented our approach and evaluated its performance by generating 150,000 semantically mutated image inputs for five state-of-the-art perception systems. Our approach produced novel image inputs not found in the original test suite, and uncovered inputs that lead to significant issues in the analyzed systems at a very low cost.


### SoFi: Reflection-Augmented Fuzzing for JavaScript Engines (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](https://dl.acm.org/doi/10.1145/3460120.3484823)

**Abstract:** JavaScript engines have been shown prone to security vulnerabilities, which can lead to serious consequences due to their popularity. Fuzzing is an effective testing technique to discover vulnerabilities. The main challenge of fuzzing JavaScript engines is to generate syntactically and semantically valid inputs such that deep functionalities can be explored. However, due to the dynamic nature of JavaScript and the special features of different engines, it is quite challenging to generate semantically meaningful test inputs.

We observed that state-of-the-art semantic-aware JavaScript fuzzers usually require manually written rules to analyze the semantics for a JavaScript engine, which is labor-intensive, incomplete and engine-specific. Moreover, the error rate of generated test cases is still high. Another challenge is that existing fuzzers cannot generate new method calls that are not included in the initial seed corpus or pre-defined rules, which limits the bug-finding capability.

To this end, we propose a novel semantic-aware fuzzing technique named SoFi. To guarantee the validity of the generated test cases, SoFi adopts a fine-grained program analysis to identify available variables and infer types of these variables for the mutation. Moreover, an automatic repair strategy is proposed to repair syntax/semantic errors in invalid test cases. To improve the exploration capability of SoFi, we propose a reflection-based analysis to identify unseen attributes and methods of objects, which are further used in the mutation. With fine-grained analysis and reflection-based augmentation, SoFi can generate more valid and diverse test cases. Besides, SoFi is general in different JavaScript engines without any manual configuration (e.g., the grammar rules). The evaluation results have shown that SoFi outperforms state-of-the-art techniques in generating semantically valid inputs, improving code coverage and detecting more bugs. SoFi discovered 51 bugs in popular JavaScript engines, 28 of which have been confirmed or fixed by the developers and 10 CVE IDs have been assigned.


### V-SHUTTLE: Scalable and Semantics-Aware Hypervisor Fuzzing (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec21-salls.pdf)

**Abstract:** With the wide application and deployment of cloud computing in enterprises, virtualization developers and security researchers are paying more attention to cloud computing security. The core component of cloud computing products is the hypervisor, which is also known as the virtual machine monitor (VMM) that can isolate multiple virtual machines in one host machine. However, compromising the hypervisor can lead to virtual machine escape and the elevation of privilege, allowing attackers to gain the permission of code execution in the host. Therefore, the security analysis and vulnerability detection of the hypervisor are critical for cloud computing enterprises. Importantly, virtual devices expose many interfaces to a guest user for communication, making virtual devices the most vulnerable part of a hypervisor. However, applying fuzzing to the virtual devices of a hypervisor is challenging because the data structures transferred by DMA are constructed in a nested form according to protocol specifications. Failure to understand the protocol of the virtual devices will make the fuzzing process stuck in the initial fuzzing stage, resulting in inefficient fuzzing.

In this paper, we propose a new framework called V-Shuttle to conduct hypervisor fuzzing, which performs scalable and semanticsaware hypervisor fuzzing. To address the above challenges, we first design a DMA redirection mechanism to significantly reduce the manual efforts to reconstruct virtual devicesâ? protocol structures and make the fuzzing environment setup automated and scalable. Furthermore, we put forward a new fuzzing mutation scheduling mechanism called seedpool to make the virtual device fuzzing process semantics-aware and speed up the fuzzing process to achieve high coverage. Extensive evaluation on QEMU and VirtualBox, two of the most popular hypervisor platforms among the world, shows that V-Shuttle can efficiently reproduce existing vulnerabilities and find new vulnerabilities. We further carried out a long-term fuzzing campaign in QEMU/KVM and VirtualBox with V-Shuttle. In total, we discovered 35 new bugs with 17 CVEs assigne.


### Token-Level Fuzzing (WiSec 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec21-salls.pdf)

**Abstract:** Fuzzing has become a commonly used approach to identifying bugs in complex, real-world programs. However, interpreters are notoriously difficult to fuzz effectively, as they expect highly structured inputs, which are rarely produced by most fuzzing mutations. For this class of programs, grammar-based fuzzing has been shown to be effective. Tools based on this approach can find bugs in the code that is executed after parsing the interpreter inputs, by following language-specific rules when generating and mutating test cases. Unfortunately, grammar-based fuzzing is often unable to discover subtle bugs associated with the parsing and handling of the language syntax. Additionally, if the grammar provided to the fuzzer is incomplete, or does not match the implementation completely, the fuzzer will fail to exercise important parts of the available functionality.

In this paper, we propose a new fuzzing technique, called Token-Level Fuzzing. Instead of applying mutations either at the byte level or at the grammar level, Token-Level Fuzzing applies mutations at the token level. Evolutionary fuzzers can leverage this technique to both generate inputs that are parsed successfully and generate inputs that do not conform strictly to the grammar. As a result, the proposed approach can find bugs that neither byte-level fuzzing nor grammar-based fuzzing can find. We evaluated Token-Level Fuzzing by modifying AFL and fuzzing four popular JavaScript engines, finding 29 previously unknown bugs, several of which could not be found with state-of-the-art byte-level and grammar-based fuzzers.


### Extended grammar-based fuzzing algorithm for JavaScript Engines (2021)

* <img src="image/pdf_24px.png">[Paper](http://www.mathnet.ru/links/df9ecd382b70b1e4bc06d2fe1ecb5f4d/pdma550.pdf)

**Abstract:** JavaScript engine security continues to be critical for user safety. Unfortunately, modern fuzzing algorithms cover only a small part of the entire engine. JavaScript engine requires highly structured input â? JavaScript programs that are syntactically and semantically correct. The most of generated input struggle to pass syntax and semantic correctness checks. In this paper, we describe the extension of the grammar-based fuzzing algorithm. We propose a way of describing grammar for fuzzing using a set of JavaScript source codes. Grammars constructed with our method cover larger part of JavaScript language in comparison with grammars created by describing grammar rules. Another change of the basic algorithm is controlling the context in the mutation process. It allows filtering a lot of inputs that don't give new results. Our experiments show that the improved algorithm has increased speed of finding new paths in the target program.


### Gramatron: Effective Grammar-Aware Fuzzing (ISSTA 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.nebelwelt.net/files/21ISSTA.pdf)

* <img src="image/youtube.png">[Video](https://youtu.be/QKn1zMkLrO8)

**Abstract:** Fuzzers aware of the input grammar can explore deeper program states using grammar-aware mutations. Existing grammar-aware fuzzers are ineffective at synthesizing complex bug triggers due to: (i) grammars introducing a sampling bias during input generation due to their structure, and (ii) the current mutation operators for parse trees performing localized small-scale changes. Gramatron uses grammar automatons in conjunction with aggressive mutation operators to synthesize complex bug triggers faster. We build grammar automatons to address the sampling bias. It restructures the grammar to allow for unbiased sampling from the input state space. We redesign grammar-aware mutation operators to be more aggressive, i.e., perform large-scale changes. Gramatron can consistently generate complex bug triggers in an efficient manner as compared to using conventional grammars with parse trees. Inputs generated from scratch by Gramatron have higher diversity as they achieve up to 24.2% more coverage relative to existing fuzzers. Gramatron makes input generation 98% faster and the input representations are 24% smaller. Our redesigned mutation operators are 6.4Ã more aggressive while still being 68% faster at performing these mutations. We evaluate Gramatron across three interpreters with 10 known bugs consisting of three complex bug triggers and seven simple bug triggers against two Nautilus variants. Gramatron finds all the complex bug triggers reliably and faster. For the simple bug triggers, Gramatron outperforms Nautilus four out of seven times. To demonstrate Gramatronâs effectiveness in the wild, we deployed Gramatron on three popular interpreters for a 10-day fuzzing campaign where it discovered 10 new vulnerabilities.


### One Engine to Fuzz 'em All: Generic Language Processor Testing with Semantic Validation (S&P 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.computer.org/csdl/proceedings-article/sp/2021/893400b217/1t0x95QZOEg)

* <img src="image/youtube.png">[Video](https://youtu.be/tcTbXr8zf-Y)

* <img src="image/github_24px.png">[Code](https://github.com/s3team/Polyglot)

**Abstract:** Language processors, such as compilers and interpreters, are indispensable in building modern software. Errors in language processors can lead to severe consequences, like incorrect functionalities or even malicious attacks. However, it is not trivial to automatically test language processors to find bugs. Existing testing methods (or fuzzers) either fail to generate high-quality (i.e., semantically correct) test cases, or only support limited programming languages. In this paper, we propose POLYGLOT, a generic fuzzing framework that generates high-quality test cases for exploring processors of different programming languages. To achieve the generic applicability, POLYGLOT neutralizes the difference in syntax and semantics of programming languages with a uniform intermediate representation (IR). To improve the language validity, POLYGLOT performs constrained mutation and semantic validation to preserve syntactic correctness and fix semantic errors. We have applied POLYGLOT on 21 popular language processors of 9 programming languages, and identified 173 new bugs, 113 of which are fixed with 18 CVEs assigned. Our experiments show that POLYGLOT can support a wide range of programming languages, and outperforms existing fuzzers with up to 30x improvement in code coverage.


### Growing A Test Corpus with Bonsai Fuzzing (ICSE 2021)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2021/icse-2021-papers/63/Growing-A-Test-Corpus-with-Bonsai-Fuzzing)

**Abstract:** This paper presents a coverage-guided grammar-based fuzzing technique for automatically generating a corpus of concise test inputs for programs such as compilers. We walk-through a case study of a compiler designed for education and the corresponding problem of generating meaningful test cases to provide to students. The prior state-of-the-art solution is a combination of fuzzing and test-case reduction techniques such as variants of delta-debugging. Our key insight is that instead of attempting to minimize convoluted fuzzer-generated test inputs, we can instead grow concise test inputs by construction using a form of iterative deepening. We call this approach Bonsai Fuzzing. Experimental results show that Bonsai Fuzzing can generate test corpora having inputs that are 16â?45% smaller in size on average as compared to a fuzz-then-reduce approach, while achieving approximately the same code coverage and fault-detection capability.


### Favocado: Fuzzing the Binding Code of JavaScript Engines Using Semantically Correct Test Cases (NDSS 2021)

* <img src="image/pdf_24px.png">[Paper](https://sefcom.asu.edu/publications/favocado-ndss21.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/favocado/Favocado)

**Abstract:** JavaScript runtime systems include some specialized programming interfaces, called binding layers. Binding layers translate data representations between JavaScript and unsafe low-level languages, such as C and C++, by converting data between different types. Due to the wide adoption of JavaScript (and JavaScript engines) in the entire computing ecosystem, discovering bugs in JavaScript binding layers is critical. Nonetheless, existing JavaScript fuzzers cannot adequately fuzz binding layers due to two major challenges: Generating syntactically and semantically correct test cases and reducing the size of the input space for fuzzing.

In this paper, we propose Favocado, a novel fuzzing approach that focuses on fuzzing binding layers of JavaScript runtime systems. Favocado can generate syntactically and semantically correct JavaScript test cases through the use of extracted semantic information and careful maintaining of execution states. This way, test cases that Favocado generates do not raise unintended runtime exceptions, which substantially increases the chance of triggering binding code. Additionally, exploiting a unique feature (relative isolation) of binding layers, Favocado significantly reduces the size of the fuzzing input space by splitting DOM objects into equivalence classes and focusing fuzzing within each equivalence class. We demonstrate the effectiveness of Favocado in our experiments and show that Favocado outperforms a stateof-the-art DOM fuzzer. Finally, during the evaluation, we find 61 previously unknown bugs in four JavaScript runtime systems (Adobe Acrobat Reader, Foxit PDF Reader, Chromium, and WebKit). 33 of these bugs are security vulnerabilities.


### CMFuzz: context-aware adaptive mutation for fuzzers (Empirical Software Engineering 2021)

* <img src="image/pdf_24px.png">[Paper](https://link.springer.com/article/10.1007/s10664-020-09927-3)

**Abstract:** Mutation-based fuzzing is a simple yet effective technique to discover bugs and security vulnerabilities in software. Given a set of well-formed initial seeds, mutation-based fuzzers continually generate interesting seeds by applying specific mutation strategy in order to maximize code coverage or the number of unique bugs explored at any point-in-time. However, existing fuzzers remain limited in the paths it could cover since it simply follows a uniform distribution to choose mutation operators. In this paper, we proposed a novel context-aware adaptive mutation scheme, namely CMFuzz, which utilizes a contextual bandit algorithm LinUCB to effectively choose optimal mutation operators for various seed files. To this end, CMFuzz dynamically extracts and encodes file characteristics, which allows mutation-based fuzzers to perform context-aware mutation. We apply this scheme on top of several state-of-the-art fuzzers, i.e., PTfuzz, AFL, and AFLFast, and implement CMFuzz-PT, CMFuzz-AFL, and CMFuzz-AFLFast, respectively. We conduct evaluation on 12 real-world open source applications and LAVA-M dataset against their counterparts. Extensive evaluations demonstrate that CMFuzz-based fuzzers achieve higher code coverage and find more crashes at a faster rate than their counterparts on most cases. Furthermore, we also utilize other mainstream bandit algorithms, e.g., Thompson Sample and epsilon-greedy, and implement Thompson-PT and Greedy-PT based on PTfuzz to examine the performance of proposed model. CMFuzz-PT significantly outperforms Thompson-PT especially in terms of unique crashes and paths, i.e., found 1.79Ã unique crashes and 1.29Ã unique paths on average. Compared to Greedy-PT, our approach still increases the amount of unique crashes and paths by 1.11Ã and 1.05Ã, respectively.


### Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing (ASE 2020)

* <img src="image/pdf_24px.png">[Paper](https://research.tudelft.nl/en/publications/generating-highly-structured-input-data-by-combining-search-based)

**Abstract:** Software testing is an important and time-consuming task that is often done manually. In the last decades, researchers have come up with techniques to generate input data (e.g., fuzzing) and automate the process of generating test cases (e.g., search-based testing). However, these techniques are known to have their own limitations: search-based testing does not generate highly-structured data; grammar-based fuzzing does not generate test case structures. To address these limitations, we combine these two techniques. By applying grammar-based mutations to the input data gathered by the search-based testing algorithm, it allows us to co-evolve both aspects of test case generation. We evaluate our approach, called G-EvoSuite, by performing an empirical study on 20 Java classes from the three most popular JSON parsers across multiple search budgets. Our results show that the proposed approach on average improves branch coverage for JSON related classes by 15% (with a maximum increase of 50%) without negatively impacting other classes.


### Montage: A Neural Network Language Model-Guided JavaScript Engine Fuzzer (Usenix Security2020)

* <img src="image/pdf_24px.png">[Paper](https://www.usenix.org/system/files/sec20summer_lee-suyoung_prepub_0.pdf)

**Abstract:** JavaScript (JS) engine vulnerabilities pose significant security threats affecting billions of web browsers. While fuzzing is a prevalent technique for finding such vulnerabilities, there have been few studies that leverage the recent advances in neural network language models (NNLMs). In this paper, we present Montage, the first NNLM-guided fuzzer for finding JS engine vulnerabilities. The key aspect of our technique is to transform a JS abstract syntax tree (AST) into a sequence of AST subtrees that can directly train prevailing NNLMs. We demonstrate that Montage is capable of generating valid JS tests, and show that it outperforms previous studies in terms of finding vulnerabilities. Montage found 37 real-world bugs, including three CVEs, in the latest JS engines, demonstrating its efficacy in finding JS engine bugs.


### Fuzzing JavaScript Engines with Aspect-preserving Mutation (S&P 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#1Nr9y)
  
* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2020/07/10/fuzzing-javascript-engines-with-aspect-preserving-mutation/)

* <img src="image/pdf_24px.png">[Paper](https://gts3.org/assets/papers/2020/park:die.pdf)

* <img src="image/ppt_24px.png">[Slides](https://gts3.org/assets/papers/2020/park:die-slides.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/sslab-gatech/DIE)

**Abstract:** Fuzzing is a practical, widely-deployed technique to find bugs in complex, real-world programs like JavaScript engines. We observed, however, that existing fuzzing approaches, either generative or mutational, fall short in fully harvesting high-quality input corpora such as known proof of concept (PoC) exploits or unit tests. Existing fuzzers tend to destruct subtle semantics or conditions encoded in the input corpus in order to generate new test cases because this approach helps in discovering new code paths of the program. Nevertheless, for JavaScript-like complex programs, such a conventional design leads to test cases that tackle only shallow parts of the complex codebase and fails to reach deep bugs effectively due to the huge input space. 

In this paper, we advocate a new technique, called an aspect preserving mutation, that stochastically preserves the desirable properties, called aspects, that we prefer to be maintained across mutation. We demonstrate the aspect preservation with two mutation strategies, namely, structure and type preservation, in our fully-fledged JavaScript fuzzer, called DIE. Our evaluation shows that DIEâs aspect-preserving mutation is more effective in discovering new bugs (5.7Ã more unique crashes) and producing valid test cases (2.4Ã fewer runtime errors) than the state-ofthe-art JavaScript fuzzers. DIE newly discovered 48 high-impact bugs in ChakraCore, JavaScriptCore, and V8 (38 fixed with 12 CVEs assigned as of today). The source code of DIE is publicly available as an open-source project.


### Language-Agnostic Generation of Compilable Test Programs (ICST 2020)

* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/document/9159098)

* <img src="image/github_24px.png">[Code](https://github.com/FAU-Inf2/StarSmith)

**Abstract:** Testing is an integral part of the development of compilers and other language processors. To automatically create large sets of test programs, random program generators, or fuzzers, have emerged. Unfortunately, existing approaches are either language-specific (and thus require a rewrite for each language) or may generate programs that violate rules of the respective programming language (which limits their usefulness). This work introduces *Smith, a language-agnostic framework for the generation of valid, compilable test programs. It takes as input an abstract attribute grammar that specifies the syntactic and semantic rules of a programming language. It then creates test programs that satisfy all these rules. By aggressively pruning the search space and keeping the construction as local as possible, *Smith can generate huge, complex test programs in short time. We present four case studies covering four real-world programming languages (C, Lua, SQL, and SMT-LIB 2) to show that *Smith is both efficient and effective, while being flexible enough to support programming languages that differ considerably. We found bugs in all four case studies. For example, *Smith detected 165 different crashes in older versions of GCC and LLVM. *Smith and the language grammars are available online.


### Smart Greybox Fuzzing (TSE 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/TSE19_Smart.pdf)

**Abstract:** Coverage-based greybox fuzzing (CGF) is one of the most successful approaches for automated vulnerability detection. Given a seed file (as a sequence of bits), a CGF randomly flips, deletes or copies some bits to generate new files. CGF iteratively constructs (and fuzzes) a seed corpus by retaining those generated files which enhance coverage. However, random bitflips are unlikely to produce valid files (or valid chunks in files), for applications processing complex file formats. In this work, we introduce smart greybox fuzzing (SGF) which leverages a high-level structural representation of the seed file to generate new files. We define innovative mutation operators that work on the virtual file structure rather than on the bit level which allows SGF to explore completely new input domains while maintaining file validity. We introduce a novel validity-based power schedule that enables SGF to spend more time generating files that are more likely to pass the parsing stage of the program, which can expose vulnerabilities much deeper in the processing logic. Our evaluation demonstrates the effectiveness of SGF. On several libraries that parse complex chunk-based files, our tool AFLSMART achieves substantially more branch coverage (up to 87% improvement), and exposes more vulnerabilities than baseline AFL. Our tool AFLSMART has discovered 42 zero-day vulnerabilities in widely-used, well-tested tools and libraries; so far 17 CVEs were assigned.


### Semantic Fuzzing with Zest (ISSTA 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#h6RGL)

* <img src="image/pdf_24px.png">[Paper](./Paper/ISSTA19_Zest.pdf)

**Abstract:** Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed input and executes the core logic of the program. Generator-based testing tools in the lineage of QuickCheck are a promising way to generate random syntactically valid test inputs for these programs. We present Zest, a technique which automatically guides QuickCheck-like randominput generators to better explore the semantic analysis stage of test programs. Zest converts random-input generators into deterministic parametric generators. We present the key insight that mutations in the untyped parameter domain map to structural mutations in the input domain. Zest leverages program feedback in the form of code coverage and input validity to perform feedback-directed parameter search. We evaluate Zest against AFL and QuickCheck on five Java programs: Maven, Ant, BCEL, Closure, and Rhino. Zest covers 1.03x-2.81x as many branches within the benchmarks semantic analysis stages as baseline techniques. Further, we find 10 new bugs in the semantic analysis stages of these benchmarks. Zest is the most effective technique in finding these bugs reliably and quickly, requiring at most 10 minutes on average to find each bug.


### Field-aware Evolutionary Fuzzing Based on Input Specifications and Vulnerability Metrics (2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/20_Field-aware.pdf.pdf)

**Abstract:** Evolutionary fuzzing technology based on genetic algorithm has become one of the most effective vulnerability discovery techniques due to its fast and scalable advantages. How to effectively mutate the seed input plays a crucial role in improving the efficiency of the fuzzing. A good mutation strategy can increase code coverage and vulnerability triggering probability. Existing fuzzing tools generally focus on how to mutate smartly to improve code coverage to find more vulnerabilities (such as passing the branch with magic bytes), but they still face two challenges which substantially reduces the efficiency of vulnerability discovery. First, the input space is huge and current fuzzers are not aware of the input format, resulting in many mutated inputs are invalid. Second, they believe all bytes are equal and mutate them sequentially, wasting lots of time testing some uninteresting bytes. To this end, this paper proposes a field-aware mutation strategy that can find more vulnerabilities by generating fewer but more effective inputs. Specifically, we extract the field and type information of the seed input through the existing input specifications to ensure that the mutation is performed in field level instead of byte level and the optimal mutation strategy is selected. At the same time, the input fields are scored by code assessment based on vulnerability metrics, thus the more important fields (i.e., fields that are more likely to trigger the vulnerability) are prioritized to be mutated. We implemented a prototype tool, FaFuzzer, and evaluated it on two different datasets consisting of a variety of real-world applications. Experiments show that our field-aware strategy can find more vulnerabilities with fewer inputs than existing tools, while maintaining high code coverage. We found many unknown bugs in five widely used real-world applications and reported them to the relevant vendors.


### Parser-Directed Fuzzing (PLDI 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#KuJuN)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/PLDI19_Parser.pdf)

**Abstract:** To be effective, software test generation needs to well cover the space of possible inputs. Traditional fuzzing generates large numbers of random inputs, which however are unlikely to contain keywords and other specific inputs of non-trivial input languages. Constraint-based test generation solves conditions of paths leading to uncovered code, but fails on programs with complex input conditions because of path explosion.
In this paper, we present a test generation technique specifically directed at input parsers. We systematically produce inputs for the parser and track comparisons made; after every rejection, we satisfy the comparisons leading to rejection. This approach effectively covers the input space: Evaluated on five subjects, from CSV files to JavaScript, our pFuzzer prototype covers more tokens than both random-based and constraint-based approaches, while requiring no symbolic analysis and far fewer tests than random fuzzers.


### GRIMOIRE: Synthesizing Structure while Fuzzing (USENIX Security2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX19_Grimoire.pdf)

**Abstract:** In the past few years, fuzÂ­zing has reÂ­ceiÂ­ved siÂ­gniÂ­fiÂ­cant atÂ­tenÂ­tiÂ­on from the reÂ­seÂ­arch comÂ­muÂ­niÂ­ty. HoweÂ­ver, most of this atÂ­tenÂ­tiÂ­on was diÂ­recÂ­ted towards proÂ­grams wiÂ­thout a deÂ­diÂ­caÂ­ted parÂ­sing stage. In such cases, fuzÂ­zers which leÂ­verÂ­aÂ­ge the input strucÂ­tuÂ­re of a proÂ­gram can achieÂ­ve a siÂ­gniÂ­fiÂ­cantÂ­ly higÂ­her code coÂ­verÂ­aÂ­ge comÂ­paÂ­red to traÂ­diÂ­tioÂ­nal fuzÂ­zing apÂ­proaÂ­ches. This adÂ­vanÂ­ceÂ­ment in coÂ­verÂ­aÂ­ge is achieÂ­ved by apÂ­pÂ­lyÂ­ing larÂ­ge-scaÂ­le muÂ­taÂ­tiÂ­ons in the apÂ­pÂ­liÂ­caÂ­tiÂ­on's input space. HoweÂ­ver, this imÂ­proÂ­veÂ­ment comes at the cost of reÂ­quiÂ­ring exÂ­pert doÂ­main knowÂ­ledge, as these fuzÂ­zers deÂ­pend on strucÂ­tuÂ­re input speÂ­ciÂ­fiÂ­caÂ­tiÂ­ons (e.g., gramÂ­mars). GramÂ­mar inÂ­feÂ­rence, a techÂ­niÂ­que which can auÂ­toÂ­maÂ­tiÂ­calÂ­ly geÂ­neÂ­raÂ­te such gramÂ­mars for a given proÂ­gram, can be used to adÂ­dress this shortÂ­coÂ­ming. Such techÂ­niÂ­ques usualÂ­ly infer a proÂ­gram's gramÂ­mar in a pre-proÂ­cesÂ­sing step and can miss imÂ­portant strucÂ­tuÂ­res that are unÂ­coÂ­verÂ­ed only later duÂ­ring norÂ­mal fuzÂ­zing.
In this paper, we preÂ­sent the deÂ­sign and imÂ­pleÂ­menÂ­taÂ­tiÂ­on of GRIÂ­MOIRE, a fully auÂ­toÂ­maÂ­ted coÂ­verÂ­aÂ­ge-guiÂ­ded fuzÂ­zer which works wiÂ­thout any form of human inÂ­terÂ­acÂ­tion or pre-conÂ­fiÂ­guÂ­raÂ­tiÂ­on; yet, it is still able to efÂ­fiÂ­ciÂ­entÂ­ly test proÂ­grams that exÂ­pect highÂ­ly strucÂ­tuÂ­red inÂ­puts. We achieÂ­ve this by perÂ­forÂ­ming larÂ­ge-scaÂ­le muÂ­taÂ­tiÂ­ons in the proÂ­gram input space using gramÂ­mar-liÂ­ke comÂ­biÂ­naÂ­tiÂ­ons to synÂ­theÂ­siÂ­ze new highÂ­ly strucÂ­tuÂ­red inÂ­puts wiÂ­thout any pre-proÂ­cesÂ­sing step. Our evaÂ­luaÂ­tiÂ­on shows that GRIÂ­MOIRE outÂ­perÂ­forms other coÂ­verÂ­aÂ­ge-guiÂ­ded fuzÂ­zers when fuzÂ­zing proÂ­grams with highÂ­ly strucÂ­tuÂ­red inÂ­puts. FurÂ­therÂ­moÂ­re, it imÂ­proÂ­ves upon exisÂ­ting gramÂ­mar-baÂ­sed coÂ­verÂ­aÂ­ge-guiÂ­ded fuzÂ­zers. Using GRIÂ­MOIRE, we idenÂ­tiÂ­fied 19 disÂ­tinct meÂ­moÂ­ry corÂ­rupÂ­tiÂ­on bugs in reÂ­al-world proÂ­grams and obÂ­tained 11 new CVEs.


### Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice Assistant Applications (NDSS 2019)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS19_Life.pdf)

**Abstract:** Popular Voice Assistant (VA) services such as Amazon Alexa and Google Assistant are now rapidly appifying their platforms to allow more flexible and diverse voice-controlled service experience. However, the ubiquitous deployment of VA devices and the increasing number of third-party applications have raised security and privacy concerns. While previous works such as hidden voice attacks mostly examine the problems of VA servicesâ? default Automatic Speech Recognition (ASR) component, our work analyzes and evaluates the security of the succeeding component after ASR, i.e., Natural Language Understanding (NLU), which performs semantic interpretation (i.e., text-to-intent) after ASRâs acoustic-to-text processing. In particular, we focus on NLUâs Intent Classifier which is used in customizing machine understanding for third-party VA Applications (or vApps). We find that the semantic inconsistency caused by the improper semantic interpretation of an Intent Classifier can create the opportunity of breaching the integrity of vApp processing when attackers delicately leverage some common spoken errors.
In this paper, we design the first linguistic-model-guided fuzzing tool, named LipFuzzer, to assess the security of Intent Classifier and systematically discover potential misinterpretation-prone spoken errors based on vAppsâ? voice command templates. To guide the fuzzing, we construct adversarial linguistic models with the help of Statistical Relational Learning (SRL) and emerging Natural Language Processing (NLP) techniques. In evaluation, we have successfully verified the effectiveness and accuracy of LipFuzzer. We also use LipFuzzer to evaluate both Amazon Alexa and Google Assistant vApp platforms. We have identified that a large portion of real-world vApps are vulnerable based on our fuzzing result.


### SLF: Fuzzing without Valid Seed Inputs (ICSE 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#mhYUc)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE19_SLF.pdf)

**Abstract:** Fuzzing is an important technique to detect software bugs and vulnerabilities. It works by mutating a small set of seed inputs to generate a large number of new inputs. Fuzzersâ? performance often substantially degrades when valid seed inputs are not available. Although existing techniques such as symbolic execution can generate seed inputs from scratch, they have various limitations hindering their applications in real-world complex software without source code. In this paper, we propose a novel fuzzing technique that features the capability of generating valid seed inputs. It piggy-backs on AFL to identify input validity checks and the input fields that have impact on such checks. It further classifies these checks according to their relations to the input. Such classes include arithmetic relation, object offset, data structure length and so on. A multi-goal search algorithm is developed to apply class specific mutations in order to satisfy inter-dependent checks all together. We evaluate our technique on 20 popular benchmark programs collected from other fuzzing projects and the Google fuzzer test suite, and compare it with existing fuzzers AFL and AFLFast, symbolic execution engines KLEE and S2E, and a hybrid tool Driller that combines fuzzing with symbolic execution. The results show that our technique is highly effective and efficient, out-performing the other tools.


### Superion: Grammar-Aware Greybox Fuzzing (ICSE 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#uJyov)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE19_Superion.pdf)

**Abstract:** In recent years, coverage-based greybox fuzzing has proven itself to be one of the most effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop (AFL for short) is deemed to be a great success in fuzzing relatively simple test inputs. Unfortunately, when it meets structured test inputs such as XML and JavaScript, those grammar-blind trimming and mutation strategies in AFL hinder the effectiveness and efficiency. To this end, we propose a grammar-aware coverage-based grey-box fuzzing approach to fuzz programs that process structured inputs. Given the grammar (which is often publicly available) of test inputs, we introduce a grammar-aware trimming strategy to trim test inputs at the tree level using the abstract syntax trees (ASTs) of parsed test inputs. Further, we introduce two grammar-aware mutation strategies (i.e., enhanced dictionary-based mutation and tree-based mutation). Specifically, tree-based mutation works via replacing subtrees using the ASTs of parsed test inputs. Equipped with grammar-awareness, our approach can carry the fuzzing exploration into width and depth. We implemented our approach as an extension to AFL, named Superion; and evaluated the effectiveness of Superion on real-life large-scale programs (a XML engine libplist and three JavaScript engines WebKit, Jerryscript and ChakraCore). Our results have demonstrated that Superion can improve the code coverage (i.e., 16.7% and 8.8% in line and function coverage) and bug-finding capability (i.e., 30 new bugs, among which we discovered 21 new vulnerabilities with 16 CVEs assigned and 3.2K USD bug bounty rewards received) over AFL and jsfunfuzz.


### ProFuzzer: On-the-fly Input Type Probing for Better Zero-day Vulnerability Discovery (S&P 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#u2wje)
  
* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/130901035)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP19_Profuzz.pdf)

* <img src="image/ppt_24px.png">[Slides](https://www.inforsec.org/wp/wp-content/uploads/2020/01/%E6%B8%B8%E4%BC%9F-ProFuzzer-Wei-You.pdf)

* <img src="image/github_24px.png">[POC](https://github.com/profuzzer)

**Abstract:** Existing mutation based fuzzers tend to randomly mutate the input of a program without understanding its underlying syntax and semantics. In this paper, we propose a novel on-the-fly probing technique (called ProFuzzer) that automatically recovers and understands input fields of critical importance to vulnerability discovery during a fuzzing process and intelligently adapts the mutation strategy to enhance the chance of hitting zero-day targets. Since such probing is transparently piggybacked to the regular fuzzing, no prior knowledge of the input specification is needed. During fuzzing, individual bytes are first mutated and their fuzzing results are automatically analyzed to link those related together and identify the type for the field connecting them; these bytes are further mutated together following type-specific strategies, which substantially prunes the search space. We define the probe types generally across all applications, thereby making our technique application agnostic. Our experiments on standard benchmarks and real-world applications show that ProFuzzer substantially outperforms AFL and its optimized version AFLFast, as well as other state-of-art fuzzers including VUzzer, Driller and QSYM. Within two months, it exposed 42 zero-days in 10 intensively tested programs, generating 30 CVEs.


### CodeAlchemist: Semantics-Aware Code Generation to Find Vulnerabilities in JavaScript Engines (NDSS 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#CfBUR)
  
* <img src="image/blog_24px.jpg">[Reading Note from dawuge](https://dawuge.github.io/2019/10/31/CodeAlchemistandT-Fuzz/)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS19_CodeAlchemist.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/NDSS19_CodeAlchemist_slidesz.pdf)

**Abstract:** JavaScript engines are an attractive target for attackers due to their popularity and flexibility in building exploits. Current state-of-the-art fuzzers for finding JavaScript engine vulnerabilities focus mainly on generating syntactically correct test cases based on either a predefined context-free grammar or a trained probabilistic language model. Unfortunately, syntactically correct JavaScript sentences are often semantically invalid at runtime. Furthermore, statically analyzing the semantics of JavaScript code is challenging due to its dynamic nature: JavaScript code is generated at runtime, and JavaScript expressions are dynamically-typed. To address this challenge, we propose a novel test case generation algorithm that we call semantics-aware assembly, and implement it in a fuzz testing tool termed CodeAlchemist. Our tool can generate arbitrary JavaScript code snippets that are both semantically and syntactically correct, and it effectively yields test cases that can crash JavaScript engines. We found numerous vulnerabilities of the latest JavaScript engines with CodeAlchemist and reported them to the vendors.


### NAUTILUS: Fishing for Deep Bugs with Grammars (NDSS 2019)

* <img src="image/wechat_24px.png">[Reading Note From BAIZE](https://mp.weixin.qq.com/s/8TaYwNTeNsAMFr0UNNNMHg)

* <img src="image/pdf_24px.png">[Paper](./Paper/NDSS19_Nautilus.pdf)

* <img src="image/ppt_24px.png">[Slides](https://www.ndss-symposium.org/wp-content/uploads/ndss2019_04A-3_Aschermann_slides.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/RUB-SysSec/nautilus)

**Abstract:** Fuzzing is a well-known method for efficiently identifying bugs in programs.Unfortunately, when fuzzing targets that require highly-structured inputs such as interpreters, many fuzzing methods struggle to pass the syntax checks.
More specifically, interpreters often process inputs in multiple stages: first syntactic, then semantic correctness is checked. Only if these checks are passed, the interpreted code gets executed.
This prevents fuzzers from executing ``deeper'' --- and hence potentially more interesting --- code.
Typically two valid inputs that lead to the execution of different features in the target application require too many mutations for simple mutation-based fuzzers to discover: making small changes like bit flips usually only leads to the execution of error paths in the parsing engine.
So-called grammar fuzzers are able to pass the syntax checks by using Context-Free Grammars.
Using feedback can significantly increase the efficiency of fuzzing engines.
Hence, it is commonly used in state-of-the-art mutational fuzzers that do not use grammars.
Yet, grammar fuzzers do not make use of code coverage, i.e., they do not know whether any input triggers new functionality or not.

In this paper, we propose NAUTILUS, a method to efficiently fuzz programs that require highly-structured inputs by combining the use of grammars with the use of code coverage feedback.
This allows us to recombine aspects of interesting inputs that were learned individually, and to dramatically increase the probability that any generated input will be accepted by the parser.
We implemented a proof-of-concept fuzzer that we tested on multiple targets, including ChakraCore (the JavaScript engine of Microsoft Edge), PHP, mruby, and Lua.
NAUTILUS identified multiple bugs in all of the targets: Seven in mruby, three in PHP, two in ChakraCore, and one in Lua.
Reporting these bugs was awarded with a sum of 2600 USD and 6 CVEs were assigned.
Our experiments show that combining context-free grammars and feedback-driven fuzzing significantly outperforms state-of-the-art approaches like American Fuzzy Lop (AFL) by an order of magnitude and grammar fuzzers by more than a factor of two when measuring code coverage.


### TIFF: Using Input Type Inference To Improve Fuzzing (ACSAC 2018)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#M4YZI)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/ACSAC18_TIFF.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/vusec/tiff)

**Abstract:** Developers commonly use fuzzing techniques to hunt down all manner of memory corruption vulnerabilities during the testing phase. Irrespective of the fuzzer, input mutation plays a central role in providing adequate code coverage, as well as in triggering bugs. However, each class of memory corruption bugs requires a different trigger condition. While the goal of a fuzzer is to find bugs, most existing fuzzers merely approximate this goal by targeting their mutation strategies toward maximizing code coverage. 

In this work, we present a new mutation strategy that maximizes the likelihood of triggering memory-corruption bugs by generating fewer, but better inputs. In particular, our strategy achieves bug- directed mutation by inferring the type of the input bytes. To do so, it tags each offset of the input with a basic type (e.g., 32-bit integer, string, array etc.), while deriving mutation rules for specific classes of bugs, We infer types by means of in-memory data-structure identification and dynamic taint analysis, and implement our novel mutation strategy in a fully functional fuzzer which we call TIFF (Type Inference-based Fuzzing Framework). Our evaluation on real-world applications shows that type-based fuzzing triggers bugs much earlier than existing solutions, while maintaining high code coverage. For example, on several real-world applications and libraries (e.g., poppler, mpg123 etc.), we find real bugs (with known CVEs) in almost half of the time and upto an order of magnitude fewer inputs than state-of-the-art fuzzers.


### Skyfire: Data-Driven Seed Generation for Fuzzing (S&P 2017)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP17_Skyfire.pdf)

**Abstract:** Programs that take highly-structured files as inputs normally process inputs in stages: syntax parsing, semantic checking, and application execution. Deep bugs are often hidden in the application execution stage, and it is non-trivial to automatically generate test inputs to trigger them. Mutation-based fuzzing generates test inputs by modifying well-formed seed inputs randomly or heuristically. Most inputs are rejected at the early syntax parsing stage. Differently, generation-based fuzzing generates inputs from a specification (e.g., grammar). They can quickly carry the fuzzing beyond the syntax parsing stage. However, most inputs fail to pass the semantic checking (e.g., violating semantic rules), which restricts their capability of discovering deep bugs. In this paper, we propose a novel data-driven seed generation approach, named Skyfire, which leverages the knowledge in the vast amount of existing samples to generate well-distributed seed inputs for fuzzing programs that process highly-structured inputs. Skyfire takes as inputs a corpus and a grammar, and consists of two steps. The first step of Skyfire learns a probabilistic context-sensitive grammar (PCSG) to specify both syntax features and semantic rules, and then the second step leverages the learned PCSG to generate seed inputs. We fed the collected samples and the inputs generated by Skyfire as seeds of AFL to fuzz several open-source XSLT and XML engines (i.e., Sablotron, libxslt, and libxml2). The results have demonstrated that Skyfire can generate well-distributed inputs and thus significantly improve the code coverage (i.e., 20% for line coverage and 15% for function coverage on average) and the bug-finding capability of fuzzers. We also used the inputs generated by Skyfire to fuzz the closed-source JavaScript and rendering engine of Internet Explorer 11. Altogether, we discovered 19 new memory corruption bugs (among which there are 16 new vulnerabilities and received 33.5k USD bug bounty rewards) and 32 denial-of-service bugs.



# Exploit Generation

### ETHPLOIT: From Fuzzing to Efficient Exploit Generation against Smart Contracts (SANER2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/SANER20_ETHPLOIT.pdf)

* <img src="image/ppt_24px.png">[Slides](https://loccs.sjtu.edu.cn/~romangol/slides/saner20b.pdf)

**Abstract:** Smart contracts, programs running on blockchain systems, leverage diverse decentralized applications (DApps). Unfortunately, well-known smart contract platforms, Ethereum for example, face serious security problems. Exploits to contracts may cause enormous financial losses, which emphasize the importance of smart contract testing. However, current exploit generation tools have difficulty to solve hard constraints in execution paths and cannot simulate the blockchain behaviors very well. These problems cause a loss of coverage and accuracy of exploit generation.

To overcome the problems, we design and implement ETHPLOIT, a smart contract exploit generator based on fuzzing. ETHPLOIT adopts static taint analysis to generate exploit targeted transaction sequences, a dynamic seed strategy to pass hard constraints and an instrumented Ethereum Virtual Machine to simulate blockchain behaviors. We evaluate ETHPLOIT on 45,308 smart contracts and discovered 554 exploitable contracts. ETHPLOIT automatically generated 644 exploits without any false positive and 306 of them cannot be generated by previous exploit generation tools.


### Gollum: Modular and Greybox Exploit Generation for Heap Overflows in Interpreters (CCS 2019)

* <img src="image/pdf_24px.png">[Paper](https://www.kroening.com/papers/ccs2019.pdf)

**Abstract:** We present the first approach to automatic exploit generation for heap overflows in interpreters. It is also the first approach to exploit generation in any class of program that integrates a solution for automatic heap layout manipulation. At the core of the approach is a novel method for discovering exploit primitives---inputs to the target program that result in a sensitive operation, such as a function call or a memory write, utilizing attacker-injected data. To produce an exploit primitive from a heap overflow vulnerability, one has to discover a target data structure to corrupt, ensure an instance of that data structure is adjacent to the source of the overflow on the heap, and ensure that the post-overflow corrupted data is used in a manner desired by the attacker. Our system addresses all three tasks in an automatic, greybox, and modular manner. Our implementation is called GOLLUM, and we demonstrate its capabilities by producing exploits from 10 unique vulnerabilities in the PHP and Python interpreters, 5 of which do not have existing public exploits.


### From proof-of-concept to exploitable (Cybersecurity 2019)

* <img src="image/pdf_24px.png">[Paper](https://cybersecurity.springeropen.com/track/pdf/10.1186/s42400-019-0028-9)

* <img src="image/ppt_24px.png">[Slides](./Paper/CCS18_Revery_Slides)

* <img src="image/youtube.png">[Video](https://www.youtube.com/watch?v=QsG_nTBSN4c)

**Abstract:** Exploitability assessment of vulnerabilities is important for both defenders and attackers. The ultimate way to assess the exploitability is crafting a working exploit. However, it usually takes tremendous hours and significant manual efforts. To address this issue, automated techniques can be adopted. Existing solutions usually explore in depth the crashing paths, i.e., paths taken by proof-of-concept (PoC) inputs triggering vulnerabilities, and assess exploitability by finding exploitable states along the paths. However, exploitable states do not always exist in crashing paths. Moreover, existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit generation.

In this paper, we propose a novel solution to generate exploit for userspace programs or facilitate the process of crafting a kernel UAF exploit. Technically, we utilize oriented fuzzing to explore diverging paths from vulnerability point. For userspace programs, we adopt a control-flow stitching solution to stitch crashing paths and diverging paths together to generate exploit. For kernel UAF, we leverage a lightweight symbolic execution to identify, analyze and evaluate the system calls valuable and useful for exploiting vulnerabilities.

We have developed a prototype system and evaluated it on a set of 19 CTF (capture the flag) programs and 15 realworld Linux kernel UAF vulnerabilities. Experiment results showed it could generate exploit for most of the userspace test set, and it could also facilitate security mitigation bypassing and exploitability evaluation for kernel test set.


### Revery: From Proof-of-Concept to Exploitable (CCS 2018)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS18_Revery.pdf)

* <img src="image/ppt_24px.png">[Slides](./Paper/CCS18_Revery_Slides)

* <img src="image/youtube.png">[Video](https://www.youtube.com/watch?v=QsG_nTBSN4c)

**Abstract:** Automatic exploit generation is an open challenge. Existing solutions usually explore in depth the crashing paths, i.e., paths taken by proof-of-concept (POC) inputs triggering vulnerabilities, and generate exploits when exploitable states are found along the paths. However, exploitable states do not always exist in crashing paths. Moreover, existing solutions heavily rely on symbolic execution and are not scalable in path exploration and exploit generation. In addition, few solutions could exploit heap-based vulnerabilities. In this paper, we propose a new solution revery to search for exploitable states in paths diverging from crashing paths, and generate control-flow hijacking exploits for heap-based vulnerabilities. It adopts three novel techniques:(1) a digraph to characterize a vulnerability's memory layout and its contributor instructions;(2) a fuzz solution to explore diverging paths, which have similar memory layouts as the crashing paths, in order to search more exploitable states and generate corresponding diverging inputs;(3) a stitch solution to stitch crashing paths and diverging paths together, and synthesize EXP inputs able to trigger both vulnerabilities and exploitable states. We have developed a prototype of revery based on the binary analysis engine angr, and evaluated it on a set of 19 real world CTF (capture the flag) challenges. Experiment results showed that it could generate exploits for 9 (47%) of them, and generate EXP inputs able to trigger exploitable states for another 5 (26%) of them.


### SemFuzz: Semantics-based Automatic Generation of Proof-of-Concept Exploits (CCS 2017)

* <img src="image/pdf_24px.png">[Paper](./Paper/CCS17_SemFuzz.pdf)

**Abstract:** Patches and related information about software vulnerabilities are often made available to the public, aiming to facilitate timely fixes. Unfortunately, the slow paces of system updates (30 days on average) often present to the attackers enough time to recover hidden bugs for attacking the unpatched systems. Making things worse is the potential to automatically generate exploits on input-validation flaws through reverse-engineering patches, even though such vulnerabilities are relatively rare (e.g., 5% among all Linux kernel vulnerabilities in last few years). Less understood, however, are the implications of other bug-related information (e.g., bug descriptions in CVE), particularly whether utilization of such information can facilitate exploit generation, even on other vulnerability types that have never been automatically attacked. In this paper, we seek to use such information to generate proof-of-concept (PoC) exploits for the vulnerability types never automatically attacked. Unlike an input validation flaw that is often patched by adding missing sanitization checks, fixing other vulnerability types is more complicated, usually involving replacement of the whole chunk of code. Without understanding of the code changed, automatic exploit becomes less likely. To address this challenge, we present SemFuzz, a novel technique leveraging vulnerability-related text (e.g., CVE reports and Linux git logs) to guide automatic generation of PoC exploits. Such an end-to-end approach is made possible by natural-language processing (NLP) based information extraction and a semantics-based fuzzing process guided by such information. Running over 112 Linux kernel flaws reported in the past five years, SemFuzz successfully triggered 18 of them, and further discovered one zero-day and one undisclosed vulnerabilities. These flaws include use-after-free, memory corruption, information leak, etc., indicating that more complicated flaws can also be automatically attacked. This finding calls into question the way vulnerability-related information is shared today.

### ExploitMeter: Combining Fuzzing with Machine Learning for Automated Evaluation of Software Exploitability (PAC 2017)

* <img src="image/pdf_24px.png">[Paper](./Paper/PAC17_ExploitMeter.pdf)

**Abstract:** Exploitable software vulnerabilities pose severe threats to its information security and privacy. Although a great amount of efforts have been dedicated to improving software security, research on quantifying software exploitability is still in its infancy. In this work, we propose ExploitMeter, a fuzzing-based framework of quantifying software exploitability that facilitates decision-making for software assurance and cyber insurance. Designed to be dynamic, efficient and rigorous, ExploitMeter integrates machine learning-based prediction and dynamic fuzzing tests in a Bayesian manner. Using 100 Linux applications, we conduct extensive experiments to evaluate the performance of ExploitMeter in a dynamic environment.



# Parallel / Ensemble Fuzzing

### UltraFuzz: Towards Resource-saving in Distributed Fuzzing (TSE 2022)
* <img src="image/pdf_24px.png">[Paper](https://ieeexplore.ieee.org/abstract/document/9939114)

* <img src="image/gitlink_24px.png">[Code](https://gitlink.org.cn/hunter-2018/Ultrafuzz)

**Abstract:** Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment amplifies the resource-wasting problem caused by the random nature of fuzzing. In the parallel mode, owing to the lack of an appropriate task dispatching scheme and timely fuzzing status synchronization among different fuzzing instances, task conflicts and workload imbalance occur, making the resource-wasting problem severe. In this paper, we design UltraFuzz, a fuzzer for resource-saving in distributed fuzzing. Based on centralized dynamic scheduling, UltraFuzz can dispatch tasks and schedule power globally and reasonably to avoid resource-wasting. Besides, UltraFuzz can elastically allocate computing power for fuzzing and seed evaluation, thereby avoiding the potential bottleneck of seed evaluation that blocks the fuzzing process. UltraFuzz was evaluated using real-world programs, and the results show that with the same testing resource, UltraFuzz outperforms state-of-the-art tools, such as AFL, AFL-P, PAFL, and EnFuzz. Most importantly, the experiment reveals certain results that seem counter-intuitive, namely that parallel fuzzing can achieve "super-linear acceleration" when compared with single-core fuzzing. We conduct additional experiments to reveal the deep reasons behind this phenomenon and dig deep into the inherent advantages of parallel fuzzing over serial fuzzing, including the global optimization of seed energy scheduling and the escape of local optimal seed. Additionally, 24 real-world vulnerabilities were discovered using UltraFuzz.

### Towards Systematic and Dynamic Task Allocation for Collaborative Parallel Fuzzing (ASE 2021 NIER)

* <img src="image/pdf_24px.png">[Paper](https://thuanpv.github.io/publications/AFLTeam-ASE21-NIER.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/MelbourneFuzzingHub/aflteam)

**Abstract:** Parallel coverage-guided greybox fuzzing is the most common setup for vulnerability discovery at scale. However, so far it has received little attention from the research community compared to single-mode fuzzing, leaving open several problems particularly in its task allocation strategies. Current approaches focus on managing micro tasks, at the seed input level, and their task division algorithms are either ad-hoc or static. In this paper, we leverage research on graph partitioning and search algorithms to propose a systematic and dynamic task allocation solution that works at the macro-task level. First, we design an attributed graph to capture both the program structures (e.g., program call graph) and fuzzing information (e.g., branch hit counts, bug discovery probability). Second, our graph partitioning algorithm divides the global program search space into sub-search-spaces. Finally our search algorithm prioritizes these sub-search-spaces (i.e., tasks) and explores them to maximize code coverage and number of bugs found. The results are collected to update the graph and guide further iterations of partitioning and exploration. We implemented a prototype tool called AFLTeam. In our preliminary experiments on well-tested benchmarks, AFLTeam achieved higher code coverage (up to 16.4% branch coverage improvement) compared to the default parallel mode of AFL and discovered 2 zero-day bugs in FFmpeg and JasPer toolkits.


### CollabFuzz: A Framework for Collaborative Fuzzing (EuroSec 2021)

* <img src="image/pdf_24px.png">[Paper](./Paper/Eurosec21_Collabfuzz.pdf)

**Abstract:** In the recent past, there has been lots of work on improving fuzz testing. In prior work, EnFuzz showed that by sharing progress among different fuzzers, they can perform better than the sum of their parts. In this paper, we continue this line of work and present CollabFuzz, a collaborative fuzzing framework allowing multiple different fuzzers to collaborate under an informed scheduling policy based on a number of central analyses. More specifically, CollabFuzz is a generic framework that allows a user to express different test case scheduling policies, such as the collaborative approach presented by EnFuzz. CollabFuzz can control which tests cases are handed out to what fuzzer and allows the orchestration of different fuzzers across the network. Furthermore, it allows the centralized analysis of the test cases generated by the various fuzzers under its control, allowing to implement scheduling policies based on the results of arbitrary program (e.g., data-flow) analysis.


### Improving Web Application Vulnerability Detection Leveraging Ensemble Fuzzing (ENASE 2021)

* <img src="image/pdf_24px.png">[Paper](http://www.di.fc.ul.pt/~imedeiros/papers/ENASE2021_Fuzzing.pdf)

**Abstract:** The vast majority of online services we use nowadays provide their web application to the users. The correctness of the source code of these applications is crucial to prevent attackers from exploiting its vulnerabilities, leading to severe consequences like the disclosure of sensitive information or the degradation of the availability of the application. Currently, multiple existent solutions analyse and detect vulnerabilities in the source code. Attackers, however, do not usually have access to the source code and must work with the information that is made public. Their goals are clear â? exploit vulnerabilities without accessing the code â?, and they resort of black-box fuzzing tools to achieve such. In this paper, we propose an ensemble fuzzing approach to check the correctness of the web applications from the point of view of an attacker and, in a posterior phase, analyse the source code to correlate with the collected information. The approach focuses first on the quality of fuzzersâ? crawlers and afterwards on fuzzers capabilities of exploiting the results of all crawlers between them, in order to provide better coverage and precision in the detection of web vulnerabilities. Our preliminary results show that the ensemble performs better than fuzzers individually.


### Cupid: Automatic Fuzzer Selection for Collaborative Fuzzing (ACSAC 2020)

* <img src="image/pdf_24px.png">[Paper](https://www.syssec.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2020/09/26/ACSAC20-Cupid_TiM9H07.pdf)

**Abstract:** Combining the strengths of individual fuzzing methods is an appealing idea to find software faults more efficiently, especially when the computing budget is limited. In prior work, EnFuzz introduced the idea of ensemble fuzzing and devised three heuristics to classify properties of fuzzers in terms of diversity. Based on these heuristics, the authors manually picked a combination of different fuzzers that collaborate.

In this paper, we generalize this idea by collecting and applying empirical data from single, isolated fuzzer runs to automatically identify a set of fuzzers that complement each other when executed collaboratively. To this end, we present Cupid, a collaborative fuzzing framework allowing automated, data-driven selection of multiple complementary fuzzers for parallelized and distributed fuzzing. We evaluate the automatically selected target-independent combination of fuzzers by Cupid on Googleâs fuzzer-test-suite, a collection of real-world binaries, as well as on the synthetic Lava-M dataset. We find that Cupid outperforms two expert-guided, targetspecific and hand-picked combinations on Googleâs fuzzer-test-suite in terms of branch coverage, and improves bug finding on Lava-M by 10%. Most importantly, we improve the latency for obtaining 95% and 99% of the coverage by 90% and 64%, respectively. Furthermore, Cupid reduces the amount of CPU hours needed to find a high-performing combination of fuzzers by multiple orders of magnitude compared to an exhaustive evaluation.


### EnFuzz: Ensemble Fuzzing with Seed Synchronization among Diverse Fuzzers (USENIX Security2019)

* <img src="image/wechat_24px.png">[Reading Note](https://mp.weixin.qq.com/s/wjp-54oevmK4XNAuEi_AnA)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX19_EnFuzz.pdf)

**Abstract:** Fuzzing is widely used for software vulnerability detection. There are various kinds of fuzzers with different fuzzing strategies, and most of them perform well on their targets. However, in industry practice and empirical study, the performance and generalization ability of those well-designed fuzzing strategies are challenged by the complexity and diversity of real-world applications. 
In this paper, inspired by the idea of ensemble learning, we first propose an ensemble fuzzing approach EnFuzz, that integrates multiple fuzzing strategies to obtain better performance and generalization ability than that of any constituent fuzzer alone. First, we define the diversity of the base fuzzers and choose those most recent and well-designed fuzzers as base fuzzers. Then, EnFuzz ensembles those base fuzzers with seed synchronization and result integration mechanisms. For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications. The 24-hour experiment indicates that, with the same resources usage, these four base fuzzers perform variously on different applications, while EnFuzz shows better generalization ability and always outperforms others in terms of path coverage, branch coverage and crash discovery. Even compared with the best cases of AFL, AFLFast, AFLGo and FairFuzz, EnFuzz discovers 26.8%, 117%, 38.8% and 39.5% more unique crashes, executes 9.16%, 39.2%, 19.9% and 20.0% more paths and covers 5.96%, 12.0%, 21.4% and 11.1% more branches respectively.


### PAFL: Extend FuzzingOptimizations of Single Mode to Industrial Parallel Mode (ESEC/FSE 2018)

* <img src="image/wechat_24px.png">[Reading Note](https://mp.weixin.qq.com/s/wjp-54oevmK4XNAuEi_AnA)

* <img src="image/pdf_24px.png">[Paper](http://wingtecher.com/themes/WingTecherResearch/assets/papers/fse18-pafl.pdf)

**Abstract:** Researchers have proposed many optimizations to improve the efficiency of fuzzing, and most optimized strategies work very well on their targets when running in single mode with instantiating one fuzzer instance. However, in real industrial practice, most fuzzers run in parallel mode with instantiating multiple fuzzer instances, and those optimizations, unfortunately, fail to maintain the efficiency improvements.

In this paper, we present PAFL, a framework that utilizes efficient guiding information synchronization and task division to extend those existing fuzzing optimizations of single-mode to industrial parallel mode. With an additional data structure to store the guiding information, the synchronization ensures the information is shared and updated among different fuzzer instances timely. Then, the task division promotes the diversity of fuzzer instances by splitting the fuzzing task into several sub-tasks based on branch bitmap. We first evaluate PAFL using 12 different real-world programs from Google fuzzer-test-suite. Results show that in parallel mode, two AFL improversâAFLFast and FairFuzz do not outperform AFL, which is different from the case in a single mode. However, when augmented with PAFL, the performance of AFLFast and FairFuzz in parallel mode improves. They cover 8% and 17% more branches, trigger 79% and 52% more unique crashes. For further evaluation of more widely-used software systems from GitHub, optimized fuzzers augmented with PAFL find more real bugs, and 25 of which are security-critical vulnerabilities registered as CVEs in the US National Vulnerability Database.



# Sanitizer-guided Fuzzing

### ParmeSan: Sanitizer-guided Greybox Fuzzing (USENIX Security2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#TieIo)
  
* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2020/09/04/parmesan-sanitizer-guided-greybox-fuzzing/)

* <img src="image/pdf_24px.png">[Paper](./Paper/USENIX20_ParmeSan.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/vusec/parmesan)

**Abstract:** One of the key questions when fuzzing is where to look for vulnerabilities. Coverage-guided fuzzers indiscriminately optimize for covering as much code as possible given that bug coverage often correlates with code coverage. Since code coverage overapproximates bug coverage, this approach is less than ideal and may lead to non-trivial time-to-exposure (TTE) of bugs. Directed fuzzers try to address this problem by directing the fuzzer to a basic block with a potential vulnerability. This approach can greatly reduce the TTE for a specific bug, but such special-purpose fuzzers can then greatly underapproximate overall bug coverage.

In this paper, we present sanitizer-guided fuzzing, a new design point in this space that specifically optimizes for bug coverage. For this purpose, we make the key observation that while the instrumentation performed by existing software sanitizers are regularly used for detecting fuzzer-induced error conditions, they can further serve as a generic and effective mechanism to identify interesting basic blocks for guiding fuzzers. We present the design and implementation of ParmeSan, a new sanitizer-guided fuzzer that builds on this observation. We show that ParmeSan greatly reduces the TTE of real-world bugs, and finds bugs 37% faster than existing state-of-the-art coverage-based fuzzers (Angora) and 288% faster than directed fuzzers (AFLGo), while still covering the same set of bugs.


# State / Sequence Guided Fuzzing

### Stateful Greybox Fuzzing (USENIX Security 2022)

* <img src="image/pdf_24px.png">[Paper](https://mboehme.github.io/paper/USENIX22.pdf)

**Abstract:** Many protocol implementations are reactive systems, where the protocol process is in continuous interaction with other processes and the environment. If a bug can be exposed only in a certain state, a fuzzer needs to provide a specific sequence of events as inputs that would take protocol into this state before the bug is manifested. We call these bugs as "stateful" bugs. Usually, when we are testing a protocol implementation, we do not have a detailed formal specification of the protocol to rely upon. Without knowledge of the protocol, it is inherently difficult for a fuzzer to discover such stateful bugs. A key challenge then is to cover the state space without an explicit specification of the protocol.
In this work, we posit that manual annotations for state identification can be avoided for stateful protocol fuzzing. Specifically, we rely on a programmatic intuition that the state variables used in protocol implementations often appear in enum type variables whose values (the state names) come from named constants. In our analysis of the Top-50 most widely used open-source protocol implementations, we found that every implementation uses state variables that are assigned named constants (with easy to comprehend names such as INIT, READY) to represent the current state. In this work, we propose to automatically identify such state variables and track the sequence of values assigned to them during fuzzing to produce a "map" of the explored state space.
Our experiments confirm that our stateful fuzzer discovers stateful bugs twice as fast as the baseline greybox fuzzer that we extended. Starting from the initial state, our fuzzer exercises one order of magnitude more state/transition sequences and covers code two times faster than the baseline fuzzer. Several zero-day bugs in prominent protocol implementations were found by our fuzzer, and 8 CVEs have been assigned.


### Linear-time Temporal Logic guided Greybox Fuzzing (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/46/Linear-time-Temporal-Logic-guided-Greybox-Fuzzing)

**Abstract:** Software model checking is a verification technique which is widely used for checking temporal properties of software systems. Even though it is a property verification technique, its common usage in practice is in âbug findingâ?, that is, finding violations of temporal properties. Motivated by this observation and leveraging the recent progresses in fuzzing, we build a greybox fuzzing framework to find violations of Linear-time Temporal Logic (LTL) properties.

Our framework takes as input a sequential program written in C, and an LTL property. It finds violations, or counter-example traces, of the LTL property in stateful software systems; however, it does not achieve verification. Our work substantially extends directed greybox fuzzing to witness arbitrarily complex event orderings. We note that existing directed greybox fuzzing approaches are limited to witnessing reaching a location or witnessing simple event orderings like use-after-free. At the same time, compared to model checkers, our approach finds the counter-examples faster, thereby finding more counterexamples within a given time budget.

Our LTL-fuzzer tool, built on top of the AFL fuzzer, is shown to be effective in detecting bugs in well-known protocol implementations, such as OpenSSL and Telnet. We use LTL-fuzzer to reproduce known vulnerabilities (CVEs), to find 15 zero-day bugs by checking properties extracted from RFCs (for which 9 CVEs have been assigned), and to find violations of both safety as well as liveness properties in real-world protocol implementations. Our work represents a practical advance over software model checkers â? while simultaneously also representing a conceptual advance over existing greybox fuzzers. Our work provides a starting point for understanding the unexplored synergies between software model checking and greybox fuzzing.


### Rtkaller: State-aware Task Generation for RTOS Fuzzing (EMSOFT 2021)

* <img src="image/pdf_24px.png">[Paper](http://www.wingtecher.com/themes/WingTecherResearch/assets/papers/emsoft21.pdf)

**Abstract:** A real-time operating system (RTOS) is an operating system designed to meet certain real-time requirements. It is widely used in embedded applications, and its correctness is safety-critical. However, the validation of RTOS is challenging due to its complex real-time features and large code base.

In this paper, we propose Rtkaller, a state-aware kernel fuzzer for the vulnerability detection in RTOS. First, Rtkaller implements an automatic task initialization to transform the syscall sequences into initial tasks with more real-time information. Then, a coverage-guided task mutation is designed to generate those tasks that explore more in-depth real-time related code for parallel execution. Moreover, Rtkaller realizes a task modification to correct those tasks that may hang during fuzzing. We evaluated it on recent versions
of rt-Linux, which is one of the most widely used RTOS. Compared to the state-of-the-art kernel fuzzers Syzkaller and Moonshine, Rtkaller achieves the same code coverage at the speed of 1.7X and 1.6X , gains an increase of 26.1% and 22.0% branch coverage within 24 hours respectively. More importantly, Rtkaller has confirmed 28 previously unknown vulnerabilities that are missed by other fuzzers.


### Typestate-Guided Fuzzer for Discovering Use-after-Free Vulnerabilities (ICSE 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#fUjb3)
  
* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2020/07/31/typestate-guided-fuzzer-for-discovering-use-after-free-vulnerabilities/)

* <img src="image/wechat_24px.png">[Reading Note](https://mp.weixin.qq.com/s/RE19ba-BnQsZRST338lJHA)

* <img src="image/pdf_24px.png">[Paper](https://www.scedt.tees.ac.uk/s.qin/papers/icse2020-uafl.pdf)

* <img src="image/ppt_24px.png">[Slides](https://wcventure.github.io/pdf/ICSE2020_UAFL_Slides.pdf)

**Abstract:** Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage 
to guide the fuzzing process, which has shown great potential in finding vulnerabilities.
However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). 
This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges,
but also to traverse some long sequence of edges in a particular order, which is challenging for existing fuzzers.
To this end, we first propose to model UaF vulnerabilities as typestate properties,
then develop a typestate-guided fuzzer, named UAFL, for discovering 
vulnerabilities violating typestate properties.
%Our approach works in two phases.
Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property.
Then, the fuzzing process is guided by the operation sequences in order to progressively generate test cases triggering property violations.
In addition, we also adopt the information flow analysis to improve the efficiency of the fuzzing process.
We performed a thorough evaluation of UAFL on 14 widely-used real-world programs.
The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities.
We discovered 10 previously unknown vulnerabilities, and received 5 new CVEs.


### IJON: Exploring Deep State Spaces via Fuzzing (S&P 2020)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://securitygossip.com/blog/2020/03/20/ijon-exploring-deep-state-spaces-via-fuzzing/)

* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/109971382)

* <img src="image/pdf_24px.png">[Paper](./Paper/SP20_IJON.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/RUB-SysSec/ijon)

**Abstract:** Although current fuzz testing (fuzzing) methods are highly effective, there are still many situations such as complex state machines where fully automated approaches fail. State-of-the-art fuzzing methods offer very limited ability for a human to interact and aid the fuzzer in such cases. More specifically, most current approaches are limited to adding a dictionary or new seed inputs to guide the fuzzer. When dealing with complex programs, these mechanisms are unable to uncover new parts of the codebase.

In this paper, we propose IJON, an annotation mechanism that a human analyst can use to guide the fuzzer. In contrast to the two aforementioned techniques, this approach allows a more systematic exploration of the programâs behavior based on the data representing the internal state of the program. As a consequence, using only a small (usually one line) annotation, a user can help the fuzzer to solve previously unsolvable challenges. We extended various AFL-based fuzzers with the ability to annotate the source code of the target application with guidance hints. Our evaluation demonstrates that such simple annotations are able to solve problems thatâto the best of our knowledgeâno other current fuzzer or symbolic execution based tool can overcome. For example, with our extension, a fuzzer is able to play and solve games such as Super Mario Bros. or resolve more complex patterns such as hash map lookups. To further demonstrate the capabilities of our annotations, we use AFL combined with IJON to uncover both novel security issues and issues that previously required a custom and comprehensive grammar to be uncovered. Lastly, we show that using IJON and AFL, one can solve many challenges from the CGC data set that resisted all fully automated and human-guided attempts so far.


### MemFuzz: Using Memory Accesses to Guide Fuzzing (ICST 2019)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#VwAnn)
  
* <img src="image/pdf_24px.png">[Paper](./Paper/ICST19_MemFuzz.pdf)

**Abstract:** Fuzzing is a form of random testing that is widely used for finding bugs and vulnerabilities. State of the art approaches commonly leverage information about the control flow of prior executions of the program under test to decide which inputs to mutate further. By relying solely on control flow information to characterize executions, such approaches may miss relevant differences. We propose augmenting evolutionary fuzzing by additionally leveraging information about memory accesses performed by the target program. The resulting approach can leverage more sophisticated information about the execution of the target program, enhancing the effectiveness of the evolutionary fuzzing. We implement our approach as a modification of the widely used AFL fuzzer and evaluate our implementation on three widely used target applications. We find distinct crashes from those detected by AFL for all three targets in our evaluation.


# Rust Fuzzing

### Rust Library Fuzzing via API Dependency Graph Traversal (ASE 2021)

* <img src="image/pdf_24px.png">[Paper](https://arxiv.org/pdf/2104.12064.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/Artisan-Lab/RULF)

**Abstract:** Robustness is a key concern for Rust library development because Rust promises no risks of undefined behaviors if developers use safe APIs only. Fuzzing is a practical approach for examining the robustness of programs. However, existing fuzzing tools are not directly applicable to library APIs due to the absence of fuzz targets. It mainly relies on human efforts to design fuzz targets case by case which is labor-intensive. To address this problem, this paper proposes a novel automated fuzz target generation approach for fuzzing Rust libraries via API dependency graph traversal. We identify several essential requirements for library fuzzing, including validity and effectiveness of fuzz targets, high API coverage, and efficiency. To meet these requirements, we first employ breadth-first search with pruning to find API sequences under a length threshold, then we backward search longer sequences for uncovered APIs, and finally we optimize the sequence set as a set covering problem. We implement our fuzz target generator and conduct fuzzing experiments with AFL++ on several real-world popular Rust projects. Our tool finally generates 7 to 118 fuzz targets for each library with API coverage up to 0.92. We exercise each target with a threshold of 24 hours and find 30 previously-unknown bugs from seven libraries.


### RUSTY: A Fuzzing Tool for Rust (ACSAC 2020)

* <img src="image/pdf_24px.png">[Paper](https://www.acsac.org/2020/program/poster-wips/2020-3-RUSTY%20%20A%20Fuzzing%20Tool%20for%20Rust.pdf)

**Abstract:** Rust is known as one of the most popular programming languages on the Stack Overflow website in 2020, indicating that many programmers have had the opportunity to use Rust in different projects. There are many reasons for this success, mostly due to its performance and safety. Rust is a friendly compiler with useful error messages, which provides excellent documentation with an integrated package manager. Furthermore, it is memory-efficient and fast without a garbage collector. Hence, the Rust compiler can power performance-critical services run on embedded devices. Likewise, due to Rusts rich type system and ownership model, it guarantees memory-safety and thread-safety enabling the end-users to reduce various bug types at compile-time.

However, even though all the advantages come with Rust, we found there are still various security issues in this ecosystem that can compromise the safety of Rust programs. To perform our security analysis, we created RUSTY that perform bug fuzzing based on the combination of concolic testing and property-based testing. is also the first kind of its own for the Rust ecosystem. Our preliminary evaluation collected multiple trendy Rust projects on GitHub, and RUSTY could successfully identify various memory security issues in this benchmark suite.


# Regression Fuzzing

### R2Z2: Detecting Rendering Regressions in Web Browsers through Differential Fuzz Testing (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/8/R2Z2-Detecting-Rendering-Regressions-in-Web-Browsers-through-Differential-Fuzz-Testi)

**Abstract:** A rendering regression is a bug introduced by a web browser where a web page no longer functions as users expect. Such rendering bugs critically harm the usability of web browsers as well as web applications. The unique aspect of rendering bugs is that they affect the presented visual appearance of web pages, but those web pages have no pre-defined correct appearance. Therefore, it is challenging to automatically detect errors in their appearance. In practice, web browser vendors rely on non-trivial and time-prohibitive manual analysis to detect and handle rendering regressions.

This paper proposes R2Z2, an automated tool to find rendering regressions. R2Z2 uses the differential fuzz testing approach, which repeatedly compares the rendering results of two different versions of a browser while providing the same HTML as input. If the rendering results are different, R2Z2 further performs cross browser compatibility testing to check if the rendering difference is indeed a rendering regression. After identifying a rendering regression, R2Z2 will perform an in-depth analysis to aid in fixing the regression. Specifically, R2Z2 performs a delta-debugging-like analysis to pinpoint the exact browser source code commit causing the regression, as well as inspecting the rendering pipeline stages to pinpoint which pipeline stage is responsible. We implemented a prototype of R2Z2 particularly targeting the Chrome browser. So far, R2Z2 found 11 previously undiscovered rendering regressions in Chrome, all of which were confirmed by the Chrome developers. Importantly, in each case, R2Z2 correctly reported the culprit commit. Moreover, R2Z2 correctly pin-pointed the culprit rendering pipeline stage in all but one case.


### FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/132/FADATest-Fast-and-Adaptive-Performance-Regression-Testing-of-Dynamic-Binary-Translat)

**Abstract:** Dynamic binary translation (DBT) is the cornerstone of many important applications. In practice, however, it is quite difficult to maintain the performance efficiency of a DBT system due to its inherent complexity. Although performance regression testing isan effective approach to detect potential performance regression issues, it is not easy to apply performance regression testing to DBT systems, because of the natural differences between DBT systems and common software systems and the limited availability of effective test programs. In this paper, we present FADATest, which devises several novel techniques to address these challenges. Specifically,FADATest automatically generates adaptable test programs from existing real benchmark programs of DBT systems according to the runtime characteristics of the benchmarks. The test programs can then be used to achieve highly efficient and adaptive performance regression testing of DBT systems. We have implemented a prototype of FADATest. Experimental results on SPEC CPU 2017 and PARSEC show that FADATest can successfully uncover the same performance regression issues across the evaluated versions of two popular DBT systems, QEMU and Valgrind, as the original benchmark programs. Moreover, the testing efficiency is improved significantly on two different hardware platforms powered by x86-64 and AArch64, respectively.


### Regression Greybox Fuzzing (CCS 2021)

* <img src="image/pdf_24px.png">[Paper](https://mboehme.github.io/paper/CCS21.pdf)

**Abstract:** What you change is what you fuzz! In an empirical study of all fuzzer-generated bug reports in OSSFuzz, we found that four in every five bugs have been introduced by recent code changes. That is, 77% of 23k bugs are regressions. For a newly added project, there is usually an initial burst of new reports at 2-3 bugs per day. However, after that initial burst, and after weeding out most of the existing bugs, we still get a constant rate of 3-4 bug reports per week. The constant rate can only be explained by an increasing regression rate. Indeed, the probability that a reported bug is a regression (i.e., we could identify the bug-introducing commit) increases from 20% for the first bug to 92% after a few hundred bug reports.

In this paper, we introduce regression greybox fuzzing (RGF) a fuzzing approach that focuses on code that has changed more recently or more often. However, for any active software project, it is impractical to fuzz sufficiently each code commit individually. Instead, we propose to fuzz all commits simultaneously, but code present in more (recent) commits with higher priority. We observe that most code is never changed and relatively old. So, we identify means to strengthen the signal from executed code-of-interest. We also extend the concept of power schedules to the bytes of a seed and introduce Ant Colony Optimization to assign more energy to those bytes which promise to generate more interesting inputs.

Our large-scale fuzzing experiment demonstrates the validity of our main hypothesis and the efficiency of regression greybox fuzzing. We conducted our experiments in a reproducible manner within Fuzzbench, an extensible fuzzer evaluation platform. Our experiments involved 3+ CPU-years worth of fuzzing campaigns and 20 bugs in 15 open-source C programs available on OSSFuzz.


### DeltaFuzz: Historical Version Information Guided Fuzz Testing (Journal of Computer Science and Technology 2021)

* <img src="image/youtube.png">[Video](https://lcs.ios.ac.cn/setta2021/videos/day2_10.mp4)

* <img src="image/pdf_24px.png">[Paper](https://jcst.ict.ac.cn/EN/10.1007/s11390-021-1663-7)

**Abstract:** With the widespread use of agile software development methods, such as agile and scrum, software is iteratively updated more frequently. To ensure the quality of the software, regression testing is conducted before new versions are released. Moreover, to improve the efficiency of regression testing, testing efforts should be concentrated on the modified and impacted parts of a program. However, the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive. Fuzz testing is an effective method for generating test data automatically, but it is usually devoted to achieving higher code coverage, which makes fuzz testing unsuitable for direct regression testing scenarios. For this reason, we propose a fuzz testing method based on the guidance of historical version information. First, the differences between the program being tested and the historical version are analyzed, and the results of the analysis are used to locate change points. Then, change impact analysis is performed to find the corresponding impacted basic blocks. Finally, the fitness values of test cases are calculated according to the execution traces, and new test cases are generated iteratively by the genetic algorithm. Based on the proposed method, we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects. Compared with the fuzzing tool AFLGo, AFLFast and AFL, DeltaFuzz could cover the target faster, and the time taken by DeltaFuzz was reduced by 20.59%, 30.05% and 32.61%, respectively.


### HyDiff: Hybrid Differential Software Analysis (ICSE 2020)

* <img src="image/blog_24px.jpg">[Reading Note](https://www.yuque.com/xianglincheng/sc94zo/namw67#NrdLd)
  
* <img src="image/zhihu.png">[Reading Note](https://zhuanlan.zhihu.com/p/138238406)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE20_Hydiff.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/yannicnoller/hydiff)

**Abstract:** Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants.

This paper presents HyDiff, the first hybrid approach for differential software analysis. HyDiff integrates and extends two very successful testing techniques: Feedback-directed greybox fuzzing for efficient program testing and shadow symbolic execution for systematic program exploration. HyDiff extends greybox fuzzing with divergence-driven feedback based on novel cost metrics that take into account the control flow graph of the program. Furthermore HyDiff extends shadow symbolic execution by applying four-way forking in a systematic exploration and still having the ability to incorporate concrete inputs in the analysis. HyDiff applies divergence revealing heuristics based on resource consumption and control-flow information to efficiently guide the symbolic exploration, which allows its efficient usage beyond regression testing applications. We introduce differential metrics such as output, decision and cost difference, as well as patch distance, to assist the fuzzing and symbolic execution components in maximizing the execution divergence.

We implemented our approach on top of the fuzzer AFL and the symbolic execution framework Symbolic PathFinder. We illustrate HyDiff on regression and side-channel analysis for Java bytecode programs, and further show how to use HyDiff for robustness analysis of neural networks.


# Directed Fuzzing

### WindRanger: A Directed Greybox Fuzzer driven by Deviation Basic Block (ICSE 2022)

* <img src="image/pdf_24px.png">[Paper](https://conf.researchr.org/details/icse-2022/icse-2022-papers/162/WindRanger-A-Directed-Greybox-Fuzzer-driven-by-DeviationBasic-Blocks)

**Abstract:** Directed grey-box fuzzing (DGF) is a security testing technique that aims to steer the fuzzer towards predefined target sites in the program. To gain directness, DGF prioritizes the seeds whose execution traces are closer to the target sites. Therefore, evaluating the distance between the execution trace of a seed and the target sites (aka, the seed distance) is important for DGF. The first directed grey-box fuzzer, AFLGo, uses an approach of calculating the basic block level distances during static analysis and accumulating the distances of the executed basic blocks to compute the seed distance. Following AFLGo, most of the existing state-of-the-art DGF techniques use all the basic blocks on the execution trace and only the control flow information for seed distance calculation. However, not every basic block is equally important and there are certain basic blocks where the execution trace starts to deviate from the target sites (aka, deviation basic blocks).

In this paper, we propose a technique called WindRanger which leverages deviation basic blocks to facilitate DGF. To identify the deviation basic blocks, WindRanger applies both static reachability analysis and dynamic filtering. To conduct directed fuzzing, WindRanger uses the deviation basic blocks and their related data flow information for seed distance calculation, mutation, seed prioritization as well as explore-exploit scheduling. We evaluated WindRanger on 3 datasets consisting of 29 programs. The experiment results show that WindRanger outperforms AFLGo, AFL, and Fairfuzz by reaching the target sites 21%, 34%, and 37% faster and detecting the target crashes 44%, 66%, and 77% faster respectively. Moreover, we found a 0-day vulnerability with a CVE ID assigned in ffmpeg (a popular multimedia library extensively fuzzed by OSS-fuzz) with WindRanger by supplying manually identified suspect locations as the target sites.


### BEACON: Directed Grey-Box Fuzzing with Provable Path Pruning (S&P 2022)

* <img src="image/pdf_24px.png">[Paper](https://5hadowblad3.github.io/files/Oakland22-Beacon.pdf)

* <img src="image/github_24px.png">[Code](https://hub.docker.com/r/yguoaz/beacon)
  

**Abstract:** Unlike coverage-based fuzzing that gives equal attention to every part of a code, directed fuzzing aims to direct a fuzzer to a specific target in the code, e.g., the code with potential vulnerabilities. Despite much progress, we observe that existing directed fuzzers are still not efficient as they often symbolically or concretely execute a lot of program paths that cannot reach the target code. They thus waste a lot of computational resources.
This paper presents BEACON, which can effectively direct a greybox fuzzer in the sea of paths in a provable manner. That is, assisted by a lightweight static analysis that computes abstracted preconditions for reaching the target, we can prune 82.94% of the executing paths at runtime with negligible analysis overhead (Ä5h) but with the guarantee that the pruned paths must be spurious with respect to the target. We have implemented our approach, BEACON, and compared it to five state-of-the-art (directed) fuzzers in the application scenario of vulnerability reproduction. The evaluation results demonstrate that BEACON is 11.50x faster on average than existing directed grey-box fuzzers and it can also improve the speed of the conventional coverage-guided fuzzers, AFL, AFL++, and Mopt, to reproduce specific bugs with 6.31x ,11.86x, and 10.92x speedup, respectively. More interestingly, when used to test the vulnerability patches, BEACON found 14 incomplete fixes of existing CVE-identified vulnerabilities and 8 new bugs while 10 of them are exploitable with new CVE ids assigned


### Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA (ASE 2021 Industry)

* <img src="image/pdf_24px.png">[Paper](https://github.com/hongshin/publications/blob/main/ase21-industry.pdf)

**Abstract:** This paper presents industrial experiences on enhancing the configurability of a fuzzing framework for effective continuous fuzzing of the SAP HANA components. We propose five new mutation scheduling strategies for effective uses of grammar-aware mutators in the unit-level fuzzing framework, and three new seed corpus selection strategies to configure a fuzzing campaign to check on changed code in priority. The empirical results show that the proposed extension gives users chances to improve fuzzing effectiveness and efficiency by configuring the framework specifically for each target component


### KCFuzz: Directed Fuzzing Based on Keypoint Coverage (ICAIS 2021)

* <img src="image/pdf_24px.png">[Paper](https://link.springer.com/chapter/10.1007/978-3-030-78609-0_27)

**Abstract:** Directed fuzzing, as an efficient method to focus on a specific set of targets in the program, often works better than random fuzzing when combined with a researcherâs empirical judgment. However, the current directed fuzzing work is not efficient enough. In previous studies, some have generated closer seed inputs by guiding the execution path through the distance from the target region, but the distance guided algorithm is less robust. Some studies used selective symbolic execution for directed testing to alleviate the path explosion problem, but it brings a higher false-positive rate. In this paper, we propose a keypoint coverage-based fuzzing (KCFuzz) method, which extracts the keypoint list using a control flow graph, obtains the keypoint list coverage information through runtime instrumentation, calculates the test priority of the seeds based on the overall coverage and keypoint coverage using an energy scheduling algorithm, and continuously generates test inputs closer to the target according to the specified mutation strategy. On this basis, a hybrid testing framework is implemented, using keypoint coverage directed fuzzing to generate a seed queue covering keypoints, using offspring generation strategies and hybrid execution technology, and further exploring the new state of the program according to changes in overall and keypoint coverage. The experimental results show that the KCFuzz method can efficiently induce the generation of seed queues to reach the target region, and at the same time, the depth and validity of the exploration paths are higher than those of the most advanced directed fuzzing methods such as AFLGo.


### Constraint-guided Directed Greybox Fuzzing (USENIX Security2021)

* <img src="image/pdf_24px.png">[Paper](https://lifeasageek.github.io/papers/gwangmu-cafl.pdf)

**Abstract:** Directed greybox fuzzing is an augmented fuzzing technique intended for the targeted usages such as crash reproduction and proof-of-concept generation, which gives directedness to fuzzing by driving the seeds toward the designated program locations called target sites. However, we find that directed greybox fuzzing can still suffer from the long fuzzing time before exposing the targeted crash, because it does not consider the ordered target sites and the data conditions. This paper presents constraint-guided directed greybox fuzzing that aims to satisfy a sequence of constraints rather than merely reaching a set of target sites. Constraint-guided greybox fuzzing defines a constraint as the combination of a target site and the data conditions, and drives the seeds to satisfy the constraints in the specified order. We automatically generate the constraints with seven types of crash dumps and four types of patch changelogs, and evaluate the prototype system CAFL against the representative directed greybox fuzzing system AFLGo with 47 real-world crashes and 12 patch changelogs. The evaluation shows CAFL outperforms AFLGo by 2.88x for crash reproduction, and better performs in PoC generation as the constraints get explicit.


### Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing (MDPI 2021)

* <img src="image/pdf_24px.png">[Paper](https://www.mdpi.com/2076-3417/11/3/1351)

**Abstract:** Control Flow Graphs (CFGs) provide fundamental data for many program analyses, such as malware analysis, vulnerability detection, code similarity analysis, etc. Existing techniques for constructing control flow graphs include static, dynamic, and hybrid analysis, which each having their own advantages and disadvantages. However, due to the difficulty of resolving indirect jump relations, the existing techniques are limited in completeness. In this paper, we propose a practical technique that applies static analysis and dynamic analysis to construct more complete control flow graphs. The main innovation of our approach is to adopt directed gray-box fuzzing (DGF) instead of coverage-based gray-box fuzzing (CGF) used in the existing approach to generate test cases that can exercise indirect jumps. We first employ a static analysis to construct the static CFGs without indirect jump relations. Then, we utilize directed gray-box fuzzing to generate test cases and resolve indirect jump relations by monitoring the execution traces of these test cases. Finally, we combine the static CFGs with indirect jump relations to construct more complete CFGs. In addition, we also propose an iterative feedback mechanism to further improve the completeness of CFGs. We have implemented our technique in a prototype and evaluated it through comparing with the existing approaches on eight benchmarks. The results show that our prototype can resolve more indirect jump relations and construct more complete CFGs than existing approaches.


### Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities (RAID 2020)

* <img src="image/GoSSIP_note.jpg">[Reading Note from GoSSIP](https://mp.weixin.qq.com/s/_kFbnKNa-fXbfqF-7TFN5A)

* <img src="image/pdf_24px.png">[Paper](./Paper/Arxiv20_BinaryUAF.pdf)

**Abstract:** Directed fuzzing focuses on automatically testing specific parts of the code by taking advantage of additional information such as (partial) bug stack trace, patches or risky operations. Key applications include bug reproduction, patch testing and static analysis report verification. Although directed fuzzing has received a lot of attention recently, hard-to-detect vulnerabilities such as Use-Afer-Free (UAF) are still not well addressed, more especially at the binary level. We propose UAFuzz, the first (binary-level) directed greybox fuzzer dedicated to UAF bugs. The technique features a fuzzing engine tailored to UAF specifics, a lightweight code instrumentation and an efficient bug triage step. Experimental evaluation for bug reproduction on real cases demonstrates that UAFuzz significantly outperforms state-of-the-art directed fuzzers in terms of fault detection rate, time to exposure and bug triaging. UAFuzz has also been proven effective in patch testing, leading to the discovery of 20 new bugs in Perl, GPAC and GNU Patch (including a buggy patch) - all of them have been acknowledged and 14 have been fixed. Last but not least, we provide to the community the first fuzzing benchmark dedicated to UAF, built on both real codes and real bugs.

### Ankou: Guiding Grey-box Fuzzing towards Combinatorial Difference (ICSE 2020)

* <img src="image/pdf_24px.png">[Paper](./Paper/ICSE20_Ankou.pdf)

* <img src="image/github_24px.png">[Code](https://github.com/SoftSec-KAIST/Ankou)

**Abstract:** Grey-box fuzzing is an evolutionary process, which maintains and evolves a population of test cases with the help of a fitness function. Fitness functions used by current grey-box fuzzers are not informative in that they cannot distinguish different program executions as long as those executions achieve the same coverage. The problem is that the current fitness functions only consider a union of data, but not the combination of them. As such, fuzzers often get stuck in a local optimum during their search. In this paper, we introduce Ankou, the first grey-box fuzzer that recognizes different \emph{combinations} of execution information, and present several scalability challenges encountered while designing and implementing Ankou. Our experimental results show that Ankou is $1.94\times$ and $8.0\times$ more effective in finding bugs than AFL and Angora, respectively.

### RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation (2020)

* <img src="image/pdf_24px.png">[Paper](https://www.hindawi.com/journals/mpe/2020/7698916/)

**Abstract:** Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental re