Scores of compilers produce JavaScript, enabling programmers to use many languages on the Web, reuse existing code, and even use Web IDEs. Unfortunately, most compilers expose the browser's compromised execution model, so long-running programs freeze the browser tab, infinite loops crash IDEs, and so on. The few compilers that avoid these problems suffer poor performance and are difficult to engineer. This paper presents Stopify, a source-to-source compiler that extends JavaScript with debugging abstractions and blocking operations, and easily integrates with existing compilers. We apply Stopify to 10 programming languages and develop a Web IDE that supports stopping, single-stepping, breakpointing, and long-running computations. For nine languages, Stopify requires no or trivial compiler changes. For eight, our IDE is the first that provides these features. Two of our subject languages have compilers with similar features. Stopify's performance is competitive with these compilers and it makes them dramatically simpler. Stopify's abstractions rely on first-class continuations, which it provides by compiling JavaScript to JavaScript. We also identify sub-languages of JavaScript that compilers implicitly use, and exploit these to improve performance. Finally, Stopify needs to repeatedly interrupt and resume program execution. We use a sampling-based technique to estimate program speed that outperforms other systems.
Pivot @cite_59 isolates untrusted JavaScript into an iframe and rewrites the program to use generators, which allows blocking I O between the iframe and the outside world. is not an isolation framework and implements blocking without generators or ( sec:callcc ).
@cite_59
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Pivot is a new JavaScript isolation framework for web applications. Pivot uses iframes as its low-level isolation containers, but it uses code rewriting to implement synchronous cross-domain interfaces atop the asynchronous cross-frame post Message() primitive. Pivot layers a distributed scheduling abstraction across the frames, essentially treating each frame as a thread which can invoke RPCs that are serviced by external threads. By rewriting JavaScript call sites, Pivot can detect RPC invocations, Pivot exchanges RPC requests and responses via post Message(), and it pauses and restarts frames using a novel rewriting technique that translates each frame's JavaScript code into a restart able generator function. By leveraging both iframes and rewriting, Pivot does not need to rewrite all code, providing an order-of-magnitude performance improvement over rewriting-only solutions. Compared to iframe-only approaches, Pivot provides synchronous RPC semantics, which developers typically prefer over asynchronous RPCs. Pivot also allows developers to use the full, unrestricted JavaScript language, including powerful statements like eval(). Web sites that incorporate untrusted content may use browser- or language-based methods to keep such content from maliciously altering pages, stealing sensitive information, or causing other harm. We study language-based methods for filtering and rewriting JavaScript code, using Yahoo! ADSafe and Facebook FBJS as motivating examples. We explain the core problems by describing previously unknown vulnerabilities and subtleties, and develop a foundation for improved solutions based on an operational semantics of the full ECMA-262 language. We also discuss how to apply our analysis to address the JavaScript isolation problems we discovered. Publishers wish to sandbox third-party advertisements to protect themselves from malicious advertisements. One promising approach, used by ADsafe, Dojo Secure, and Jacaranda, sandboxes advertisements by statically verifying that their JavaScript conforms to a safe subset of the language. These systems blacklist known dangerous properties that would let advertisements escape the sandbox. Unfortunately, this approach does not prevent advertisements from accessing new methods added to the built-in prototype objects by the hosting page. In this paper, we show that onethird of the Alexa US Top 100 web sites would be exploitable by an ADsafe-verified advertisement. We propose an improved statically verified JavaScript subset that whitelists known-safe properties using namespaces. Our approach maintains the expressiveness and performance of static verification while improving security. The optimistic variants of Multi-Version Concurrency Control (MVCC) avoid blocking concurrent transactions at the cost of having a validation phase. Upon failure in the validation phase, the transaction is usually aborted and restarted from scratch. The "abort and restart" approach becomes a performance bottleneck for use cases with high contention objects or long running transactions. In addition, restarting from scratch creates a negative feedback loop in the system, because the system incurs additional overhead that may create even more conflicts. In this paper, we propose a novel approach for conflict resolution in MVCC for in-memory databases. This low overhead approach summarizes the transaction programs in the form of a dependency graph. The dependency graph also contains the constructs used in the validation phase of the MVCC algorithm. Then, when encountering conflicts among transactions, our mechanism quickly detects the conflict locations in the program and partially re-executes the conflicting transactions. This approach maximizes the reuse of the computations done in the initial execution round, and increases the transaction processing throughput. In Java 2 and Microsoft .NET Common Language Runtime (CLR), trusted code has often been programmed to perform access-restricted operations not explicitly requested by its untrusted clients. Since an untrusted client will be on the call stack when access control is enforced, an access-restricted operation will not succeed unless the client is authorized. To avoid this, a portion of the trusted code can be made “privileged.” When access control is enforced, privileged code causes the stack traversal to stop at the trusted code frame, and the untrusted code stack frames will not be checked for authorization. For large programs, manually understanding which portions of code should be made privileged is a difficult task. Developers must understand which authorizations will implicitly be extended to client code and make sure that the values of the variables used by the privileged code are not “tainted” by client code. This paper presents an interprocedural analysis for Java bytecode to automatically identify which portions of trusted code should be made privileged, ensure that there are no tainted variables in privileged code, and detect “unnecessary” and “redundant” privileged code. We implemented the algorithm and present the results of our analyses on a set of large programs. While the analysis techniques are in the context of Java code, the basic concepts are also applicable to non-Java systems with a similar authorization model. Information flow control allows untrusted code to access sensitive and trustworthy information without leaking this information. However, the presence of covert channels subverts this security mechanism, allowing processes to communicate information in violation of IFC policies. In this paper, we show that concurrent deterministic IFC systems that use time-based scheduling are vulnerable to a cache-based internal timing channel. We demonstrate this vulnerability with a concrete attack on Hails, one particular IFC web framework. To eliminate this internal timing channel, we implement instruction-based scheduling, a new kind of scheduler that is indifferent to timing perturbations from underlying hardware components, such as the cache, TLB, and CPU buses. We show this scheduler is secure against cache-based internal timing attacks for applications using a single CPU. To show the feasibility of instruction-based scheduling, we have implemented a version of Hails that uses the CPU retired-instruction counters available on commodity Intel and AMD hardware. We show that instruction-based scheduling does not impose significant performance penalties. Additionally, we formally prove that our modifications to Hails’ underlying IFC system preserve non-interference in the presence of caches. We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (kâ � 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems. This paper introduces a method to control JavaScript execution. The aim is to prevent or modify inappropriate behaviour caused by e.g. malicious injected scripts or poorly designed third-party code. The approach is based on modifying the code so as to make it self-protecting: the protection mechanism (security policy) is embedded into the code itself and intercepts security relevant API calls. The challenges come from the nature of the JavaScript language: any variables in the scope of the program can be redefined, and code can be created and run on-the-fly. This creates potential problems, respectively, for tamper-proofing the protection mechanism, and for ensuring that no security relevant events bypass the protection. Unlike previous approaches to instrument and monitor JavaScript to enforce or adjust behaviour, the solution we propose is lightweight in that (i) it does not require a modified browser, and (ii) it does not require any run-time parsing and transformation of code (including dynamically generated code). As a result, the method has low run-time overhead compared to other methods satisfying (i), and the lack of need for browser modifications means that the policy can even be applied on the server to mitigate some effects of cross-site scripting bugs. Many optimizing compilers use basic block reordering to reduce conditional branch misprediction penalties, decrease the number of unconditional branches and to improve the instruction cache performance. To perform this optimization one has to solve an NP-hard problem of finding a covering of a weighted control flow graph with a node-disjoint set of simple paths with maximal total weight; the problem is usually solved heuristically. We present the precise method which decomposes the initial graph into the set of subgraphs of certain kind — hammocks — and then solves a subproblem for each subgraph by an exponential branch-and-bound algorithm. The algorithm was tested on several real-world C programs, and its running time was as small as the rest of the compilation time for 91 of the tested graphs. Unfortunately it still failed to finish in a reasonable time for the remaining graphs since they didn’t have enough hammocks. We consider fixing up this drawback as a future work. JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them. In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought. Today large software systems (i.e., giants) thrive in commodity markets, but are untrustworthy due to their numerous and inevitable software bugs that can be exploited by the adversary. Thus, the best hope of security is that some small, simple, and trustworthy software components (i.e., wimps) can be protected from attacks launched by adversary-controlled giants. However, wimps in isolation typically give up a variety of basic services (e.g., file system, networking, device I O), trading usefulness and viability with security. Among these basic services, isolated I O channels remained an unmet challenge over the past three decades. Isolated I O is a critical security primitive for a myriad of applications (e.g., secure user interface, remote device control). With this primitive, isolated wimps can transfer I O data to commodity peripheral devices and the data secrecy and authenticity are protected from the co-existing giants. This thesis addresses this challenge by proposing a new security architecture to provide services to isolated wimps. Instead of restructuring the giants or bloating the Trusted Computing Base that underpins wimp-giant isolation (dubbed underlying TCB), this thesis presents a unique on-demand I O isolation model and a trusted add-on component called wimpy kernel to instantiate this model. In our model, the wimpy kernel dynamically takes control of the devices managed by a commodity OS, connects them to the isolated wimps, and relinquishes controls to the OS when done. This model creates ample opportunities for the wimpy kernel to outsource I O subsystem functions to the untrusted OS and verify their results. The wimpy kernel further exports device drivers and I O subsystem code to wimps and mediates the operations of the exported code. These two methodologies help to significantly reduce the size and complexity of the wimpy kernel for high security assurance. Using the popular and complex USB subsystem as a case study, this thesis illustrates the dramatic reduction of the wimpy kernel; i.e., over 99 of the Linux USB code base is removed. In addition, the wimpy kernel also composes with the underlying TCB, by retaining its code size, complexity and security properties. We present a new approach for predicting program properties from massive codebases (aka "Big Code"). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs) in order to perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNice for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNice predicts correct names for 63 of name identifiers and its type annotation predictions are correct in 81 of the cases. In the first week since its release, JSNice was used by more than 30,000 developers and in only few months has become a popular tool in the JavaScript developer community. By formulating the problem of inferring program properties as structured prediction and showing how to perform both learning and inference in this context, our work opens up new possibilities for attacking a wide range of difficult problems in the context of "Big Code" including invariant generation, decompilation, synthesis and others. This paper presents an approach to the safe execution of applications based on software-based fault isolation and policy-based system call authorization. A running application is encapsulated in an additional layer of protection using dynamic binary translation in user-space. This virtualization layer dynamically recompiles the machine code and adds multiple dynamic security guards that verify the running code to protect and contain the application. The binary translation system redirects all system calls to a policy-based system call authorization framework. This interposition framework validates every system call based on the given arguments and the location of the system call. Depending on the user-loadable policy and an extensible handler mechanism the framework decides whether a system call is allowed, rejected, or redirect to a specific user-space handler in the virtualization layer. This paper offers an in-depth analysis of the different security guarantees and a performance analysis of libdetox, a prototype of the full protection platform. The combination of software-based fault isolation and policy-based system call authorization imposes only low overhead and is therefore an attractive option to encapsulate and sandbox applications to improve host security. Decentralized Information Flow Control (DIFC) is an approach to security that allows application writers to control how data flows between the pieces of an application and the outside world. As applied to privacy, DIFC allows untrusted software to compute with private data while trusted security code controls the release of that data. As applied to integrity, DIFC allows trusted code to protect untrusted software from unexpected malicious inputs. In either case, only bugs in the trusted code, which tends to be small and isolated, can lead to security violations. We present Flume, a new DIFC model that applies at the granularity of operating system processes and standard OS abstractions (e.g., pipes and file descriptors). Flume was designed for simplicity of mechanism, to ease DIFC's use in existing applications, and to allow safe interaction between conventional and DIFC-aware processes. Flume runs as a user-level reference monitor onLinux. A process confined by Flume cannot perform most system calls directly; instead, an interposition layer replaces system calls with IPCto the reference monitor, which enforces data flowpolicies and performs safe operations on the process's behalf. We ported a complex web application (MoinMoin Wiki) to Flume, changingonly 2 of the original code. Performance measurements show a 43 slowdown on read workloadsand a 34 slowdown on write workloads, which aremostly due to Flume's user-level implementation. The advent of Web 2.0 has lead to the proliferation of client-side code that is typically written in JavaScript. This code is often combined -- or mashed-up -- with other code and content from disparate, mutually untrusting parties, leading to undesirable security and reliability consequences. This paper proposes GATEKEEPER, a mostly static approach for soundly enforcing security and reliability policies for JavaScript programs. GATEKEEPER is a highly extensible system with a rich, expressive policy language, allowing the hosting site administrator to formulate their policies as succinct Datalog queries. The primary application of GATEKEEPER this paper explores is in reasoning about JavaScript widgets such as those hosted by widget portals Live.com and Google IG. Widgets submitted to these sites can be either malicious or just buggy and poorly written, and the hosting site has the authority to reject the submission of widgets that do not meet the site's security policies. To show the practicality of our approach, we describe nine representative security and reliability policies. Statically checking these policies results in 1,341 verified warnings in 684 widgets, no false negatives, due to the soundness of our analysis, and false positives affecting only two widgets. Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory. To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times. NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8 for Cassandra, 85.0 for Lucene and 96.45 for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage. Dynamic languages, such as JavaScript, give programmers the freedom to ignore types, and enable them to write concise code in short time. Despite this freedom, many programs follow implicit type rules, for example, that a function has a particular signature or that a property has a particular type. Violations of such implicit type rules often correlate with problems in the program. This paper presents Type Devil, a mostly dynamic analysis that warns developers about inconsistent types. The key idea is to assign a set of observed types to each variable, property, and function, to merge types based in their structure, and to warn developers about variables, properties, and functions that have inconsistent types. To deal with the pervasiveness of polymorphic behavior in real-world JavaScript programs, we present a set of techniques to remove spurious warnings and to merge related warnings. Applying Type Devil to widely used benchmark suites and real-world web applications reveals 15 problematic type inconsistencies, including correctness problems, performance problems, and dangerous coding practices. There is an ever-increasing demand for more complex transactions and higher throughputs in transaction processing systems leading to higher degrees of transaction concurrency and, hence, higher data contention. The conventional two-phase locking (2PL) Concurrency Control (CC) method may, therefore, restrict system throughput to levels inconsistent with the available processing capacity. This is especially a concern in shared-nothing or data-partitioned systems due to the extra latencies for internode communication and a reliable commit protocol. The optimistic CC (OCC) is a possible solution, but currently proposed methods have the disadvantage of repeated transaction restarts. We present a distributed OCC method followed by locking, such that locking is an integral part of distributed validation and two-phase commit. This method ensures at most one re-execution, if the validation for the optimistic phase fails. Deadlocks, which are possible with 2PL, are prevented by preclaiming locks for the second execution phase. This is done in the same order at all nodes. We outline implementation details and compare the performance of the new OCC method with distributed 2PL through a detailed simulation that incorporates queueing effects at the devices of the computer systems, buffer management, concurrency control, and commit processing. It is shown that for higher data contention levels, the hybrid OCC method allows a much higher maximum transaction throughput than distributed 2PL in systems with high processing capacities. In addition to the comparison of CC methods, the simulation study is used to study the effect of varying the number of computer systems with a fixed total processing capacity and the effect of locality of access in each case. We also describe several interesting variants of the proposed OCC method, including methods for handling access variance, i.e., when rerunning a transaction results in accesses to a different set of objects. We propose a new formal criterion for secure compilation, providing strong security guarantees for components written in unsafe, low-level languages with C-style undefined behavior. Our criterion goes beyond recent proposals, which protect the trace properties of a single component against an adversarial context, to model dynamic compromise in a system of mutually distrustful components. Each component is protected from all the others until it receives an input that triggers an undefined behavior, causing it to become compromised and attack the remaining uncompromised components. To illustrate this model, we demonstrate a secure compilation chain for an unsafe language with buffers, procedures, and components, compiled to a simple RISC abstract machine with built-in compartmentalization. The protection guarantees offered by this abstract machine can be achieved at the machine-code level using either software fault isolation or tag-based reference monitoring. We are working on machine-checked proofs showing that this compiler satisfies our secure compilation criterion. Abstract This paper shows how to integrate two complementary techniques for manipulating program invariants: dynamic detection and static verification. Dynamic detection proposes likely invariants based on program executions, but the resulting properties are not guaranteed to be true over all possible executions. Static verification checks that properties are always true, but it can be difficult and tedious to select a goal and to annotate programs for input to a static checker. Combining these techniques overcomes the weaknesses of each: dynamically detected invariants can annotate a program or provide goals for static verification, and static verification can confirm properties proposed by a dynamic tool. We have integrated a tool for dynamically detecting likely program invariants, Daikon, with a tool for statically verifying program properties, ESC Java. Daikon examines run-time values of program variables; it looks for patterns and relationships in those values, and it reports properties that are never falsified during test runs and that satisfy certain other conditions, such as being statistically justified. ESC Java takes as input a Java program annotated with preconditions, postconditions, and other assertions, and it reports which annotations cannot be statically verified and also warns of potential runtime errors, such as null dereferences and out-of-bounds array indices. Our prototype system runs Daikon, inserts its output into code as ESC Java annotations, and then runs ESC Java, which reports unverifiable annotations. The entire process is completely automatic, though users may provide guidance in order to improve results if desired. In preliminary experiments, ESC Java verified all or most of the invariants proposed by Daikon.