Return-Oriented Programming (ROP) is a software exploit for system compromise. By chaining short instruction sequences from existing code pieces, ROP can bypass static code-integrity checking approaches and non-executable page protections. Existing defenses either require access to source code or binary, a customized compiler or hardware modifications, or suffer from high performance and storage overhead. In this work, we propose SIGDROP, a low-cost approach for ROP detection which uses low-level properties inherent to ROP attacks. Specifically, we observe special patterns of certain hardware events when a ROP attack occurs during program execution. Such hardware event-based patterns form signatures to flag ROP attacks at runtime. SIGDROP leverages Hardware Performance Counters, which are already present in commodity processors, to efficiently capture and extract the signatures. Our evaluation demonstrates that SIGDROP can effectively detect ROP attacks with acceptable performance overhead and negligible storage overhead.
Davi et. al. propose two new processor instructions that enforce a golden CFI model @cite_21 . For each direct and indirect @math instruction, a @math instruction is added, where @math is a hard-coded, unique immediate value associated to the @math . @math pushes the label in a protected memory segment. If no @math is found for the @math instruction, the processor assumes a CFI violation. Each @math instruction has an associated @math instruction that verifies if the @math is in the protected memory segment. If no matching @math is found, a CFI violation is detected. The proposed approach requires changes to different stages of the processor pipeline to incorporate the new instructions and is thus not portable to platforms currently available.
@cite_21
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Current software attacks often build on exploits that subvert machine-code execution. The enforcement of a basic safety property, Control-Flow Integrity (CFI), can prevent such attacks from arbitrarily controlling program behavior. CFI enforcement is simple, and its guarantees can be established formally even with respect to powerful adversaries. Moreover, CFI enforcement is practical: it is compatible with existing software and can be done efficiently using software rewriting in commodity systems. Finally, CFI provides a useful foundation for enforcing further security policies, as we demonstrate with efficient software implementations of a protected shadow call stack and of access control for memory regions. Numerous researchers have studied the contention that arises among tasks running in parallel on a multicore processor. Most of those studies seek to derive a tight and sound upper-bound for the worst-case delay with which a processor resource may serve an incoming request, when its access is arbitrated using time-predictable policies such as round-robin or FIFO. We call this value upper-bound delay ( @math ). Deriving trustworthy @math statically is possible when sufficient public information exists on the timing latency incurred on access to the resource of interest. Unfortunately however, that is rarely granted for commercial-of-the-shelf (COTS) processors. Therefore, the users resort to measurement observations on the target processor and thus compute a “measured” @math . However, using @math to compute worst-case execution time values for programs running on COTS multicore processors requires qualification on the soundness of the result. In this paper, we present a measurement-based methodology to derive a @math under round-robin (RoRo) and first-in-first-out (FIFO) arbitration, which accurately approximates @math from above, without needing latency information from the hardware provider. Experimental results, obtained on multiple processor configurations, demonstrate the robustness of the proposed methodology. The availability of a new carry-less multiplication instruction in the latest Intel desktop processors significantly accelerates multiplication in binary fields and hence presents the opportunity for reevaluating algorithms for binary field arithmetic and scalar multiplication over elliptic curves. We describe how to best employ this instruction in field multiplication and the effect on performance of doubling and halving operations. Alternate strategies for implementing inversion and half-trace are examined to restore most of their competitiveness relative to the new multiplier. These improvements in field arithmetic are complemented by a study on serial and parallel approaches for Koblitz and random curves, where parallelization strategies are implemented and compared. The contributions are illustrated with experimental results improving the state-of-the-art performance of halving and doubling-based scalar multiplication on NIST curves at the 112- and 192-bit security levels and a new speed record for side-channel-resistant scalar multiplication in a random curve at the 128-bit security level. The algorithms presented in this work were implemented on Westmere and Sandy Bridge processors, the latest generation Intel microarchitectures. Embedded systems have become pervasive and are built into a vast number of devices such as sensors, vehicles, mobile and wearable devices. However, due to resource constraints, they fail to provide sufficient security, and are particularly vulnerable to runtime attacks (code injection and ROP). Previous works have proposed the enforcement of control-flow integrity (CFI) as a general defense against runtime attacks. However, existing solutions either suffer from performance overhead or only enforce coarse-grain CFI policies that a sophisticated adversary can undermine. In this paper, we tackle these limitations and present the design of novel security hardware mechanisms to enable fine-grained CFI checks. Our CFI proposal is based on a state model and a per-function CFI label approach. In particular, our CFI policies ensure that function returns can only transfer control to active call sides (i.e., return landing pads of functions currently executing). Further, we restrict indirect calls to target the beginning of a function, and lastly, deploy behavioral heuristics for indirect jumps.