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Binsec/Rel: Efficient Relational Symbolic Execution for Constant-Time at Binary-Level

Abstract : The constant-time programming discipline (CT) is an efficient countermeasure against timing side-channel attacks, requiring the control flow and the memory accesses to be independent from the secrets. Yet, writing CT code is challenging as it demands to reason about pairs of execution traces (2- hypersafety property) and it is generally not preserved by the compiler, requiring binary-level analysis. Unfortunately, current verification tools for CT either reason at higher level (C or LLVM), or sacrifice bug-finding or bounded-verification, or do not scale. We tackle the problem of designing an efficient binary-level verification tool for CT providing both bug-finding and bounded-verification. The technique builds on relational symbolic execution enhanced with new optimizations dedicated to information flow and binary-level analysis, yielding a dramatic improvement over prior work based on symbolic execution. We implement a prototype, Binsec/Rel, and perform extensive experiments on a set of 338 cryptographic implementations, demonstrating the benefits of our approach in both bug-finding and bounded-verification. Using Binsec/Rel, we also automate a previous manual study of CT preservation by compilers. Interestingly, we discovered that gcc -O0 and backend passes of clang introduce violations of CT in implementations that were previously deemed secure by a state-of-the-art CT verification tool operating at LLVM level, showing the importance of reasoning at binary-level.
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https://hal.inria.fr/hal-03141415
Contributor : Tamara Rezk Connect in order to contact the contributor
Submitted on : Monday, February 15, 2021 - 11:45:48 AM
Last modification on : Friday, November 5, 2021 - 4:14:59 AM

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  • HAL Id : hal-03141415, version 1
  • ARXIV : 1912.08788

Citation

Lesly-Ann Daniel, Sébastien Bardin, Tamara Rezk. Binsec/Rel: Efficient Relational Symbolic Execution for Constant-Time at Binary-Level. IEEE Security and Privacy (IEEE S&P'20), May 2020, Virtual, France. ⟨hal-03141415⟩

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