Declassiflow: A Static Analysis for Modeling Non-Speculative Knowledge to Relax Speculative Execution Security Measures

Declassiflow: A Static Analysis for Modeling Non-Speculative Knowledge to Relax Speculative Execution Security Measures

연구 분야: Strategies

학회: CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

Speculative execution attacks undermine the security of constant-time programming, the standard technique used to prevent microarchitectural side channels in security-sensitive software such as cryptographic code. Constant-time code must therefore also deploy a defense against speculative execution attacks to prevent leakage of secret data stored in memory or the processor registers. Unfortunately, contemporary defenses, such as speculative load hardening (SLH), can only satisfy this strong security guarantee at a very high performance cost. This paper proposes Declassiflow, a static program analysis and protection framework to efficiently protect constant-time code from speculative leakage. Declassiflow models "attacker knowledge"-data which is inherently transmitted (or, implicitly declassified) by the code's non-speculative execution-and statically removes protection on such data from points in the program where it is already guaranteed to leak non-speculatively. Overall, Declassiflow ensures that data which never leaks during the non-speculative execution does not leak during speculative execution, but with lower overhead than conservative protections like SLH.