The constant-time programming discipline is commonly used to protect cryptographic libraries against side-channel attacks. However, it is hard to write constant-time code; moreover, compilers can introduce constant-time violations. Therefore, it is important to ensure that assembly code is constant-time. One approach is to show that source programs are constant-time, and that constant-timeness is preserved by compilation. In this paper, we explore the methodological soundness and scalability of the Decompile-then-Analyze approach, a less conventional alternative that has been suggested in the broader setting of static analysis. Informally, the Decompile-then-Analyze approach uses decompilers a front-end for static analysis tools. As a motivation for our study, we show that current decompilers eliminate CT vulnerabilities before CT analysis, leading to non-CT programs being accepted as constant-time. Independently, we provide constructed examples of non-CT, exploitable, programs that are accepted by two popular CT analysis tools; in both cases the culprit are program transformations that are used internally prior to CT analysis and eliminate CT violations. While our examples do not invalidate the general approach of these tools, they emphasize the need for studying the Decompile-then-Analyze approach. On the methodological side, we define the notion of CT transparency . Informally, a program transformation is CT transparent if does not eliminate nor introduce CT violations. We also provide general methods for proving that a transformation is CT transparent, and show that several transformations of interest are transparent. We also sketch an extension of CT transparency to speculative constant-time, which is used by cryptographic software as a protection against Spectre attacks. On the practical side, we build a CT-transparent version of the popular LLVM-based decompiler Ret Dec , and combine it with CT-LLVM, an existing CT verification tool for LLVM. We evaluate the resulting tool, called CT- Ret Dec on a benchmark set of real-world vulnerabilities in binaries, and show that the modifications had significant impact on how well CT- Ret Dec performs.

Constant-time (CT) verification tools are commonly used for detecting potential side-channel vulnerabilities in cryptographic libraries. Recently, a new class of tools, called speculative constant-time (SCT) tools, has also been used for detecting potential Spectre vulnerabilities. In many cases, these SCT tools have emerged as liftings of CT tools. However, these liftings are seldom defined precisely and are almost never analyzed formally. The goal of this paper is to address this gap, by developing formal foundations for these liftings, and to demonstrate that these foundations can yield practical benefits. Concretely, we introduce a program transformation, coined Speculation-Passing Style (SPS), for reducing SCT verification to CT verification. Essentially, the transformation instruments the program with a new input that corresponds to attacker-controlled predictions and modifies the program to follow them. This approach is sound and complete, in the sense that a program is SCT if and only if its SPS transform is CT. Thus, we can leverage existing CT verification tools to prove SCT; we illustrate this by combining SPS with three standard methodologies for CT verification, namely reducing it to noninterference, assertion safety, and dynamic taint analysis. We realize these combinations with three existing tools, EasyCrypt, Binsec/Rel , and CTGrind , and we evaluate them on Kocher’s benchmarks for Spectre-v1. Our results focus on Spectre-v1 in the standard CT leakage model; however, we also discuss applications of our method to other variants of Spectre and other leakage models.

Spectre attacks void the guarantees of constant-time cryptographic code by leaking secrets during speculative execution. Recent research shows that such code can be protected from Spectre-v1 attacks with minimal overhead, but leaves open the question of protecting against other Spectre variants. In this work, we design, validate, implement, and verify a new approach to protect cryptographic code against all known classes of Spectre attacks, in particular Spectre-RSB. Our approach combines a new value-dependent information-flow type system that ensures that no secrets leak even under speculative execution and a compiler transformation that enables it on the generated low-level code. We first prove the soundness of the type system and the correctness of the compiler transformation using the Coq proof assistant. We then implement our approach in the Jasmin framework for high-assurance cryptography and demonstrate that the overhead incurred by all Spectre protections is below 2% for most cryptographic primitives and reaches only about 5--7% for the more complex post-quantum key-encapsulationkmechanism Kyber.

Machine-learning-assisted side-channel analysis (ML-assisted SCA) automates the procedure of analyzing side-channel activities to reconstruct secrets. Although ML-assisted SCA does produce promising results, it is hard to determine whether its machine-learning model tends to reconstruct secrets or generate new instances. In this paper, we revisit the first general ML-assisted SCA framework for media software (Yuan et al. USENIX Security 2022), which we refer to as the Manifold-SCA framework, with a case study of reconstructing images from cache activities. We show that Manifold-SCA tends to generate images more than reconstruct them. Inspired by the autoencoder implemented in the Manifold-SCA framework, we theoretically and experimentally show that an autoencoder is sufficient to reconstruct images from cache activities. Through three ablation studies, we show that an autoencoder outperforms the Manifold-SCA framework under all scenarios. In the end, we apply an autoencoder to analyze practical cache activities collected by a profiling-based Prime+Probe attack, and show that an autoencoder can reconstruct partial pixel-related activities, but these activities are insufficient to reconstruct images due to the information loss in the activities.