CacheQL: Quantifying and Localizing Cache Side-Channel Vulnerabilities in Production Software

Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on crypto systems and media libraries are demonstrated under different scenarios, inferring secret keys from crypto algorithms and reconstructing private media data such as images.

This work first presents eight criteria for designing a fullfledged detector for cache side-channel vulnerabilities. Then, we propose CacheQL, a novel detector that meets all of these criteria. CacheQL precisely quantifies information leaks of binary code, by characterizing the distinguishability of logged side channel traces. Moreover, CacheQL models leakage as a cooperative game, allowing information leakage to be precisely distributed to program points vulnerable to cache side channels. CacheQL is meticulously optimized to analyze whole side channel traces logged from production software (where each trace can have millions of records), and it alleviates randomness introduced by crypto blinding, ORAM, or real-world noises.

Our evaluation quantifies side-channel leaks of production crypto and media software. We further localize vulnerabilities reported by previous detectors and also identify a few hundred new vulnerable program points in recent OpenSSL (ver. 3.0.0), MbedTLS (ver. 3.0.0), Libgcrypt (ver. 1.9.4). Many of our localized program points are within the pre-processing modules of crypto libraries, which are not analyzed by existing works due to scalability. We also localize vulnerabilities in Libjpeg (ver. 2.1.2) that leak privacy about input images.