论文标题
cacheql：生产软件中的量化和本地化缓存侧通道漏洞
CacheQL: Quantifying and Localizing Cache Side-Channel Vulnerabilities in Production Software
论文作者
论文摘要
缓存侧通道攻击通过检查受害者软件如何访问缓存来提取秘密。迄今为止，在不同方案下对密码系统和媒体库进行了实际攻击，从而推断秘密键并重建私人媒体数据（例如图像）。 这项工作首先提出了八个标准，该标准用于设计用于缓存侧通道漏洞的成熟探测器。然后，我们提出了符合所有这些标准的新型检测器Cacheql。 CacheQL通过表征记录的侧通道轨迹的区分性来精确量化二进制代码的信息泄漏。此外，CacheQL模型泄漏为合作游戏，从而使信息泄漏可以精确地分布到容易受到缓存侧通道的程序点。 CacheQL经过精心优化，以分析从生产软件中记录的整个侧通道轨迹（每个跟踪都可以具有数百万个记录），并且可以减轻密码盲，Oram，Oram或现实世界噪声引入的随机性。 我们的评估量化了生产加密和媒体软件的侧通道泄漏。我们进一步定位了先前检测器报告的漏洞，并在最近的OpenSSL（VER。3.0.0），MBEDTL（VER。3.0.0），libgcrypt（VER。1.9.4）中识别几百个新的泄漏站点。我们的许多本地化程序点都在加密系统的预处理模块中，由于可伸缩性，现有作品未对其进行分析。我们还将漏洞（VER。2.1.2）定位为泄露输入图像的隐私。
Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on cryptosystems and media libraries are demonstrated under different scenarios, inferring secret keys and reconstructing private media data such as images. This work first presents eight criteria for designing a full-fledged 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 cryptographic blinding, ORAM, or real-world noises. Our evaluation quantifies side-channel leaks of production cryptographic and media software. We further localize vulnerabilities reported by previous detectors and also identify a few hundred new leakage sites 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 cryptosystems, 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.
