论文标题
Specognitor：通过预测感知的符号执行来识别幽灵漏洞
Specognitor: Identifying Spectre Vulnerabilities via Prediction-Aware Symbolic Execution
论文作者
论文摘要
Spectre攻击利用投机执行，以泄漏敏感信息。在过去的几年中，已经提出了许多静态侧通道检测器，以检测出在投机执行的情况下的缓存泄漏。但是，这些技术要么忽略分支预测机制，要么检测不适合检测新模式的静态预定义模式，或者导致错误的负面因素。 在本文中，我们说明了预测不可能的最先进方法的弱点。我们提出了Specognitor，这是一种新颖的预测感知符号执行引擎，以探索程序路径并检测细微的幽灵变体1和变体2漏洞。我们提出了一种动态模式检测机制，以解释现有和未来的漏洞。我们的实验结果表明，Specognitor在分析现实世界加密程序W.R.T.的有效性和效率不同的处理器家庭。
Spectre attacks exploit speculative execution to leak sensitive information. In the last few years, a number of static side-channel detectors have been proposed to detect cache leakage in the presence of speculative execution. However, these techniques either ignore branch prediction mechanism, detect static pre-defined patterns which is not suitable for detecting new patterns, or lead to false negatives. In this paper, we illustrate the weakness of prediction-agnostic state-of-the-art approaches. We propose Specognitor, a novel prediction-aware symbolic execution engine to soundly explore program paths and detect subtle spectre variant 1 and variant 2 vulnerabilities. We propose a dynamic pattern detection mechanism to account for both existing and future vulnerabilities. Our experimental results show the effectiveness and efficiency of Specognitor in analyzing real-world cryptographic programs w.r.t. different processor families.
