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OOPSLA2024顶会

HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware Tracing

Yiyu Zhang, Tianyi Liu, Yueyang Wang, Yun Qi, Kai Ji, Jian Tang, Xiaoliang Wang, Xuandong Li, Zhiqiang Zuo

2024年份
7被引次数
1顶会引用

摘要

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over the past decades, numerous attempts have been made to lower the overhead of DTA. Unfortunately, the reductions they achieved are marginal, causing DTA only applicable to the debugging/testing scenarios. In this paper, we propose and implement HardTaint, a system that can realize production-run dynamic taint tracking. HardTaint adopts a hybrid and systematic design which combines static analysis, selective hardware tracing and parallel graph processing techniques. The comprehensive evaluations demonstrate that HardTaint introduces only around 8% runtime overhead which is an order of magnitude lower than the state-of-the-arts, while without sacrificing any taint detection capability.

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