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 9% runtime overhead which is an order of magnitude lower than the state-of-the-arts, while without sacrificing any taint detection capability.
@article{arxiv.2402.17241,
title = {HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware Tracing},
author = {Yiyu Zhang and Tianyi Liu and Yueyang Wang and Yun Qi and Kai Ji and Jian Tang and Xiaoliang Wang and Xuandong Li and Zhiqiang Zuo},
journal= {arXiv preprint arXiv:2402.17241},
year = {2024}
}