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

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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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2024-10-08
DOI
https://doi.org/10.1145/3689768
Citations
4
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
1.40
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HardTaint: Production-Run Dynamic Taint Analysis via Selective Hardware Tracing

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4 citations
Proceedings of the ACM on Programming Languages
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article

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

Xiaoliang Wang, Tianyi Liu, Jian Tang, Zhiqiang Zuo, Kai Ji, Xuandong Li, Yun Lian Qi, Yiyu Zhang, Yueyang Wang
article en
4 citations

Abstract

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.

Proceedings of the ACM on Programming LanguagesVol. 8(OOPSLA2)
Nanjing University (CN)
Openalex Percentile: Top 18%
Advanced Malware Detection Techniques
1.40
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