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INDIGO (University of Illinois at Chicago)· 2018

Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software

James Newsome, Dawn Song

Short summary

TaintCheck, a dynamic taint analysis tool, reliably detects most exploits on commodity software without source code or special compilation, producing no false positives in testing.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Dynamic taint analysis via runtime binary rewriting reliably detects most exploits on commodity software.
  • The TaintCheck mechanism requires neither source code nor special compilation of the monitored program.
  • Testing showed TaintCheck produced zero false positives across multiple programs.
  • The approach is designed to improve automatic signature generation for exploits.

AI-generated from the title and abstract; the full text is not read.

Abstract

Software vulnerabilities have had a devastating effect on the Internet. Worms such as CodeRed and Slammer can compromise hundreds of thousands of hosts within hours or even minutes, and cause millions of dollars of damage [26, 43]. To successfully combat these fast automatic Internet attacks, we need fast automatic attack detection and filtering mechanisms. In this paper we propose dynamic taint analysis for automatic detection of overwrite attacks, which include most types of exploits. This approach does not need source code or special compilation for the monitored program, and hence works on commodity software. To demonstrate this idea, we have implemented TaintCheck, a mechanism that can perform dynamic taint analysis by performing binary rewriting at run time. We show that TaintCheck reliably detects most types of exploits. We found that TaintCheck produced no false positives for any of the many different programs that we tested. Further, we describe how TaintCheck could improve automatic signature generation in several ways.

The authors' abstract, as published at the source. INDIGO (University of Illinois at Chicago), 2018 · DOI ↗

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Field: Signal Processing

Signal ProcessingComputer Science