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Vulnerability-oriented directed fuzzing for binary programs.

Lu Yu1,2, Yuliang Lu3,4, Yi Shen1

  • 1College of Electronic Engineering, National University of Defense Technology, Hefei, 230007, China.

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|March 12, 2022
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Summary
This summary is machine-generated.

VDFuzz automatically identifies and targets vulnerable code in binary programs, overcoming limitations of manual selection and source code dependency. This directed binary fuzzing framework enhances vulnerability detection efficiency.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Directed greybox fuzzing (DGF) is effective for vulnerability detection but faces challenges.
  • Existing DGFs require manual target selection, rely solely on distance feedback, and need source code access.

Purpose of the Study:

  • To propose VDFuzz, a novel vulnerability-oriented directed binary fuzzing framework.
  • To automate target identification and leverage dynamic information for guided fuzzing.

Main Methods:

  • VDFuzz employs a neural-network-based target identifier to locate vulnerable code areas.
  • A directed fuzzer assigns weights to basic blocks, using this dynamic information as feedback for test case generation.

Main Results:

  • VDFuzz successfully automates target identification, eliminating manual effort.
  • The framework effectively utilizes weighted basic block feedback for guided fuzzing.
  • Experimental results show VDFuzz outperforms state-of-the-art fuzzers in vulnerability detection.

Conclusions:

  • VDFuzz offers an automated and efficient solution for directed binary fuzzing.
  • The framework addresses key limitations of existing DGF methods.
  • VDFuzz demonstrates significant effectiveness in detecting vulnerabilities in real-world programs.