FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning

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Bibliografische gegevens
Gepubliceerd in:	arXiv.org (Dec 11, 2024), p. n/a
Hoofdauteur:	Upadhyay, Saket
Gepubliceerd in:	Cornell University Library, arXiv.org
Onderwerpen:	Testing time Machine learning Harnesses Computer programming Software testing
Online toegang:	Citation/Abstract Full text outside of ProQuest
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Omschrijving
Samenvatting:	Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning to refine fuzzing targets. By analyzing compile-time information, such as function call graphs' features, loop information, and memory operations, FuzzDistill identifies high-priority areas of the codebase that are more probable to contain vulnerabilities. I demonstrate the efficacy of my approach through experiments conducted on real-world software, demonstrating substantial reductions in testing time.
ISSN:	2331-8422
Bron:	Engineering Database