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DAFuzz: data-aware fuzzing of in-memory data stores.

Yingpei Zeng1, Fengming Zhu1, Siyi Zhang1

  • 1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China.

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|October 9, 2023
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Summary
This summary is machine-generated.

Data-aware fuzzing (DAFuzz) improves software vulnerability discovery by ensuring correct data usage. This method enhances code path coverage and accelerates the identification of security flaws in data-intensive applications.

Keywords:
Coverage-base fuzzingCoverage-guided fuzzingData-awareIn-memory data storeInput generationSemantic-aware

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Fuzzing is crucial for software vulnerability detection.
  • Existing syntactic- and semantic-aware fuzzing methods struggle with in-memory data stores.
  • Certain code paths require specific data availability, limiting current fuzzing effectiveness.

Purpose of the Study:

  • To introduce a novel data-aware fuzzing method (DAFuzz) for in-memory data stores.
  • To enhance fuzzing by considering data usage during input generation and execution.
  • To improve the coverage of data-sensitive code paths.

Main Methods:

  • DAFuzz loads diverse data into stores before fuzzing to activate different code paths.
  • It generates syntactically and semantically valid inputs that correctly utilize available data.
  • A prototype was implemented using Superion and tested on Redis and Memcached.

Main Results:

  • DAFuzz achieved 13-95% greater edge coverage compared to AFL, Superion, AFL++, and AFLNet.
  • Vulnerability discovery was over 2.7 times faster with DAFuzz.
  • Four new vulnerabilities in Redis and Memcached were identified.

Conclusions:

  • DAFuzz effectively addresses limitations of existing fuzzing techniques for data stores.
  • The method significantly improves vulnerability detection rates and efficiency.
  • All discovered vulnerabilities were reported, acknowledged, and fixed by developers.