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V-Fuzz: Vulnerability Prediction-Assisted Evolutionary Fuzzing for Binary Programs.

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    V-Fuzz enhances bug detection by predicting vulnerable code sections. This evolutionary fuzzing framework efficiently finds software vulnerabilities, discovering ten common vulnerabilities and three new ones.

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    Area of Science:

    • Computer Science
    • Software Engineering
    • Cybersecurity

    Background:

    • Traditional fuzzing methods often focus on maximizing code coverage, which is inefficient for finding vulnerabilities.
    • Vulnerable code constitutes a small fraction of a program, making broad coverage-based approaches suboptimal.
    • Efficiently locating bugs in binary programs within limited timeframes is a significant challenge.

    Purpose of the Study:

    • To design and implement an evolutionary fuzzing framework, V-Fuzz, for efficient and rapid bug detection in binary programs.
    • To improve the efficiency of fuzzing by prioritizing areas of code predicted to be vulnerable.
    • To accelerate the discovery of software vulnerabilities using a vulnerability-prediction-guided approach.

    Main Methods:

    • Developed V-Fuzz, an evolutionary fuzzing framework comprising a vulnerability prediction model and a vulnerability-oriented evolutionary fuzzer.
    • The vulnerability prediction model estimates the likelihood of vulnerability in different program sections.
    • A guided evolutionary algorithm generates inputs targeting predicted vulnerable locations.

    Main Results:

    • V-Fuzz demonstrates efficient bug detection capabilities, significantly outperforming traditional methods by leveraging vulnerability prediction.
    • The framework successfully identified ten common vulnerabilities and exposures (CVEs).
    • Three of the discovered vulnerabilities were previously unknown.

    Conclusions:

    • V-Fuzz provides an efficient and effective approach to discovering software vulnerabilities in binary programs.
    • Integrating vulnerability prediction into evolutionary fuzzing accelerates the identification of security flaws.
    • The framework shows promise for practical application in software security testing and vulnerability research.