VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification

• URL: http://arxiv.org/abs/2309.01971v1
• Date: Tue, 5 Sep 2023 05:55:18 GMT
• Title: VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification
• Authors: Son Nguyen, Thanh Trong Vu, and Hieu Dinh Vo
• Abstract summary: VFFINDER is a graph-based approach for automated silent vulnerability fix identification. It distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1.
• Score: 4.837912059099674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing reliance of software projects on third-party libraries has raised concerns about the security of these libraries due to hidden vulnerabilities. Managing these vulnerabilities is challenging due to the time gap between fixes and public disclosures. Moreover, a significant portion of open-source projects silently fix vulnerabilities without disclosure, impacting vulnerability management. Existing tools like OWASP heavily rely on public disclosures, hindering their effectiveness in detecting unknown vulnerabilities. To tackle this problem, automated identification of vulnerability-fixing commits has emerged. However, identifying silent vulnerability fixes remains challenging. This paper presents VFFINDER, a novel graph-based approach for automated silent vulnerability fix identification. VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features. We conducted experiments to evaluate VFFINDER on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 47% with the same review effort of 5% compared to the existing approaches.

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