Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks

• URL: http://arxiv.org/abs/2309.08225v1
• Date: Fri, 15 Sep 2023 07:51:39 GMT
• Title: Silent Vulnerability-fixing Commit Identification Based on Graph Neural Networks
• Abstract summary: VFFINDER is a graph-based approach for automated silent vulnerability fix identification. VFFINDER 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 272-420% in Precision, 22-70% in Recall, and 3.2X-8.2X in F1.
• Score: 4.837912059099674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing dependence of software projects on external libraries has generated apprehensions regarding the security of these libraries because of concealed vulnerabilities. Handling these vulnerabilities presents difficulties due to the temporal delay between remediation and public exposure. Furthermore, a substantial fraction of open-source projects covertly address vulnerabilities without any formal notification, influencing vulnerability management. Established solutions like OWASP predominantly hinge on public announcements, limiting their efficacy in uncovering undisclosed vulnerabilities. To address this challenge, the automated identification of vulnerability-fixing commits has come to the forefront. In this paper, we present 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. To precisely capture the meaning of code changes, the changed code is represented in connection with the related unchanged code. In VFFINDER, the structure of the changed code and related unchanged code are captured and the structural changes are represented in annotated Abstract Syntax Trees (aAST). VFFINDER distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models to extract structural features expressed in aASTs. We conducted experiments to evaluate VFFINDER on a dataset of 11K+ vulnerability fixing commits in 507 real-world C/C++ projects. Our results show that VFFINDER significantly improves the state-of-the-art methods by 272-420% in Precision, 22-70% in Recall, and 3.2X-8.2X in F1. Especially, VFFINDER speeds up the silent fix identification process by up to 121% with the same effort reviewing 50K LOC compared to the existing approaches.

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