  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.
