GraphSPD: Graph-Based Security Patch Detection with Enriched Code Semantics

Abstract

With the increasing popularity of open-source software, embedded vulnerabilities have been widely propagating to downstream software. Due to different maintenance policies, software vendors may silently release security patches without providing sufficient advisories (e.g., CVE). This leaves users unaware of security patches and provides attackers good chances to exploit unpatched vulnerabilities. Thus, detecting those silent security patches becomes imperative for secure software maintenance. In this paper, we propose a graph neural network based security patch detection system named GraphSPD, which represents patches as graphs with richer semantics and utilizes a patch-tailored graph model for detection. We first develop a novel graph structure called PatchCPG to represent software patches by merging two code property graphs (CPGs) for the pre-patch and post-patch source code as well as retaining the context, deleted, and added components for the patch. By applying a slicing technique, we retain the most relevant context and reduce the size of PatchCPG. Then, we develop the first end-to-end deep learning model called PatchGNN to determine if a patch is security-related directly from its graph-structured PatchCPG. PatchGNN includes a new embedding process to convert PatchCPG into a numeric format and a new multi-attributed graph convolution mechanism to adapt diverse relationships in PatchCPG. The experimental results show GraphSPD can significantly outperform the state-of-the-art approaches on security patch detection.