PVDetector: Pretrained Vulnerability Detection on Vulnerability-enriched Code Semantic Graph

Automated vulnerability detection is a critical issue in software security. The advent of deep learning (DL) has led to numerous studies employing DL to detect vulnerabilities in software source code. However, existing approaches still perform poorly, particularly with real-world vulnerabilities, due to the difficulty in accurately capturing their properties. To this end, we introduce PVDetector, a DL-based approach that utilizes rich code semantics, incorporates vulnerability knowledge, and leverages pretrained code representations for precise vulnerability detection. At its core, PVDetector employs a new model called Vulnerability-enriched Code Semantic Graph (VCSG), which accurately characterizes functions by distinguishing the semantics of identical variables and more finely capturing control dependencies, data dependencies, and vulnerability relationships. Additionally, we introduce four pretraining tasks specifically designed to learn the semantics of control, data, vulnerability, and variables from the VCSG model. These pretraining tasks significantly enhance PVDetector's capability to detect vulnerabilities in downstream tasks. Experimental results indicate that PVDetector outperforms SOTAs by 5.0%-12.5% in precision, 0.2%-9.7% in recall, and 3.0%-15.1% in F1-score. Additionally, it supports six programming languages and demonstrates high efficiency (e.g., 10.6 \(\times\) faster than DeepDFA). When applied to seven software products, PVDetector discovered 55 vulnerabilities, including 10 silently patched flaws that had not been previously reported.