Enhancing Fine-Grained Vulnerability Detection With Reinforcement Learning
Year: 2025
Type: article
Abstract: The rapid growth of vulnerabilities has significantly accelerated the development of automated vulnerability detection methods, especially those based on data-driven models. However, most of them primarily focus on extracting accurate code representations while overlooking the complex vulnerability patterns among vulnerable statements, thereby leaving room for improvement. To overcome this limitation, we present a novel reinforcement learning framework (RLFD) for detecting vulnerabilities at a fine-grained level.RLFDredefines the detection task as a sequential decision-making process and then employs reinforcement learning to automatically learn vulnerability-relevant structures from code snippets. Moreover, by designing reward functions aligned with fine-grained evaluation metrics,RLFDfocuses on the co-existence relations among statements from a global perspective, enabling the model to capture complex interactions that lead to vulnerabilities. Additionally, the framework utilizes CodeBERT-HLS for code representation, ensuring consistency with the state-of-the-art method while highlighting the improvements brought by the proposed reinforcement learning-based approach. Comprehensive experiments show that our method achieves a locating precision (IoU) of 69.7% and a Top-5% Acc of 67.7% on thebig_vuldataset, outperforming the state-of-the-art method by an overall 3.4% improvement in IoU. Notably, our method achieves up to a 19.7% increase in IoU for specific categories, e.g., CWE-416 (use-after-free).
Authors: Yuan Jiang, Zhichen Qu, Christoph Treude, Xiaohong Su, Tiantian Wang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G33789141, G6537669212
OpenAlex ID: W4413822380