The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors

• URL: http://arxiv.org/abs/2401.02737v3
• Date: Sat, 7 Sep 2024 12:26:49 GMT
• Title: The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors
• Authors: Baijun Cheng, Kailong Wang, Cuiyun Gao, Xiapu Luo, Li Li, Yao Guo, Xiangqun Chen, Haoyu Wang,
• Abstract summary: VULEXPLAINER is a tool for locating vulnerability-critical code lines from coarse-level vulnerable code snippets. It can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities.
• Score: 33.395068754566935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have achieved high effectiveness. Despite their capability of detecting vulnerable code snippets from given code fragments, the detectors are typically unable to further locate the fine-grained information pertaining to the vulnerability, such as the precise vulnerability triggering locations.In this paper, we propose VULEXPLAINER, a tool for automatically locating vulnerability-critical code lines from coarse-level vulnerable code snippets reported by DL-based detectors.Our approach takes advantage of the code structure and the semantics of the vulnerabilities. Specifically, we leverage program slicing to get a set of critical program paths containing vulnerability-triggering and vulnerability-dependent statements and rank them to pinpoint the most important one (i.e., sub-graph) as the data flow associated with the vulnerability. We demonstrate that VULEXPLAINER performs consistently well on four state-of-the-art graph-representation(GP)-based vulnerability detectors, i.e., it can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities, outperforming five widely used GNN-based explanation approaches. The experimental results demonstrate the effectiveness of VULEXPLAINER, which provides insights into a promising research line: integrating program slicing and deep learning for the interpretation of vulnerable code fragments.

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