Program Graph Learning for Software Vulnerability Analysis: A Survey

Tong Yu; Junjie Wang; Ming Li; Yuhang Hu; Junwei Hu; Xiantao Cai; Wenbin Hu; Alessio Borgi

Abstract

Software vulnerabilities represent an enduring threat to modern cyberspace. Effective vulnerability detection increasingly relies on reasoning about complex program semantics, structural dependencies, and execution behaviors. Consequently, extracting vulnerability-relevant features from code efficiently has become a pressing research issue. In recent years, advances in graph representation learning and large language models have reframed vulnerability analysis as a graph learning problem over program entities. By building on and extending traditional methods including static analysis, dynamic analysis, symbolic execution, and fuzzing, this perspective enables more effective vulnerability detection, fine-grained localization, multi-class classification, and repair. We review recent progress in graph-driven vulnerability intelligence, structuring the survey around these tasks and then providing a structured overview of representative graph-based methods, graph-based multimodal methods, LLM-assisted methods, and vulnerability repair methods, together with widely adopted datasets, program graph construction tools, and computing frameworks. We then highlight several persistent challenges: interpretability, cross-language generalization, data quality and label noise, the lack of benchmark standardization, and agent-oriented vulnerability analysis. These challenges are often intertwined, complicating piecemeal solutions. Addressing them will be critical for developing reliable, transferable, and explainable vulnerability intelligence systems.

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