VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection

• URL: http://arxiv.org/abs/2602.10787v1
• Date: Wed, 11 Feb 2026 12:24:51 GMT
• Title: VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection
• Abstract summary: VulReaD is a knowledge-graph-guided approach for vulnerability reasoning and detection.<n>It generates CWE-consistent contrastive reasoning supervision, enabling student model training without manual annotations.<n>It improves binary F1 by 8-10% and multi-class classification by 30% Macro-F1 and 18% Micro-F1.
• Score: 37.944533057524986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations alongside predictions, but most work focuses on binary evaluation, and explanations often lack semantic consistency with Common Weakness Enumeration (CWE) categories. We propose VulReaD, a knowledge-graph-guided approach for vulnerability reasoning and detection that moves beyond binary classification toward CWE-level reasoning. VulReaD leverages a security knowledge graph (KG) as a semantic backbone and uses a strong teacher LLM to generate CWE-consistent contrastive reasoning supervision, enabling student model training without manual annotations. Students are fine-tuned with Odds Ratio Preference Optimization (ORPO) to encourage taxonomy-aligned reasoning while suppressing unsupported explanations. Across three real-world datasets, VulReaD improves binary F1 by 8-10% and multi-class classification by 30% Macro-F1 and 18% Micro-F1 compared to state-of-the-art baselines. Results show that LLMs outperform deep learning baselines in binary detection and that KG-guided reasoning enhances CWE coverage and interpretability.

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