Structured Security Explanations for Learning-Based Vulnerability Detection

Abstract

Software vulnerability detection remains difficult because existing approaches rely on predefined analysis rules, learn vulnerability patterns implicitly from code and labels, or use large language models (LLMs) to make the final security judgment. It remains unclear whether structured descriptions of security-relevant program behavior can improve supervised detection. We present VulnLens, an explanation-augmented framework for binary C/C++ vulnerability detection that separates program-behavior analysis from the final decision. A label-blind LLM-based Explainer examines each function from seven fixed perspectives: data flow, memory access, object lifetime, arithmetic and type behavior, control flow, shared state, and external-resource access. Without receiving vulnerability labels or CWE information, it generates structured explanations. A supervised Detector combines source code and explanations through sequential cross-attention. We evaluate VulnLens on a PrimeVul-derived dataset covering ten CWE categories and test transfer to SecVulEval without retraining. On the 526-function PrimeVul test set, VulnLens achieves 75.5% accuracy and 77.2% F1, exceeding DeepSeek V4 Pro by 4.2 percentage points in accuracy and Gemini 2.5 Pro by 2.2 points in F1. Matched explanations improve F1 by 7.3 points over a separately trained code-only detector. On SecVulEval, VulnLens achieves 66.4% accuracy and 65.8% F1, outperforming DeepSeek V4 Pro by 7.6 points in accuracy and 20.0 points in F1, and Gemini 2.5 Pro by 16.6 points in accuracy and 7.8 points in F1.