Reasoning with LLMs for Zero-Shot Vulnerability Detection

• URL: http://arxiv.org/abs/2503.17885v1
• Date: Sat, 22 Mar 2025 23:59:17 GMT
• Title: Reasoning with LLMs for Zero-Shot Vulnerability Detection
• Authors: Arastoo Zibaeirad, Marco Vieira,
• Abstract summary: We present textbfVulnSage, a comprehensive evaluation framework and a curated dataset from diverse, large-scale open-source system software projects.<n>The framework supports multi-granular analysis across function, file, and inter-function levels.<n>It employs four diverse zero-shot prompt strategies: Baseline, Chain-of-context, Think, and Think & verify.
• Score: 0.9208007322096533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating software vulnerability detection (SVD) remains a critical challenge in an era of increasingly complex and interdependent software systems. Despite significant advances in Large Language Models (LLMs) for code analysis, prevailing evaluation methodologies often lack the \textbf{context-aware robustness} necessary to capture real-world intricacies and cross-component interactions. To address these limitations, we present \textbf{VulnSage}, a comprehensive evaluation framework and a dataset curated from diverse, large-scale open-source system software projects developed in C/C++. Unlike prior datasets, it leverages a heuristic noise pre-filtering approach combined with LLM-based reasoning to ensure a representative and minimally noisy spectrum of vulnerabilities. The framework supports multi-granular analysis across function, file, and inter-function levels and employs four diverse zero-shot prompt strategies: Baseline, Chain-of-Thought, Think, and Think & Verify. Through this evaluation, we uncover that structured reasoning prompts substantially improve LLM performance, with Think & Verify reducing ambiguous responses from 20.3% to 9.1% while increasing accuracy. We further demonstrate that code-specialized models consistently outperform general-purpose alternatives, with performance varying significantly across vulnerability types, revealing that no single approach universally excels across all security contexts. Link to dataset and codes: https://github.com/Erroristotle/VulnSage.git

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