VulnLLM R 7B: Specialized Reasoning LLM for Vulnerability Detection VulnLLM R is the first specialized reasoning Large Language Model designed specifically for software vulnerability detection. Unlike traditional static analysis tools (like CodeQL) or small LLMs that rely on simple pattern matching, VulnLLM R is trained to reason step by step about data flow, control flow, and security context. It mimics the thought process of a human security auditor to identify complex logic vulnerabilities with high accuracy. 🔗 Quick Links Paper: arXiv:2512.07533 Code & Data: GitHub Demo: Web demo 💡 Key Features Reasoning Based Detection: Does not just classify code; it generates a "Chain of Thought" to analyze why a vulnerability exists. Superior Accuracy: Outperforms commercial giants (like Claude 3.7 Sonnet, o3 mini) and industry standard tools (CodeQL, AFL++) on key benchmarks. Efficiency: Achieves SOTA performance with only 7B parameters , making it 30x smaller and significantly faster than general purpose reasoning models. Broad Coverage: Trained and tested on C, C++, Python, and Java (zero shot generalization). 🚀 Quick Start 📊 Performance VulnLLM R 7B achieves state of the art results…
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