Leopo1d/OpenVul-Qwen3-4B-SFT-ep1
Leopo1d/OpenVul-Qwen3-4B-SFT-ep1 is a 4 billion parameter Qwen3-based language model developed by Youpeng Li, Fuxun Yu, and Xinda Wang, fine-tuned for vulnerability detection in C/C++ code. It specializes in identifying security flaws by analyzing inter-procedural contexts rather than isolated functions. This model establishes basic security expertise and instruction-following capabilities through training on high-quality vulnerability reasoning Chain-of-Thought (CoT) data. Its primary use is for context-level vulnerability detection, focusing on Common Weakness Enumeration (CWE) standards.
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OpenVul-Qwen3-4B-SFT: Vulnerability Detection Model
OpenVul-Qwen3-4B-SFT is a 4 billion parameter model built on the Qwen3 architecture, specifically fine-tuned for vulnerability detection in C/C++ code. Developed by Youpeng Li, Fuxun Yu, and Xinda Wang, this model is designed to identify security flaws with a focus on Common Weakness Enumeration (CWE) standards.
Key Capabilities & Features
- Context-Level Vulnerability Detection: Unlike models that analyze isolated functions, OpenVul-Qwen3-4B-SFT excels at utilizing inter-procedural contexts, including global variables, type definitions, and callee functions, for more comprehensive analysis.
- Specialized Training: It has been fine-tuned on high-quality vulnerability reasoning Chain-of-Thought (CoT) data, curated using Rejection Sampling from DeepSeek-R1-0528. This method was chosen to prevent "ground-truth leakage" and reasoning hallucinations, ensuring robust security expertise.
- Instruction Following: The model demonstrates strong instruction-following capabilities tailored for security analysis tasks.
- Evidence-Based Analysis: It is designed to provide precise, evidence-based analysis without speculation, clearly labeling detected vulnerabilities and their CWE identifiers.
Recommended Use Cases
- Automated Security Audits: Ideal for integrating into pipelines for automated scanning of C/C++ codebases to identify potential security vulnerabilities.
- Developer Tools: Can be used to assist developers in identifying and understanding security flaws during the coding process.
- Research in Code Security: Provides a strong foundation for further research and development in LLM-based vulnerability detection, particularly for post-training pipelines as detailed in its associated paper.