用GPT-4o和RAG实现代码漏洞自动检测，效果优于现有方法
LProtector: An LLM-driven Vulnerability Detection System
- 基于GPT-4o与RAG，通过上下文检索增强代码理解能力
- 在Big-Vul数据集上F1分数超越两个主流基线模型
- 适合安全研究者与开发团队快速识别高危代码漏洞
本文提出LProtector，一个由大语言模型GPT-4o与检索增强生成（RAG）驱动的C/C++代码库漏洞自动化检测系统。随着软件复杂度提升，传统检测方法面临效率与准确率瓶颈。LProtector利用GPT-4o强大的代码理解与生成能力，对目标代码进行二分类并识别漏洞。我们在Big-Vul数据集上开展实验，结果表明，LProtector在F1分数上优于两种当前最先进的基线方法，验证了将LLM与漏洞检测结合的潜力。
原文摘要 · Abstract (English)
This paper presents LProtector, an automated vulnerability detection system for C/C++ codebases driven by the large language model (LLM) GPT-4o and Retrieval-Augmented Generation (RAG). As software complexity grows, traditional methods face challenges in detecting vulnerabilities effectively. LProtector leverages GPT-4o's powerful code comprehension and generation capabilities to perform binary classification and identify vulnerabilities within target codebases. We conducted experiments on the Big-Vul dataset, showing that LProtector outperforms two state-of-the-art baselines in terms of F1 score, demonstrating the potential of integrating LLMs with vulnerability detection.
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