LProtector: An LLM-driven Vulnerability Detection System
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916479803326464 |
|---|---|
| author | Sheng, Ze Wu, Fenghua Zuo, Xiangwu Li, Chao Qiao, Yuxin Hang, Lei |
| author_facet | Sheng, Ze Wu, Fenghua Zuo, Xiangwu Li, Chao Qiao, Yuxin Hang, Lei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LProtector: An LLM-driven Vulnerability Detection System Sheng, Ze Wu, Fenghua Zuo, Xiangwu Li, Chao Qiao, Yuxin Hang, Lei Cryptography and Security Artificial Intelligence 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. |
| title | LProtector: An LLM-driven Vulnerability Detection System |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06493 |