LProtector: An LLM-driven Vulnerability Detection System

2024/11/10 by Ze Sheng, Fengying Wu, Sheng, Ze +8 · 5 citations
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2411.06493

openalex publication_date 2024/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

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• Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting
• LLaVul: A Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code
• Large Language Models for Security Operations Centers: A Comprehensive Survey
• LLM-GUARD: Large Language Model-Based Detection and Repair of Bugs and Security Vulnerabilities in C++ and Python

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