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LProtector: An LLM-driven Vulnerability Detection System

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arxiv 2411.06493 v2 pith:YM3QUVZT submitted 2024-11-10 cs.CR cs.AI

Ze Sheng , Fenghua Wu , Xiangwu Zuo , Chao Li , Yuxin Qiao , Lei Hang This is my paper · ORCID
classification cs.CRcs.AI
keywords lprotectordetectionvulnerabilitycodebasesgenerationgpt-4osystemvulnerabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting

    cs.CR 2025-09 conditional novelty 6.0 of 10

    Standardized modular threat-hunting workflows (CyberTeam) improve LLM performance on blue team tasks compared to open-ended ICL, CoT, and ToT prompting across 30 tasks and 452k samples.

  2. Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub

    cs.CR 2025-05 conditional novelty 6.0 of 10

    A single vulnerable Node.js path traversal pattern was found in 1,756 GitHub projects, most rated critical, and the authors' automated pipeline produced patches, disclosures, and evidence that LLMs have learned the pattern.

  3. SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection

    cs.SE 2025-05 conditional novelty 5.0 of 10

    SecVulEval provides a statement-level C/C++ vulnerability benchmark with context; state-of-the-art LLMs achieve only 23.83% F1 on locating vulnerable statements with correct reasoning.

  4. Large Language Models for Security Operations Centers: A Comprehensive Survey

    cs.CR 2025-09 conditional novelty 4.0 of 10

    A systematic review of 138 papers classifying LLM applications in SOC workflows by phase, model family, datasets, and maturity.

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