# LProtector: An LLM-driven Vulnerability Detection System

arXiv: https://arxiv.org/abs/2411.06493 · HTML: https://mlsanity.com/paper/2411.06493/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://mlsanity.com/paper/2302.08468/) (2023)
- [Chain-of-Thought Reasoning Without Prompting](https://mlsanity.com/paper/2402.10200/) (2024)
- [Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code](https://mlsanity.com/paper/2404.00971/) (2024)
- [IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://mlsanity.com/paper/2405.17238/) (2024)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation](https://mlsanity.com/paper/2404.10100/) (2024)
- [Evaluating Fuzz Testing](https://mlsanity.com/paper/1808.09700/) (2018)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [A Survey on Automated Program Repair Techniques](https://mlsanity.com/paper/2303.18184/) (2023)
- [Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning](https://mlsanity.com/paper/2407.18962/) (2024)
- [Enhancing Large Language Models for Text-to-Testcase Generation](https://mlsanity.com/paper/2402.11910/) (2024)
- [VERCATION: Precise Vulnerable Open-source Software Version Identification based on Static Analysis and LLM](https://mlsanity.com/paper/2408.07321/) (2024)
- [Enhanced Credit Score Prediction Using Ensemble Deep Learning Model](https://mlsanity.com/paper/2410.00256/) (2024)
- [In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement](https://mlsanity.com/paper/2410.03124/) (2024)

## Built on by (most cited first, 8 of 8)

- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting](https://mlsanity.com/paper/2509.23571/) (2025)
- [AutoPatch: Multi-Agent Framework for Patching Real-World CVE Vulnerabilities](https://mlsanity.com/paper/2505.04195/) (2025)
- [Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy?](https://mlsanity.com/paper/2504.13769/) (2025)
- [Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++](https://mlsanity.com/paper/2508.16419/) (2025)
- [LLaVul: A Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code](https://mlsanity.com/paper/2509.17337/) (2025)
- [VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection](https://mlsanity.com/paper/2602.10787/) (2026)
- [FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs](https://mlsanity.com/paper/2608.25158/) (2026)

## Abstract (arXiv)

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.