# Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead

arXiv: https://arxiv.org/abs/2404.02525 · HTML: https://mlsanity.com/paper/2404.02525/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

## Built on by (most cited first, 30 of 42)

- [CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities](https://mlsanity.com/paper/2503.17332/) (2025)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [A Contemporary Survey of Large Language Model Assisted Program Analysis](https://mlsanity.com/paper/2502.18474/) (2025)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview](https://mlsanity.com/paper/2503.10784/) (2025)
- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry](https://mlsanity.com/paper/2601.18844/) (2026)
- [Improving LLM Reasoning for Vulnerability Detection via Group Relative Policy Optimization](https://mlsanity.com/paper/2507.03051/) (2025)
- [SecureFixAgent: A Hybrid LLM Agent for Automated Python Static Vulnerability Repair](https://mlsanity.com/paper/2509.16275/) (2025)
- [POLAR: Automating Cyber Threat Prioritization through LLM-Powered Assessment](https://mlsanity.com/paper/2510.01552/) (2025)
- [FuzzingBrain V2: A Multi-Agent LLM System for Automated Vulnerability Discovery and Reproduction](https://mlsanity.com/paper/2605.21779/) (2026)
- [DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection](https://mlsanity.com/paper/2607.13439/) (2026)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Llama-based source code vulnerability detection: Prompt engineering vs Fine tuning](https://mlsanity.com/paper/2512.09006/) (2025)
- [AgenticVM: Agentic AI for Adaptive Software Vulnerability Management](https://mlsanity.com/paper/2605.01739/) (2026)
- [EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution](https://mlsanity.com/paper/2605.30105/) (2026)
- [LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries](https://mlsanity.com/paper/2505.08842/) (2025)
- [Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs](https://mlsanity.com/paper/2507.20977/) (2025)
- [Multi-task Code LLMs: Data Mix or Model Merge?](https://mlsanity.com/paper/2601.21115/) (2026)
- [Red-MIRROR: Agentic LLM-based Autonomous Penetration Testing with Reflective Verification and Knowledge-augmented Interaction](https://mlsanity.com/paper/2603.27127/) (2026)
- [Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points](https://mlsanity.com/paper/2412.11194/) (2024)
- [To Patch or Not to Patch: Motivations, Challenges, and Implications for Cybersecurity](https://mlsanity.com/paper/2502.17703/) (2025)
- [AVIATOR: Towards AI-Agentic Vulnerability Injection Workflow for High-Fidelity, Large-Scale Code Security Dataset](https://mlsanity.com/paper/2508.20866/) (2025)
- [ArchISMiner: A Framework for Automatic Mining of Architectural Issue-Solution Pairs from Online Developer Communities](https://mlsanity.com/paper/2510.21966/) (2025)
- [On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection](https://mlsanity.com/paper/2510.27675/) (2025)
- [From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection](https://mlsanity.com/paper/2512.10485/) (2025)

## Abstract (arXiv)

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading SE, AI, and Security conferences and journals, encompassing 43 papers published across 25 distinct venues, along with 15 high-quality preprint papers, bringing the total to 58 papers. By answering three key research questions, we aim to (1) summarize the LLMs employed in the relevant literature, (2) categorize various LLM adaptation techniques in vulnerability detection, and (3) classify various LLM adaptation techniques in vulnerability repair. Based on our findings, we have identified a series of limitations of existing studies. Additionally, we have outlined a roadmap highlighting potential opportunities that we believe are pertinent and crucial for future research endeavors.