# A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback

arXiv: https://arxiv.org/abs/2405.15690 · HTML: https://mlsanity.com/paper/2405.15690/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2024.

## Built on by (most cited first, 22 of 22)

- [LLM-Powered AI Agent Systems and Their Applications in Industry](https://mlsanity.com/paper/2505.16120/) (2025)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models](https://mlsanity.com/paper/2410.21060/) (2024)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair](https://mlsanity.com/paper/2602.23647/) (2026)
- [OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs](https://mlsanity.com/paper/2510.15188/) (2025)
- [Raw Pointer Rewriting with LLMs for Translating C to Safer Rust](https://mlsanity.com/paper/2505.04852/) (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)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [Repairing vulnerabilities without invisible hands. A differentiated replication study on LLMs](https://mlsanity.com/paper/2507.20977/) (2025)
- [SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair](https://mlsanity.com/paper/2604.17184/) (2026)
- [Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis](https://mlsanity.com/paper/2607.05842/) (2026)
- [SoK: Measuring What Matters for Closed-Loop Security Agents](https://mlsanity.com/paper/2510.01654/) (2025)
- [TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code](https://mlsanity.com/paper/2510.11151/) (2025)
- [Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities](https://mlsanity.com/paper/2511.23408/) (2025)
- [RepoReviewer: A Local-First Multi-Agent Architecture for Repository-Level Code Review](https://mlsanity.com/paper/2603.16107/) (2026)
- [LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software](https://mlsanity.com/paper/2604.12994/) (2026)
- [RAVEN: Retrieval-Augmented Vulnerability Exploration Network for Memory Corruption Analysis in User Code and Binary Programs](https://mlsanity.com/paper/2604.17948/) (2026)
- [SHIELDS: Automating OS Hardening with Iterative Multi-Agent Remediation](https://mlsanity.com/paper/2606.05476/) (2026)
- [Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities](https://mlsanity.com/paper/2607.12723/) (2026)
- [Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins](https://mlsanity.com/paper/2609.17164/) (2026)

## Abstract (arXiv)

Recent work in automated program repair (APR) proposes the use of reasoning and patch validation feedback to reduce the semantic gap between the LLMs and the code under analysis. The idea has been shown to perform well for general APR, but its effectiveness in other particular contexts remains underexplored. In this work, we assess the impact of reasoning and patch validation feedback to LLMs in the context of vulnerability repair, an important and challenging task in security. To support the evaluation, we present VRpilot, an LLM-based vulnerability repair technique based on reasoning and patch validation feedback. VRpilot (1) uses a chain-of-thought prompt to reason about a vulnerability prior to generating patch candidates and (2) iteratively refines prompts according to the output of external tools (e.g., compiler, code sanitizers, test suite, etc.) on previously-generated patches. To evaluate performance, we compare VRpilot against the state-of-the-art vulnerability repair techniques for C and Java using public datasets from the literature. Our results show that VRpilot generates, on average, 14% and 7.6% more correct patches than the baseline techniques on C and Java, respectively. We show, through an ablation study, that reasoning and patch validation feedback are critical. We report several lessons from this study and potential directions for advancing LLM-empowered vulnerability repair