# RAVEN: Agentic RAG for Automated Vulnerability Repair

arXiv: https://arxiv.org/abs/2606.22647 · HTML: https://mlsanity.com/paper/2606.22647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 16 of 16)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://mlsanity.com/paper/2107.08760/) (2021)
- [TBar: Revisiting Template-based Automated Program Repair](https://mlsanity.com/paper/1903.08409/) (2019)
- [How Effective Are Neural Networks for Fixing Security Vulnerabilities](https://mlsanity.com/paper/2305.18607/) (2023)
- [RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair](https://mlsanity.com/paper/2309.06057/) (2023)
- [A Survey of Symbolic Execution Techniques](https://mlsanity.com/paper/1610.00502/) (2016)
- [Neural Transfer Learning for Repairing Security Vulnerabilities in C Code](https://mlsanity.com/paper/2104.08308/) (2021)
- [Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities](https://mlsanity.com/paper/1707.04742/) (2017)
- [AVATAR : Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations](https://mlsanity.com/paper/1812.07270/) (2018)
- [APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching](https://mlsanity.com/paper/2408.13597/) (2024)
- [You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems](https://mlsanity.com/paper/1812.07283/) (2018)
- [Review4Repair: Code Review Aided Automatic Program Repairing](https://mlsanity.com/paper/2010.01544/) (2020)
- [SeqTrans: Automatic Vulnerability Fix via Sequence to Sequence Learning](https://mlsanity.com/paper/2010.10805/) (2020)
- [An Analysis of the Search Spaces for Generate and Validate Patch Generation Systems](https://mlsanity.com/paper/1602.05643/) (2016)

## Built on by (most cited first, 1 of 1)

- [CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation](https://mlsanity.com/paper/2609.02774/) (2026)

## Abstract (arXiv)

Automated vulnerability repair has emerged as a promising direction to mitigate the growing number of software vulnerabilities. Recent advances in Large Language Models (LLMs) have further accelerated research in automated repair. However, existing frameworks remain largely restricted to memory-related vulnerabilities and locally repairable vulnerability settings, leaving generalization to unseen vulnerability types underexplored. Their evaluations are often limited to a single programming language, and largely rely on proprietary models. In this paper, we propose RAVEN, a scalable, efficient and autonomous framework that integrates an agentic retrieval-augmented generation (RAG) pipeline with controlled iterative repair in a unified framework. The framework utilizes open-source LLMs in a fully locally deployable setting with limited GPU requirements, while building a multi-faceted retrieval pipeline to retrieve historically relevant vulnerability fixes and guide the patch generation. In addition, RAVEN introduces a dedicated Curator Agent that retrieves cross-file dependencies from the target repository, to fix complex vulnerabilities that cannot be addressed using local vulnerable code alone. We evaluate RAVEN on 160 real-world CVE vulnerabilities across diverse vulnerability types, two programming languages, unseen CWE categories, and out-of-distribution settings. RAVEN achieves an overall repair success rate of 83.13%, outperforming all existing state-of-the-art repair frameworks, while also demonstrating strong generalization capabilities and maintaining the repair cost negligible.