The community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — papers across function-level, repository-level, agentic, and smart-contract detection, plus datasets, benchmarks, and sur
Browse a collection of research papers on using AI to find software vulnerabilities from 2025 onwards.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeThe community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — papers across function-level, repository-level, agentic, and smart-contract detection, plus datasets, benchmarks, and sur
Awesome-LLMs-for-Vulnerability-Detection has 1.3k stars on GitHub. It has been forked 130 times. Awesome-LLMs-for-Vulnerability-Detection is written mainly in Python. It has been in active development since 2024. Awesome-LLMs-for-Vulnerability-Detection is available under the MIT license. Its main topics are awesome-list, code-security, large-language-models, llm.
The community's most comprehensive, continuously-updated index of research on Large Language Models for software vulnerability detection — papers across function-level, repository-level, agentic, and smart-contract detection, plus datasets, benchmarks, and sur
Awesome-LLMs-for-Vulnerability-Detection is an open-source project. It is released under the MIT license.
Yes. Awesome-LLMs-for-Vulnerability-Detection is free and open source — you can use, modify and self-host it.
Awesome-LLMs-for-Vulnerability-Detection is available under the MIT license.
Awesome-LLMs-for-Vulnerability-Detection is written mainly in Python.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/huhusmang-awesome-llms-for-vulnerability-detection.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.