Is Awesome-LLMs-for-Vulnerability-Detection open source?

Yes — huhusmang/Awesome-LLMs-for-Vulnerability-Detection is open source, released under the MIT license.

What language is Awesome-LLMs-for-Vulnerability-Detection written in?

huhusmang/Awesome-LLMs-for-Vulnerability-Detection is primarily written in Python.

How popular is Awesome-LLMs-for-Vulnerability-Detection?

huhusmang/Awesome-LLMs-for-Vulnerability-Detection has 1.2k stars on GitHub.

Where can I find Awesome-LLMs-for-Vulnerability-Detection?

huhusmang/Awesome-LLMs-for-Vulnerability-Detection is on GitHub at https://github.com/huhusmang/Awesome-LLMs-for-Vulnerability-Detection.

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huhusmang/Awesome-LLMs-for-Vulnerability-Detection

A firehose of papers on LLMs hunting software bugs

Someone had to catalog the flood of research turning LLMs into security auditors.

★1.2k stars Python Learning Domain Apps

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Awesome-LLMs-for-Vulnerability-Detection

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What it does

This repository is a community-maintained awesome-list that corrals research papers, benchmarks, and open-source tools applying Large Language Models to software vulnerability detection. The README is built around a sprawling markdown table spanning function-level analysis, repository-level scans, smart-contract audits, and agentic approaches, with direct links to papers and their corresponding code when available. It functions as a living map for a research niche that is proliferating faster than most bibliographic databases can track.

The interesting bit

The list exposes the field’s current obsessions—RAG-augmented detection, neuro-symbolic rule synthesis, and agentic false-positive filtering—without forcing you to read dozens of abstracts. It also quietly highlights the reproducibility gap: many 2026 preprints already have GitHub repos, while some peer-reviewed papers offer no code at all.

Key highlights

Curated coverage of function-level, repository-level, agentic, and smart-contract vulnerability detection, plus datasets and benchmarks.
Direct links to both academic papers (arXiv, DOI, or anthology) and associated GitHub repositories, where available.
Includes meta-research such as systematic literature reviews, empirical comparisons to traditional static analysis, and reproducibility critiques.
Tracks specialized niches like smart-contract auditing, embedded-system taint analysis, and neuro-symbolic rule synthesis.
Actively updated with 2025 and 2026 preprints alongside established conference publications (NDSS, ICSE, ACL, Usenix).

Caveats

Many entries have empty cells—some 2026 preprints lack paper links, while several peer-reviewed papers have no associated repository.
Discovery is README-only, so finding specific techniques relies on browser search rather than structured filtering.
No explicit inclusion criteria or review rubric is outlined in the visible portion of the README.

Verdict

If you are a security researcher, graduate student, or tool-builder trying to map the LLM vulnerability-detection landscape, this list will save you hours of literature search. If you are looking for a single tool to plug into your CI pipeline, look elsewhere—this is a reading list, not a framework.

Frequently asked

What is huhusmang/Awesome-LLMs-for-Vulnerability-Detection?: Someone had to catalog the flood of research turning LLMs into security auditors.
Is Awesome-LLMs-for-Vulnerability-Detection open source?: Yes — huhusmang/Awesome-LLMs-for-Vulnerability-Detection is open source, released under the MIT license.
What language is Awesome-LLMs-for-Vulnerability-Detection written in?: huhusmang/Awesome-LLMs-for-Vulnerability-Detection is primarily written in Python.
How popular is Awesome-LLMs-for-Vulnerability-Detection?: huhusmang/Awesome-LLMs-for-Vulnerability-Detection has 1.2k stars on GitHub.
Where can I find Awesome-LLMs-for-Vulnerability-Detection?: huhusmang/Awesome-LLMs-for-Vulnerability-Detection is on GitHub at https://github.com/huhusmang/Awesome-LLMs-for-Vulnerability-Detection.