A collection of open security research resources for AI/LLM safety, built by ThreatCode Information Security.
resources/context — AI Code Security Anti-Patterns
Comprehensive security anti-pattern references distilled from 150+ sources to help LLMs generate safer code. Covers 25+ patterns across breadth and depth versions.
- ANTI_PATTERNS_BREADTH.md — ~65K tokens, 25+ patterns
- ANTI_PATTERNS_DEPTH.md — ~100K tokens, 7 critical patterns
- Interactive Website
resources/taxonomy — Prompt Injection Taxonomy
An interactive, searchable classification of prompt injection and LLM attacks (172 nodes across 4 pillars), built for cybersecurity auditors and penetration testers moving into AI red teaming.
| Metric | Value |
|---|---|
| Sources Analyzed | 150+ |
| Anti-Patterns Covered | 25+ |
| Taxonomy Nodes | 172 |
| CVEs Documented | 40+ |
| License | CC BY 4.0 |
resources/
├── context/ # AI Code Security Anti-Patterns
│ ├── README.md
│ ├── index.html # Standalone landing page
│ ├── ANTI_PATTERNS_BREADTH.md
│ └── ANTI_PATTERNS_DEPTH.md
└── taxonomy/ # Prompt Injection Taxonomy
├── README.md
├── CHANGELOG.md
├── docs/
│ ├── index.html
│ ├── styles.css
│ ├── app.js
│ └── data/
│ ├── taxonomy.json
│ └── taxonomy.js
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Clone the repository
git clone https://github.com/threatcode/resources.git
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View the websites — Open
context/index.htmlortaxonomy/docs/index.htmlin a browser, or deploy to GitHub Pages. -
Use the data — Include the markdown files in your AI system prompt, or use the JSON data programmatically.
We welcome contributions! See CONTRIBUTING.md for guidelines.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This project synthesizes findings from 150+ sources across CVE databases, academic research, security blogs, developer forums, and GitHub security advisories.
Built by ThreatCode Information Security · Jason Haddix