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ThreatCode Resources

A collection of open security research resources for AI/LLM safety, built by ThreatCode Information Security.

Projects

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.

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.

Quick Stats

Metric Value
Sources Analyzed 150+
Anti-Patterns Covered 25+
Taxonomy Nodes 172
CVEs Documented 40+
License CC BY 4.0

Structure

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

Getting Started

  1. Clone the repository

    git clone https://github.com/threatcode/resources.git
  2. View the websites — Open context/index.html or taxonomy/docs/index.html in a browser, or deploy to GitHub Pages.

  3. Use the data — Include the markdown files in your AI system prompt, or use the JSON data programmatically.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Research Sources

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

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