Awesome AI Security - Project Overview

Purpose

This is a curated collection of AI/ML security materials and resources for pentesters, red teamers, and security researchers. The goal is to keep the list AI-focused, high-signal, well-categorized, and non-duplicated.

Project Structure

awesome-ai-security/ ├── README.md # Main resource list (curated) ├── LICENSE # License ├── .claude/ │ └── skills/ # Claude skills (this directory) └── ref/ # Reference notes (not curated) ├── my_collect.md # Personal collection ├── Awesome-AI-Security-1/ ├── awesome-ai-security-2/ ├── 模型安全/ # Model security notes ├── 渗透测试相关/ # Pentesting notes └── 网络安全相关/ # Network security notes

README.md Format Convention

Heading Structure

• Top-level categories use ## .

• Subcategories use ### (e.g., inside AI Security & Attacks ).

• Starter Pack uses bold bullets for sub-sections (e.g., - CTFs / Practice ).

Link Format

• Use full URLs, one per bullet line.

• Add a short description in square brackets: - https://... [Short description]

• Keep descriptions concise.

• Do not add the same URL in multiple places.

Example Entry

Prompt Injection

• https://github.com/example/tool [Prompt injection detector]

Categorization Rules (How to Place a New Link)

• AI Security Starter Pack: CTFs, courses, blogs, newsletters, beginner resources.

• AI/LLM Guide: LLM fundamentals, tutorials, awesome lists.

• AI Security & Attacks: Prompt injection, adversarial attacks, poisoning, privacy, model security.

• AI Pentesting & Red Teaming: AI-powered pentesting tools, red teaming, MCP security tools.

• AI Security Tools & Frameworks: AI vulnerability detection, CVE analysis, OSINT, security libraries.

• AI Agents & Frameworks: Agent frameworks, RAG, browser automation, MCP servers.

• AI Development & Training: Training frameworks, local models, uncensored models, prompts.

• AI Applications: Chat assistants, deep research, search engines, code analysis, web scraping.

• AI Image & Video: Image generation, video generation, TTS, face recognition.

• Benchmarks & Standards: AI safety benchmarks, threat frameworks, standards.

AI-Relevance Filter

Only include AI/ML-related resources. Do not add:

• Traditional security tools (unless AI-powered)

• Web3/blockchain tools (unless AI-related)

• General pentesting tools without AI integration

• Browser vulnerabilities, phishing tools, CVE collections (unless AI-analyzed)

Duplicate Policy

No duplicate URLs in README.md. If a link fits multiple categories, pick the primary one.

Contribution Checklist

• Check for duplicates in README.md before adding.

• Verify the resource is AI/ML-related.

• Verify the link points to the canonical source (avoid low-value forks).

• Keep the description concise and useful.

• Put it into the most appropriate category.

• Prefer minimal changes over reformatting large sections.

Data Source

For detailed and up-to-date resources, fetch the complete list from:

https://raw.githubusercontent.com/gmh5225/awesome-ai-security/refs/heads/main/README.md

Use this URL to get the latest curated links when you need specific tools, papers, or resources.