awesome-ai-security-overview

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, TLS / fingerprint / bot signals (JA3 clients, site bot detection, automation hardening research—use only ethically and on authorized targets).
• AI Agents & Frameworks: Agent frameworks, formal methods / Lean agents (e.g. AI-assisted theorem proving orchestration), AI memory & long context (latent memory, recursive context, long-memory RAG), RAG stacks/collections, browser automation, MCP servers, agent sandboxes & isolation (policy-enforced runtimes, container/VM boundaries).
• AI Development & Training: Training frameworks, local models, uncensored models, prompts.
• AI Applications: Chat assistants, deep research, search engines, code analysis, web scraping, vision / domain apps (e.g. agricultural or specialized image understanding with LLMs).
• AI Image & Video: Image generation, video generation, TTS, face recognition.
• Benchmarks & Standards: AI safety benchmarks, threat frameworks, standards.