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AI / LLM Red Team Field Manual & Consultant’s Handbook
| Date | Stars |
|---|---|
| 2026-07-31 | 318 |
| 2026-08-05 | 320 |
| 2026-08-06 | 320 |
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# AI / LLM Red Team Field Manual & Consultant's Handbook       A comprehensive operational toolkit for conducting **AI/LLM red team assessments** on Large Language Models, AI agents, RAG pipelines, and AI-enabled applications. This repository provides both tactical field guidance and strategic consulting frameworks. 📖 **GitBook Navigation:** See [SUMMARY.md](docs/SUMMARY.md) for the complete chapter structure. --- ## 📚 Overview This repository represents the **Gold Master** release of the AI LLM Red Team Handbook. It contains a fully standardized, 46-chapter curriculum covering the entire spectrum of AI security—from prompt injection and jailbreaking to adversarial machine learning and federated learning attacks. ### Core Resources 1. **AI LLM Red Team Handbook** (46 Chapters, Completed) - Professional consultancy guide with standardized metadata, abstracts, and navigation. - Covers Ethics, Architectures, RAG Security, Agentic Threats, and Compliance (EU AI Act/ISO 42001). 2. **AI LLM Red Team Field Manual** - Compact operational reference for field use (checklists, payloads, methodology). 3. **Python Testing Framework** (`scripts/`) - Automated suites for prompt injection, fuzzing, and safety validation. --- ## 📁 Repository Structure ```text ai-llm-red-team-handbook/ ├── docs/ # The Handbook (Chapters 01-46) │ ├── archive/ # Historical versions │ ├── assets/ # Diagrams, charts, and visual aids │ ├── field_manuals/ # Operational checklists and quick-refs │ ├── templates/ # Report and SOW templates │ └── SUMMARY.md # Master Table of Contents ├── scripts/ # Automated testing tools (Python) ├── workflows/ # CI/CD and automation workflows ├── .agent/ # Agentic memory and context ├── LICENSE # CC BY-SA 4.0 License └── README.md # This file ``` --- ## 🚀 Installation & Usage ### 1. Manual Exploration (The Handbook) The primary way to use this repository is as a reference. - Start at [SUMMARY.md](docs/SUMMARY.md) to browse all chapters. - View the [Field Manual](docs/AI_LLM%20Red%20Team%20Field%20Manual.md) for quick lookups during an engagement. ### 2. Automated Testing Environment To run the provided Python scanning and fuzzing scripts: **Prerequisites:** - Python 3.8+ - API Access to a target LLM (OpenAI, Anthropic, or local Ollama) **Setup:** ```bash # Clone the repository git clone https://github.com/shiva108/ai-llm-red-team-handbook.git cd ai-llm-red-team-handbook # Install dependencies cd scripts pip install -r config/requirements.txt ``` **Running Tests:** ```bash # Set up your environment variables (API Keys) cp .env.example .env nano .env # Run the test runner python examples/runner.py --target "gpt-4" --test "prompt_injection" ``` --- ## 📖 Chapter Roadmap (Completed) This handbook is divided into 8 strategic parts. All chapters are now **complete** and audited. ### Part I: Professional Foundations - **Ch 01-04:** Introduction, Ethics, Mindset, Rules of Engagement. ### Part II: Project Preparation - **Ch 05-08:** Threat Modeling, Scoping, Lab Setup, Chain of Custody. ### Part III: Technical Fundamentals - **Ch 09-11:** LLM Architectures, Tokens, Plugins/APIs. ### Part IV: Pipeline Security - **Ch 12-13:** RAG Pipelines, Supply Chain Security.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9b296cbc4186da9d, llm:Repository description: 'AI / LLM Red Team Field Manual & Consultant’s Handbook' (focus on red teaming for AI/LLMs).
matched fp:9b296cbc4186da9d, llm:Repository description: 'AI / LLM Red Team Field Manual & Consultant’s Handbook' (focus on red teaming for AI/LLMs).