ai-llm-red-team-handbook/docs

Introduction

Version 0.024.112

Welcome to the AI LLM Red Team Handbook.

This guide is designed to equip security consultants, red teamers, and AI practitioners with the mindset, methodologies, and technical skills required to assess and secure Large Language Models (LLMs) and AI systems. From foundational ethics to advanced attack vectors like prompt injection and model supply chain vulnerabilities, this handbook provides a structured approach to identifying risks in the rapidly evolving landscape of artificial intelligence.

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⚡ Quick Access: Field Manuals

Looking for the Operational Playbooks?

• Field Manual Index
• Quick Reference Card
• All Attack Playbooks
• Compact Field Manual (Single File)

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📚 Handbook Structure

Part I: Foundations

• Chapter 1: Introduction to AI Red Teaming
• Chapter 2: Ethics, Legal, and Stakeholder Communication
• Chapter 3: The Red Teamer's Mindset

Part II: Project Preparation

• Chapter 4: SOW, Rules of Engagement, and Client Onboarding
• Chapter 5: Threat Modeling and Risk Analysis
• Chapter 6: Scoping an Engagement
• Chapter 7: Lab Setup and Environmental Safety
• Chapter 8: Evidence, Documentation, and Chain of Custody

Part III: Technical Fundamentals (Core Concepts)

• Chapter 9: LLM Architectures and System Components
• Chapter 10: Tokenization, Context, and Generation
• Chapter 11: Plugins, Extensions, and External APIs

Part IV: Technical Fundamentals (Pipelines & Security)

• Chapter 12: Retrieval-Augmented Generation (RAG) Pipelines
• Chapter 13: Data Provenance and Supply Chain Security

Part V: Attacks & Techniques

• Chapter 14: Prompt Injection (Direct/Indirect, 1st/3rd Party)
• Chapter 15: Data Leakage and Extraction
• Chapter 16: Jailbreaks and Bypass Techniques
• Chapter 17: Plugin and API Exploitation
• Chapter 18: Evasion, Obfuscation, and Adversarial Inputs
• Chapter 19: Training Data Poisoning
• Chapter 20: Model Theft and Membership Inference
• Chapter 21: Model DoS and Resource Exhaustion
• Chapter 22: Cross-Modal and Multimodal Attacks
• Chapter 23: Advanced Persistence and Chaining
• Chapter 24: Social Engineering with LLMs

Part VI: Defense & Mitigation

• Chapter 25: Advanced Adversarial ML
• Chapter 26: Supply Chain Attacks on AI
• Chapter 27: Federated Learning Attacks
• Chapter 28: AI Privacy Attacks
• Chapter 29: Model Inversion Attacks
• Chapter 30: Backdoor Attacks

Part VII: Advanced Operations

• Chapter 31: AI System Reconnaissance
• Chapter 32: Automated Attack Frameworks
• Chapter 33: Red Team Automation
• Chapter 34: Defense Evasion Techniques
• Chapter 35: Post-Exploitation in AI Systems
• Chapter 36: Reporting and Communication
• Chapter 37: Remediation Strategies
• Chapter 38: Continuous Red Teaming
• Chapter 39: AI Bug Bounty Programs

Part VIII: Advanced Topics

• Chapter 40: Compliance and Standards
• Chapter 41: Industry Best Practices
• Chapter 42: Case Studies and War Stories
• Chapter 43: Future of AI Red Teaming
• Chapter 44: Emerging Threats
• Chapter 45: Building an AI Red Team Program
• Chapter 46: Conclusion and Next Steps

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Field Manuals & Reference

• Configuration Guide
• Field Manual Index