ai-llm-red-team-handbook/docs/SUMMARY.md

Summary

Introduction

• [Red Teaming AI & LLMs: The Consultant's Complete Handbook](AI LLM Red Team Handbook.md)

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: Operational Workflows

• Chapter 9: Writing Effective Reports and Deliverables
• Chapter 10: Presenting Results and Remediation Guidance
• Chapter 11: Lessons Learned and Building Future Readiness

Part IV: Technical Fundamentals

• 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

Reference Materials

• [Appendix A: Red Team Tools, Resources, and Further Reading](AI LLM Red Team Handbook.md#appendix-a-red-team-tools-resources-and-further-reading)