The AI Hacker's Handbook is a practical guide to finding, exploiting, and reporting vulnerabilities in large language models (LLMs), AI applications, and AI agents.

The book explores prompt injection, indirect prompt injection, jailbreaks, system prompt leakage, data exfiltration, RAG security, memory poisoning, tool and function abuse, AI agent attacks, MCP security, multimodal vulnerabilities, browser agents, and other emerging AI security risks.

Rather than focusing only on theory, the book emphasizes practical security testing, exploitation, validation, impact assessment, and responsible vulnerability reporting. It is written for penetration testers, bug bounty hunters, security researchers, red teamers, developers, AI engineers, and cybersecurity professionals working with modern AI systems.

The book contains 36 chapters, 7 appendices, numerous technical figures, practical examples, and a companion collection of resources and labs.