Red Teaming LLMs is a practical, hands-on guide to adversarial testing for modern AI systems. Written from real-world experience and hard-earned lessons, this book shows how to test, break, measure, and responsibly secure LLM-powered applications before attackers do it for you. This is not a collection of viral jailbreak tricks or shallow prompt experiments. It is a structured, professional approach to understanding how LLMs behave under pressure - and why traditional security testing is no longer enough.
Inside, you will learn what red teaming truly means for probabilistic, non-deterministic systems. You will understand how LLM red teaming differs from traditional penetration testing, why alignment testing is not the same as security testing, and how human creativity often outperforms automated safeguards. You will build a repeatable red team methodology, define assessment objectives, scope LLM capabilities, identify assets, and select attack classes that actually matter in production environments.
The book covers every layer of the real LLM attack surface:
Prompt Injection Testing - direct injection, indirect injection, multi-turn chains, instruction override, and regression testing for long-term resistance
Jailbreak Detection and Analysis - roleplay abuse, obfuscation techniques, multilingual attacks, and how to measure jailbreak resistance systematically
Context Window and Conversation State Attacks - how attackers exploit shared memory, session state, and retrieval-augmented generation inputs
Guardrail and Safety Control Bypass - testing whether your defenses hold under creative adversarial pressure or collapse at the first variation
Data Leakage and Privacy Risk Testing - how PII, secrets, and training data escape through inference, retrieval, and model output
Abuse, Denial of Service, and Economic Attacks - patterns that drain resources and rack up costs without ever crashing the system
Tool, Plugin, and API Attack Surfaces - what happens when models call external systems with attacker-controlled inputs
Automated Red Teaming - prompt fuzzing, adversarial generation, false positive management, and scaling testing without losing signal quality
Risk Measurement and Severity Assessment - how to prioritize findings honestly in systems where likelihood is fuzzy and impact is contextual
Reporting and Remediation - how to communicate findings clearly to engineers and executives and transition from one-time testing to continuous assurance
Every chapter is grounded in realistic attack scenarios, hands-on labs, reusable templates, and professional methodology you can deploy with your own teams immediately.
Red Teaming LLMs is Book 4 in the series:
The AI Security & Hacking Bible: Protect and Exploit LLMs and Autonomous Agents
If you have read LLM Security in Practice, AI Threat Modeling, and The LLM Top 10 Security Guide, this is where theory meets adversarial reality. If you are heading toward How AI Agents Work, Hardening AI Agents, and The AI Agent Attacker's Playbook, the red team methodology you build here will follow you through every subsequent volume. Red team findings feed directly into the secure design patterns, monitoring strategies, and incident response workflows covered later in the series.
This book is for you if you are a:
Security engineer or penetration tester expanding into AI and LLM assessment
Developer who wants to understand how attackers think about the systems you build
Red teamer looking for a structured, professional methodology for AI security testing
Security lead building a continuous assurance program for LLM-powered products
Anyone who has ever said "the model would not do that" - and needs to find out
