AI Red Teaming: Test, Break, and Secure Large Language Models

Your AI application is one clever prompt away from being compromised. Do you know how to stop it? Large language models don't break like traditional software. They can be jailbroken with a roleplay, tricked into leaking their system prompt, poisoned through the data they read, and manipulated into leaking training data or executing dangerous tool calls — and none of it looks like a normal attack. AI Red Teaming is the hands-on playbook for security professionals, ML engineers, and developers who need to test and defend LLM systems in the real world. No hand-waving, no theory dumps — just the techniques attackers actually use, and the defenses that actually work. Inside, you'll learn how to: Exploit prompt injection — the "SQL injection of AI" — and detect it before attackers do Bypass safety guardrails with jailbreak techniques like DAN, encoding attacks, and many-shot framing Extract system prompts, training data, and PII through targeted probing Attack multimodal models and LLM agents that use tools, APIs, and code execution Automate red teaming at scale with Garak, PyRIT, and custom Python harnesses Build defense-in-depth with Llama Guard, NeMo Guardrails, input validation, and output filtering Map your findings to the NIST AI RMF and EU AI Act — and write red-team reports that hold up Every chapter is built around working code, real tools, and a local lab you set up yourself. Whether you're securing a production LLM, building an enterprise red-team program, or breaking into AI security, this is the field manual you'll keep coming back to. Stop hoping your AI is secure. Start proving it.