AI Threat Modeling: Attack Surfaces and Risk Assessment for LLM Systems (The AI Security & Hacking Bible: Protect and Exploit LLMs and Autonomous Agents Book 2)
English | April 13, 2026 | ASIN: B0GWZS3K45 | 418 pages | Epub | 3.1 MB
AI Threat Modeling is the book I wish I had on my desk the first time an AI system behaved perfectly in testing and then failed creatively in production.Traditional threat modeling assumes software is predictable, obedient, and honest. Large language models are none of those things. They are probabilistic, improvisational systems that blur the line between code, data, configuration, and behavior. That is why classic threat models quietly fall apart when applied to AI. This book shows you how to rebuild them correctly.Inside, you will learn how to threat model modern LLM-based systems from the ground up. You will see why deterministic security assumptions fail, how to treat models as both critical assets and evolving attack surfaces, and how risk changes across training, deployment, inference, and autonomous decision-making. We move beyond prompt injection and examine the full AI attack surface - context windows, retrieval-augmented generation pipelines, memory systems, external tools, plugins, and downstream consumers that unknowingly turn AI output into execution.This book is deliberately practical. You will learn how to: Identify assets that do not look like assets in LLM-powered systemsMap real adversaries instead of imaginary hackers with unlimited resourcesBuild threat scenarios that actually happen in production environmentsModel misuse, abuse, data leakage, and cost exhaustion as first-class risksAssess cross-tenant failures and supply chain risks unique to AI systemsUnderstand how risk changes when LLMs become agents with goals, tools, and memoryPrioritize threats in probabilistic systems where likelihood is fuzzy and impact is contextualTranslate threat models into concrete security controls your team can actually implementKeep threat models alive through agile and DevOps workflows as systems evolveEvery concept is tied to realistic examples, workshops, hands-on labs, and reusable templates you can deploy with your own teams immediately.Risk analysis for AI systems gets special attention. You will learn how to prioritize threats when business risk often matters more than technical elegance, how to align threat models with secure design principles, and how to communicate risk in language that moves engineering and leadership toward action.AI Threat Modeling is Book 2 in the series:The AI Security & Hacking Bible: Protect and Exploit LLMs and Autonomous AgentsIf you have read LLM Security in Practice, this book is the natural next step - thinking ahead instead of reacting. If you are planning to tackle The LLM Top 10 Security Guide or Red Teaming LLMs, this book gives you the mental framework those efforts depend on. And if you are heading toward AI agents, autonomous tools, and real-world incident response, the threat modeling skills here will follow you through the rest of the series.This is not a compliance checklist. It is not academic theory. It is a practical, opinionated guide to understanding how AI systems fail - and how to reduce surprise when they do.This book is for you if you are a: Security engineer or architect designing LLM-powered systemsDeveloper who wants to think about risk before something breaks in productionEngineering lead responsible for AI system safety and reliabilityRisk or compliance professional extending existing frameworks into AIAnyone building, reviewing, or governing LLM systems at scaleYou cannot defend what you have not mapped. Start here.
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