Threat Modeling for Agentic AI: Attacks, Risks, Controls

Modern AI systems are no longer passive language models. They plan, remember, use tools, and act autonomously.And that changes everything about security.Threat Modeling for Agentic AI is a deep, practical course dedicated to one critical reality: traditional threat modeling fails when applied to autonomous agents.This course teaches you how to identify, analyze, and control risks that emerge only in agentic systems - risks caused by memory poisoning, unsafe tool usage, reasoning drift, privilege escalation, and multi step autonomous execution.If you are building, reviewing, or securing AI agents, this course gives you the frameworks you cannot find in classical AppSec, cloud security, or LLM tutorials.Why this course existsMost AI security content focuses on:Prompt injectionRAG data leaksModel hallucinations in isolationThis course focuses on what actually breaks real agentic systems:Persistent memory corruptionCascading reasoning failuresTool chains that trigger real world actionsAgents escalating their own privileges over timeYou will learn how agents fail as systems, not as single model calls.What makes this course differentThis is not a conceptual overview.This is a system level security course built around real agent architectures.You will learn:How autonomy expands the attack surfaceWhy agent memory is a long term liabilityHow small hallucinations turn into multi step failuresWhere classical threat models completely miss agent specific risksEvery concept is tied to artifacts, diagrams, templates, and exerc