AI Threat Modeling: Secure LLM, RAG and Agent Systems

About this course

This course contains the use of artificial intelligence. Most AI security material either lists risks or teaches attacks. This course teaches the step in between: the design-time method that turns an AI system into a diagram, a ranked threat list, chosen controls and tests that prove those controls work. One system, three stages. We follow the Northwind Shop Assistant, a fictional online retailer's shopping and support assistant. It starts as a chat assistant on a hosted model, gains retrieval over the product catalog, reviews and order history, and ends as an agent that can issue refunds, apply discount codes and email customers through tools. Each stage adds attack surface, so the threat model grows lecture by lecture, the way real systems do. What the course covers Data-flow diagrams for AI systems, and the trust boundaries that only exist in AI: system prompt, user input, retrieved documents, tool results and model output The OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI 100-2 and the OWASP agentic guidance, mapped into one working threat catalog STRIDE per element adapted for AI components, prompt injection as a trust-boundary failure, excessive agency, supply chain and denial of wallet RAG threats: poisoned documents, retrieval manipulation and access control on retrieved data Agent and tool threats: confused deputy, over-broad scopes, tool poisoning, memory manipulation, and human-in-the-loop controls that actually work A simple AI risk score, written risk decisions, controls, guardrails and their limits, and turning threats into test cases Keeping the threat model current in the delivery pipeline, and a report an auditor or customer will accept Seven recorded labs, all with free tools.

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