What you'll learn

AI applications do not have to leak data to create serious risk. They can also burn budget, exhaust queues, loop through tools, hide evidence, or become unreliable under patterns that look like normal use. In this course, Abuse and Operational Attacks for AI, you'll learn how to safely demonstrate those risks in a local, capped lab. First, you'll discover how to map where prompts, completions, retrieval, tool calls, retries, queues, and recursive calls multiply work. Next, you'll explore denial-of-wallet and distributed budget-bypass patterns without creating real provider spend. Then, you'll trigger tool loops and latency degradation, apply circuit breakers and timeouts, and validate the result. Finally, you'll package P99 latency, throughput, fake billing, budget, and evidence data into language product owners and engineering teams can act on. When you’re finished with this course, you’ll have the skills and knowledge to identify, demonstrate, and mitigate operational abuse in AI applications while clearly communicating its impact.