Map LLM threats to controls, tests, and monitoring across the OWASP Top 10 for defenders
4 sections · 9 lessons
Define the signs of an LLM application compromise and map them to the OWASP Top 10 while setting the immediate containment goal.
1 lessonCompare prompt injection with adjacent risks, then choose controls, tests, and monitoring for disclosure, hidden context, agency, and output handling.
3 lessonsTrace threats through models, prompts, datasets, plugins, runtimes, training, fine-tuning, RAG, and feedback loops, then define verification, validation, and drift monitoring.
2 lessonsEvaluate consumption abuse, RAG weaknesses, and misinformation, then run an OWASP-mapped review with tests and monitoring runbooks.
3 lessonsA live LLM compromise is rarely just one bug, it is a chain of symptoms that must be tied back to the right OWASP category fast. This course gives security teams and AI/ML engineers a defender’s view of the major threat patterns in LLM and AI applications, with practical ways to think about containment, hardening, validation, and monitoring. By the end, learners can classify attacks, choose controls that interrupt them, and review an LLM system with a repeatable security lens.
The course covers prompt injection, sensitive information disclosure, hidden context exposure, excessive agency, improper output handling, supply chain risk, data and model poisoning, unbounded consumption, vector and embedding weaknesses, and misinformation. It also covers verification, testing, monitoring signals, and an OWASP-mapped review workflow for LLM security assessments.
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