llm-security
3 free lessons tagged llm-security across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Red-Teaming: Attacking Your Own System Before Someone Else Does
Guardrails you have not attacked are guardrails you have only hoped about. This lesson covers manual and automated red-teaming: the attack methods worth knowing, how to run an exercise that produces findings rather than reassurance, what to measure, and how to turn results into regression tests that survive the next model update.
Building the Control Layer: Rails, Classifiers, and Containment
Guardrails are a layered control system around a model that cannot police itself. This lesson covers the rail types, rules versus classifiers versus model-based judges, the tools that implement them, the latency and false-positive budget that constrains every design, and why architectural containment beats filtering.
The LLM Threat Model: Why the Model Cannot Defend Itself
Guardrails exist because a language model cannot reliably distinguish instructions from data. This lesson builds the threat model: the OWASP Top 10 for LLM Applications, direct and indirect prompt injection, jailbreaks, data leakage, and why tool-using agents turn a content problem into a security one.