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A support lead at a 2,000 employee fintech pings security. Their customer support chatbot just told a user how to bypass identity verification, and the transcript shows the bot confidently citing an internal policy. The question is deceptively simple. Are we looking at a normal model mistake, a prompt injection, a data leak, or a compromised dependency. If we cannot classify the incident fast, we cannot pick the right containment move, evidence to preserve, or team to pull in.
In this course, we use the OWASP Top 10 for LLM Applications 2026 as our shared map for triage, threat modeling, testing, and monitoring. Triage means making a fast, accountable call about what kind of failure is happening, what the likely blast radius is, and what to do next while facts are still changing. The goal in Lesson 1 is to practice naming what we see in production symptoms, using categories that stay consistent across teams and time.
When an LLM feature misbehaves, teams often argue about intent and wording. We will instead anchor on observable symptoms. What input sources fed the model, what outputs were produced, and what external effects followed. Those clues usually point to one or two OWASP categories even before we know root cause. Now we will triage a simulated incident and label the leading category, plus any close secondary categories worth tracking in parallel:
To classify correctly, we need a shared mental model for how an LLM application assembles a single request. Context window means the bundle of tokens the model sees for one response. It often includes direct user text, retrieved knowledge base snippets, tool outputs, and hidden instructions like a system prompt. When untrusted content enters that same context window, it can steer behavior, trigger data exposure, or cause unsafe downstream actions. In the next view, we will look at where untrusted content likely entered and how that produced the observed policy bypass:
Do not guess from the label
One symptom can map to multiple categories. Treat early labels as working hypotheses and preserve evidence before you optimize.
OWASP Top 10 for LLM Applications 2026 is a ranked set of the most critical risks for applications that use large language models. It exists so security, engineering, and governance teams can talk about the same failure modes with the same names, and so testing plans cover the full surface instead of the last incident. This course is mapped to all ten categories, and we will cover them directly in lessons and exercises rather than treating them as a reference appendix. Here is the full map we will use throughout the course: