Free tool

OWASP LLM Top 10 Scorecard

Answer honest yes, partial, or no control questions across all ten categories of the OWASP Top 10 for LLM Applications 2025. Get a per category status, an overall readiness grade, a view of your weakest areas, and a Markdown export you can drop into a ticket or a review.

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0 of 0 answered

Answer the questions to score

A yes earns full points, a partial earns half, a no earns none. Skip a question and it is left out of the math.

What this scorecard is, and what it is not

This is a self assessment. You answer plain questions about your own LLM application and it turns the answers into a readiness grade. That makes it useful for finding gaps and starting a conversation, and it makes it useless as proof of anything. It is not a certification, not an audit, and not a guarantee. The grade only reflects how honestly and completely you answered, so a high score on self reported controls is not the same as a tested system. Treat a weak area as a prompt to go test, not as a finding you have closed.

The categories follow the OWASP Top 10 for LLM Applications 2025, published by the OWASP GenAI Security Project. Where it helps, each category notes the matching OWASP AI Testing Guide identifiers, written in the AITG-APP-01 form, so you know which concrete test procedure lines up with the gap you just found.

The ten categories at a glance

How to read the grade

Each category holds a few control questions. A yes earns the full points, a partial earns half, and a no earns none. A category status is the share of its points you earned, and the overall readiness score is the share across every question you answered, shown out of one hundred. The letter grade uses the same banding as the other tools here: A for ninety and above, B for eighty, C for seventy, D for sixty, and F below that. The weakest areas list points you at the categories that lost the most ground, which is usually where the next hour of work pays off most.

Honesty is the whole game. A partial is the right answer when a control exists but is inconsistent, untested, or only covers part of the surface. Reaching for yes when you mean partial just hides the gap from yourself. If you want to move from a self check to verified coverage, the kind of reasoning an AI security testing approach brings is what turns a self reported yes into something you can actually trust.

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