In Depth
OWASP is a nonprofit known for its Top 10 lists, which distill a field's most important security risks into a reference everyone can point to. Its Top 10 for LLM Applications, first released in 2023 and updated since, does that for systems built on language models. The list names risks such as prompt injection, sensitive information disclosure, supply-chain vulnerabilities, data and model poisoning, improper output handling, and excessive agency, which is giving an agent more autonomy or permission than it needs.
Its value is that it is concrete and threat-oriented rather than abstract. Where a governance framework like the NIST AI RMF tells you how to organize risk management, the OWASP list tells you which specific failure modes to test and defend against. The two complement each other: one is the management structure, the other is the checklist of attacks that structure has to cover.
For AI vendors, the OWASP list has become a common vocabulary in security conversations. When a buyer's security team asks how an agent handles prompt injection or excessive agency, they are often working straight from it, which makes it a practical guide to what red-teaming should cover.
What It Looks Like
A vendor preparing an agent for enterprise sale uses the OWASP Top 10 for LLM Applications as a test plan. It red-teams for prompt injection, checks whether the agent can disclose sensitive data through its outputs, reviews how much agency and how many permissions the agent really needs, and validates output handling. When the customer's security questionnaire asks about these exact risks, the vendor already has evidence for each one, mapped to a list the reviewer recognizes.
Why It Matters For AI Vendors
Buyers increasingly frame their AI security questions around the OWASP list, so being able to speak to each risk, and show testing for it, moves a review along. It also gives a vendor's own team a prioritized starting point instead of a blank page. Because most of the listed risks (prompt injection, data disclosure, excessive agency) map directly to the failure modes that drive AI liability claims, working through the list is also a way to reduce the exposures an insurer cares about.