What is the OWASP Top 10 for LLM Applications?

The OWASP Top 10 for LLM Applications is a community security risk list. It names the ten most serious vulnerability classes that show up in applications built on large language models. It is published by the OWASP GenAI Security Project, the open community behind the web application list security teams have trusted for two decades.

It is a risk list, not a standard you get audited or certified against. There is no OWASP audit to pass or fail. You test your application against the risks, find what is exploitable, and fix it.

That distinction matters for how you buy. An OWASP LLM Top 10 platform is a testing platform, built to surface the listed risks before an attacker does.

Why the OWASP LLM Top 10 matters for enterprise AI

Language model applications fail in ways traditional software does not. The same input that looks harmless can carry an instruction that hijacks the model. The OWASP list turns that worry into a concrete checklist: instead of asking whether your AI is "safe", your team can ask whether each named risk is present and exploitable. A few worth naming:

• Prompt injection, where a crafted input overrides the system's instructions. The headline risk, and the one that maps to the widest range of attacks.

• Insecure output handling, where the application trusts model output and passes it into a browser, shell, or database unchecked.

• Sensitive information disclosure, where the model reveals training data, secrets, or another user's data.

• Excessive agency, where an agent has more permission or tool access than the task needs, so one bad decision causes real damage.

For a regulated enterprise, these are not abstract. A prompt-injection flaw in a customer-facing assistant is a live security incident and, depending on the outcome, a regulatory one.

How to evaluate an OWASP LLM Top 10 platform

Most tools claim coverage. What coverage means in practice is the question. Four criteria are worth applying before you buy.

Does it cover every category, not just prompt injection?

Prompt injection gets the headlines, so many tools test it well and treat the rest as an afterthought. Ask for a category-by-category mapping, where each risk maps to specific tests you can run and read.

Is the testing continuous, or a one-off?

New attack techniques ship every week, so a test you ran at launch tells you nothing about the jailbreak published last Tuesday. A real platform tests continuously and keeps its attack library current. A point-in-time scan is a snapshot of a target that keeps moving.

Does it test agentic behaviour, not just single prompts?

Most enterprise AI is heading toward agents that call tools, chain steps, and act with autonomy. Excessive agency only shows up when you test the agent in motion, across multiple turns. Check whether the platform tests multi-turn attacks and agent tool use, or only single-prompt inputs.

Can the results feed your compliance work?

Security testing and compliance are separate jobs that share the same evidence. If your platform produces raw logs no one can hand to an auditor, you do the work twice. The stronger pattern is one where the test results assemble into audit-ready evidence for the frameworks you report against.

How Disseqt tests against the OWASP LLM Top 10

Test and Detect is the Disseqt pillar built for this job. It tests an LLM application against the full OWASP risk list before and after deployment, with 65 ML-based validators across safety, security, fairness, and reliability, in four families: base, RAG, agentic, and MCP. The families match how your application is actually built.

On the attack side, Disseqt runs 84 jailbreak techniques, single-turn and multi-turn, the attacks a real adversary would try. That surfaces prompt injection, insecure output handling, and sensitive-information disclosure in practice. The agentic and MCP families then test tool use and autonomous decisions, where excessive agency lives, so you see what an over-permissioned agent will do before it reaches production.

Disseqt uses ML-based validators rather than asking another large language model to judge the output. That keeps validation fast, around sub-50ms inline latency, and far cheaper at scale, around 99% less water and 98% less CO2 than an LLM-as-judge approach. Continuous, large-scale testing becomes viable instead of a budget line you ration.

Underneath it all sits a Live Vulnerability Database that tracks new attack techniques and model vulnerabilities as they emerge. When a new jailbreak or injection method appears, the test library updates, so your coverage moves with the threat. That is the difference between testing against OWASP once and covering it continuously.

From OWASP coverage to audit-ready evidence

Testing against the OWASP LLM Top 10 produces a security record. On its own that record is not compliance evidence, but it is the raw material for it.

Prove and Comply assembles the test results into tamper-evident audit trails and compliance dashboards, mapped to the frameworks enterprises report against. The EU AI Act expects a risk management process and post-market monitoring for high-risk systems; ISO/IEC 42001 expects a managed AI management system with evidence of control. OWASP testing feeds both.

That is what makes Disseqt the AI assurance layer rather than a standalone scanner. Security testing and compliance evidence come from one platform, so you do the work once and use it twice.

Who this is for

This is for enterprise security and AI teams deploying language model applications in financial services, regulated industries, and any organisation where a prompt-injection incident is also a reportable one. If your security team owns the OWASP risks and your compliance team owns the audit, a single platform that serves both is the efficient choice. See the full risk-by-risk breakdown on the OWASP LLM Top 10 page.

Bottom line

The OWASP Top 10 for LLM Applications tells you how your model will be attacked. It is a risk list to test against, not a certificate to chase.

The platform you choose should cover every category, stay current as attacks evolve, test agents the way they run, and turn the results into evidence auditors accept. Disseqt does all of that on one platform.