OWASP LLM Top 10 Explained for Beginners

If your app uses a large language model — a chatbot, an AI feature, an agent that takes actions — the OWASP LLM Top 10 is the reference you want. It's a list, maintained by the OWASP Foundation, of the ten most critical security risks specific to LLM-powered applications. This is a beginner-friendly walkthrough: what each risk means in plain English, and what you can do about it without a security background.

Why LLM apps need their own Top 10

The classic OWASP Top 10covers general web risks and still applies to your app's ordinary parts. But LLMs introduce risks that don't fit the old list: models that can't tell instructions from data, outputs you can't fully trust, and agents that can take real actions. The LLM Top 10 names those new risks so you can reason about them before you ship.

The ten risks, in plain English

1. Prompt injection

Malicious input that manipulates the model into ignoring its instructions. It's the number-one risk because models can't reliably separate trusted instructions from untrusted input. We cover it in depth in prompt injection explained.

2. Insecure output handling

Trusting the model's output blindly. If you drop LLM output straight into a database query, a shell command, or a web page, an attacker can use the model as a delivery vehicle for injection or XSS. Treat model output like any other untrusted input.

3. Training-data poisoning

Corrupting the data a model learns from so it behaves badly. Most app builders don't train their own models, but if you fine-tune or feed the model a knowledge base, the integrity of that data matters.

4. Model denial of service

Overloading the model with expensive requests to run up your bill or degrade service. Rate limiting and input-size limits are the practical defenses.

5. Supply-chain vulnerabilities

Risks from the components you build on — third-party models, datasets, and packages. This is also where slopsquatting lives: AI-hallucinated packages that attackers register as malicious.

6. Sensitive information disclosure

The model revealing data it shouldn't — secrets in its context, another user's data, or its own system prompt. Don't put anything in the model's context that the user isn't allowed to see.

7. Insecure plugin/tool design

When the tools you give a model are too powerful or under-validated, a manipulated model can misuse them. Give each tool the minimum capability it needs and validate its inputs.

8. Excessive agency

Giving the model too much autonomy — the ability to take consequential actions without a human or an authorization check in the loop. The fix is to gate sensitive actions behind checks the model can't talk its way past.

9. Overreliance

Trusting model output as fact when it can be confidently wrong. In a coding context this is how insecure code and hallucinated packages slip through — the model sounds sure, so no one checks.

10. Model theft

Unauthorized access to or extraction of a proprietary model. Mainly a concern if you've invested in a custom model worth stealing.

What to actually do about it

You don't need to solve all ten to be responsible. For most AI-built apps, the highest-value moves are: treat model output as untrusted, never let the model take a sensitive action without a separate authorization check, limit the data and tools it can reach, and rate-limit usage. Those four cover the risks that cause real damage.

And remember that your app has an ordinary attack surface too — the LLM Top 10 sits on top of the normal web risks, it doesn't replace them. Scan the whole thing: paste your live URL into Sayver's free website security scan to check the conventional gaps (exposed keys, missing access control, headers), then apply the LLM-specific defenses above.