The OWASP LLM Top 10, Translated for Founders Shipping AI

TL;DR — If you are shipping an AI feature — a chatbot, a RAG assistant, an agent that calls tools — the OWASP Top 10 for LLM Applications is the shortest path to knowing what can go wrong. The single risk that matters most is prompt injection: untrusted text that reaches your model can rewrite its instructions. Everything else compounds from there. You don't need to memorize ten items; you need to know which three will actually hurt you, and design against them.

The three that will bite first

LLM01 · Prompt injection. Your model can't tell your instructions from content it's reading. A poisoned support ticket, a booby-trapped web page your agent fetches, a hidden line in an uploaded PDF — any of these can say "ignore your previous instructions and…" and the model may comply. Indirect injection (through data the model retrieves, not what the user types) is the version that surprises teams, because the attacker never touches your chat box.

LLM02 · Sensitive information disclosure. Models repeat what's in their context. If you stuff a system prompt with API keys, other users' data, or internal reasoning, assume a determined user can get it back out. Context is not a vault.

LLM06 · Excessive agency. The moment your model can do things — send email, run a query, call a payment API — a successful injection becomes a successful action. The blast radius of a jailbreak is exactly the set of tools you handed the model.

What actually reduces the risk

• Treat all model input as untrusted — including retrieved data. The document your RAG pipeline pulled is attacker-controlled until proven otherwise.
• Put authority in code, not in the prompt. "Please don't reveal the system prompt" is a wish. An allow-list of tools, per-user scopes, and server-side checks on every tool call are controls.
• Constrain tools, then constrain their outputs. Least privilege for the model is the same discipline as least privilege for a service account. A read-only tool can't be tricked into a write.
• Log the whole chain. Prompt, retrieved context, tool calls, tool results. When something goes wrong, you'll need to see what the model actually saw.

Why "we tested the chatbot" isn't enough

Typing adversarial prompts into your own chat window finds the easy stuff. The findings that matter live in the indirect paths — the RAG corpus, the tool responses, the agent's browsing — and in what the model is allowed to do once persuaded. That's an offensive exercise against the whole system, mapped to MITRE ATLAS and OWASP LLM Top 10 (2025), not a spelling test for your prompt.

Where this maps

• OWASP Top 10 for LLM Applications (2025) — LLM01, LLM02, LLM06
• MITRE ATLAS — adversarial ML tactics & techniques
• NIST AI RMF and ISO/IEC 42001 — governing the same risks at the management-system level

FAQ

Is prompt injection a solved problem yet? No. There is no reliable way to make a model perfectly separate instructions from data. You manage it by limiting what a compromised model can do, not by hoping it won't be fooled.

Do guardrail filters fix it? They raise the bar and catch obvious attempts. They don't eliminate the class — attackers rephrase, encode, or move to indirect channels. Filters are a layer, not the answer.

We're small — is this really a threat to us? The exposure scales with agency, not company size. A tiny app that lets a model send emails or touch customer data has a real attack surface. If your AI can only draft text a human sends, your risk is lower — and worth confirming.