AI LLM Security: The OWASP Top 10 for LLM Apps Explained
AI LLM security is its own discipline. We walk through the OWASP Top 10 for LLM applications with real mitigation tools and data.
💡 What You Will Learn
AI LLM security is its own discipline. We walk through the OWASP Top 10 for LLM applications with real mitigation tools and data.
📜 Table of Contents
Your AI app has a threat model that traditional security training never covered: prompt injection, data poisoning, insecure output handling. The OWASP Top 10 for LLM Applications is the closest thing to a standard - here is what actually matters in production.
The Top Threats
1. Prompt injection - the big one; mitigate with llm-guard (3,202 stars) or NeMo Guardrails (6,880 stars). 2. Sensitive information disclosure - enforce least-privilege on what the model can retrieve (RAG scoping). 3. Insecure output handling - never pass model output straight to a shell or SQL; validate and encode it. 4. Data poisoning - pin your training and retrieval datasets, hash them. 5. Excessive agency - give agents the minimum tools and require human approval for destructive actions.
The other five (SSRF via web tools, over-reliance, model theft, supply chain, improper error handling) matter, but the first five cause the real incidents. A pragmatic checklist: input filter, output validation, least-privilege tools, dataset hashing, and a human-approval step for anything destructive.
Comparison
| Threat | Mitigation |
|---|---|
| Prompt injection | llm-guard / NeMo Guardrails |
| Data leakage | RAG least-privilege scoping |
| Unsafe output | Validate + encode |
| Data poisoning | Pin and hash datasets |
| Excessive agency | Min tools + human approval |
FAQ
Q: Where can I learn the full OWASP list?
A: The OWASP LLM Top 10 is on the OWASP website and GitHub - read the original, not summaries, when designing controls.
Q: Do I need all 10 mitigations before launch?
A: Start with the first five; the rest can be hardened iteratively with monitoring.
Written by our editorial team; tools listed here are tested or verified against public sources. Links point to official sites or GitHub repos for reference only โ no paid placements.
