OWASP LLM Top 10: a practical testing guide for 2026
OWASP LLM Top 10: a practical testing guide for 2026
In 2026, almost every company ships AI features. Some are chatbots. Some are agentic coding assistants. Some are RAG pipelines stitched together by a product manager who "just wanted to try it". All of them inherit a new class of security problems that traditional pentests do not cover.
The OWASP Foundation publishes an industry-standard list called the OWASP Top 10 for Large Language Model Applications. The 2025 version is the current reference, and it applies to every AI feature in production today. If you are shipping anything that calls an LLM, this is the list you use to sanity-check your security posture.
This guide is the practical version. Not theoretical definitions. What to actually test, what payloads to try, and how to automate it.
Why traditional pentests miss AI risks
A standard web application pentest checks for SQL injection, XSS, CSRF, broken authentication, and the other OWASP Web Top 10 items. It runs Burp or ZAP against your HTTP endpoints. That process was built for 2010 web apps. It is not wrong. It is just not aimed at the right target.
Your AI features introduce an attack surface that is invisible to Burp:
- The system prompt that shapes every model response
- The model's ability to call tools using your production permissions
- User input that flows through the model verbatim into downstream actions
- Embeddings stored in a vector database that an attacker can poison
- The exact wording the model uses when it thinks it is helping
None of those are tested by a conventional scanner. If you only buy traditional pentests, you are buying a 2010 inspection for a 2026 problem.
The OWASP LLM Top 10 (2025), in one sentence each
| ID | Risk | What it means | |---|---|---| | LLM01 | Prompt Injection | Attacker input overrides your system prompt instructions | | LLM02 | Sensitive Information Disclosure | Model reveals secrets from training data, tool output, or prior context | | LLM03 | Supply Chain | Compromised model weights, datasets, or dependencies | | LLM04 | Data and Model Poisoning | Attacker influences training or fine-tuning data | | LLM05 | Improper Output Handling | Model output flows into another system without validation | | LLM06 | Excessive Agency | Your AI agent has more tool permissions than it needs | | LLM07 | System Prompt Leakage | Your proprietary system prompt ends up in the wrong hands | | LLM08 | Vector and Embedding Weaknesses | Attacker contaminates the RAG index | | LLM09 | Misinformation | Model confidently states wrong facts and a downstream system trusts them | | LLM10 | Unbounded Consumption | Expensive inference loops that drain your wallet |
Those are the categories. The rest of this article is how to actually test each one.
LLM01: prompt injection
What to test
Every place your application takes untrusted text and feeds it to the model. That includes: user messages, uploaded documents, web pages the agent fetches, email bodies processed by an AI inbox, PDF attachments, Slack messages, and code comments that a coding assistant reads.
Concrete payloads
Direct injection:
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