OWASP Top 10 for LLM Applications 2026

Published in August 2026, the OWASP Top 10 for LLM Applications is a threat-modeling guide from the OWASP GenAI Security Project that ranks major security risks in software using large language models, based primarily on community input supplemented by classified incident data. Prompt Injection and Sensitive Information Disclosure retained the top two positions, while Excessive Agency rose to third and Unbounded Consumption to sixth as tool-using agents increase the potential impact of unauthorized actions and runaway costs; System Prompt Leakage was renamed Hidden Context Exposure to include developer instructions, tool schemas, retrieved policies, and other concealed context. The remaining risks cover supply-chain compromise, data and model poisoning, misinformation, vector and embedding weaknesses, and improper output handling, with examples including the EchoLeak Copilot vulnerability, a LiteLLM package compromise, an AI agent’s reported deletion of a production database, and stored cross-site scripting in AnythingLLM. The guide emphasizes layered safeguards such as treating retrieved content and model output as untrusted, restricting tool permissions, enforcing human approval for consequential actions, validating supply-chain provenance and training data, applying spending and rate limits, protecting vector-store access, and grounding or reviewing high-impact answers. It also notes that runtime security controls can inspect prompts, responses, tool calls, and usage but cannot replace secure application code, data governance, model evaluation, or access controls in retrieval systems.

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