RAG & Retrieval
OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications is a ranked list of the most critical security risks in apps built on large language models, published by the OWASP Gen AI Security Project.
In depth
The OWASP Top 10 for LLM Applications is a ranked list of the most critical security risks in apps built on large language models, published by the OWASP Gen AI Security Project. The 2025 edition runs from prompt injection (LLM01) through sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, and misinformation, to unbounded consumption (LLM10). Each entry gives the risk, example attacks, and mitigations. Teams often use it as a checklist when threat-modeling agents and RAG apps.
Primary source: genai.owasp.org
Example
In practice, developers reach for OWASP Top 10 for LLM Applications when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is OWASP Top 10 for LLM Applications?
The OWASP Top 10 for LLM Applications is a ranked list of the most critical security risks in apps built on large language models, published by the OWASP Gen AI Security Project.
Why does OWASP Top 10 for LLM Applications matter for AI developers?
OWASP Top 10 for LLM Applications sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about OWASP Top 10 for LLM Applications?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including OWASP Top 10 for LLM Applications. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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The date after which a model has no training data.
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A sequence of automated steps that move and transform data from source to destination.
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Knowledge Base
A structured repository of information that an AI system can query to answer questions or provide context.
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