OWASP Top 10 for LLM Applications

Expanded Definition

OWASP Top 10 for LLM Applications is a community-driven risk catalogue for systems that use large language models in production. It is not a product checklist and it is not a formal standard; it is a practical way to describe the most important failure patterns that arise when an application delegates interpretation, generation, or tool use to an LLM.

The term covers prompt handling, retrieval-augmented workflows, model outputs, tool invocation, and surrounding controls such as filtering, access boundaries, and logging. It excludes generic AI concepts that do not materially change the security profile of the application. A common boundary mistake is to treat “LLM application security” as only a model problem, when many of the highest-impact failures emerge in orchestration, data flow, and trust decisions around the model.

For readers comparing authority sources, OWASP’s page is best used as a risk vocabulary for builders, while NIST AI Risk Management Framework provides a broader governance lens for AI risk management.

Examples and Use Cases

Teams usually encounter this framework when they are reviewing how an LLM is embedded into a real workflow rather than when they are testing a model in isolation. The risks become concrete once the application can read data, answer users, call tools, or influence downstream decisions.

• A customer-support chatbot is given access to knowledge base content and must resist prompt injection embedded in retrieved documents.
• An internal assistant summarizes tickets or documents and must avoid leaking sensitive data from prompts, context windows, or outputs.
• An agentic workflow uses LLM output to trigger actions, which creates a need to test tool abuse, unsafe delegation, and excessive autonomy.
• A RAG system is tuned to retrieve the “right” source, but the real issue is whether source selection, filtering, and citation handling are trustworthy.
• A development team uses the framework during design reviews to decide where human approval, rate limiting, or output validation is required.

Implementation trade-offs often appear between usability and restraint: tighter controls reduce harmful behavior, but they can also make the assistant less useful if they block legitimate context or action.

Security Implications

Misunderstanding the OWASP Top 10 for LLM Applications can lead teams to secure the model while leaving the application exposed. That creates false confidence, because attackers and abuse paths often exploit the gap between what the model “knows” and what the application allows it to do.

Common consequences include prompt injection that changes system behavior, sensitive-data exposure through overbroad context, untrusted retrieval that contaminates answers, and tool misuse that turns text generation into real-world action. In practice, the failure condition is often weak separation between user input, system instructions, retrieval content, and execution authority.

A practitioner should watch for symptoms such as surprising tool calls, inconsistent refusal behavior, output that mirrors hidden instructions, or repeated leakage of internal prompts and stored content. These are usually signs that the control boundary is in the application layer, not the model layer.

The main blast radius is operational trust: once the assistant can be steered into wrong answers or unauthorized actions, the impact can spread into customer support, engineering, finance, or incident response workflows.

Domain and Governance Relevance

This term matters most in AI application governance, where teams need a shared language for recurring failure classes. It helps security, engineering, and product owners discuss whether a given LLM feature is informational only, advisory, or capable of taking actions that require stricter control.

For NHI-adjacent systems, the relevance becomes sharper when an LLM or agent can use tokens, service accounts, APIs, or delegated tools. At that point, the question is no longer only whether the model responds safely, but whether the surrounding identity and authorization model prevents excessive agency and unintended access.

That is why the framework is useful in design review, threat modeling, and control validation. It gives organisations a way to align risk discussions around real application behavior instead of treating all AI usage as a single category.

Used well, it supports governance decisions about ownership, testing, and escalation thresholds without pretending that every LLM feature belongs to the same risk tier.

Related resources from NHI Mgmt Group

• How should security teams map the OWASP Top 10 for Agentic Applications into existing control frameworks and threat models?
• What is the difference between the OWASP LLM Top 10 and the OWASP Agentic Top 10?
• OWASP Top 10 for Agentic Applications 2026
• What are the core risks identified by the OWASP Agentic Top 10?