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Updated on October 9, 2026
The OWASP Top 10 for large language model (LLM) Applications is a ranked list of the most common security risks found in software built on LLMs. The Open Worldwide Application Security Project (OWASP) publishes and maintains the list through its GenAI Security Project. It gives you a shared vocabulary for talking about risks like prompt injection, data leakage, and agents that take actions you didn’t intend. If you build, buy, or secure AI-powered tools, this list gives you a starting point for evaluating where those tools might fail.
This guide walks through how the list is built, how teams put it to work, where it applies, and where it falls short.
Technical Architecture and Core Logic
The OWASP Top 10 for LLM Applications groups risk into ten categories. Each category names a failure mode that shows up across chatbots, coding assistants, retrieval-augmented search tools, and autonomous agents. The categories cover the full lifecycle of an LLM application: how it’s trained, how it processes input, how it stores and retrieves data, and how it acts on the output it generates.
The core logic behind the list comes from a few traits unique to LLM applications. First, these systems accept natural language as both instructions and data, so an attacker can hide commands inside what looks like ordinary text. This is the basis for prompt injection, one of the most cited risks on the list. Second, many LLM applications now connect to external tools, databases, and APIs, which widens the attack surface far beyond the model itself. Third, LLMs sometimes generate false or misleading output with confidence, which creates trust problems for anyone relying on that output for decisions.
The list also accounts for newer, agent-specific risks. As LLMs move from answering questions to taking action, categories like excessive agency and tool use overreach describe what happens when an AI agent is granted more permission or autonomy than its task requires.
Mechanism and Workflow
OWASP builds this list through an open, community-driven process. Security researchers, developers, and practitioners contribute real-world examples, vulnerability reports, and mitigation strategies through working groups. The project reviews and revises the list on a recurring basis, so categories can shift, merge, or get renamed as the technology and threat landscape change.
Each category in the list includes a description of the risk, common attack scenarios, and suggested mitigations. This structure lets you use the list in a few practical ways:
- Risk identification: Review each category against your own AI application to spot gaps in design or data handling.
- Mitigation planning: Use the suggested controls tied to each category to prioritize fixes.
- Communication: Reference a shared category name, like memory poisoning or sensitive information disclosure, so security teams and developers describe the same problem the same way.
Because the list is published openly and updated by the community, you can treat it as a living reference rather than a one-time checklist.
Use Cases
Security teams and developers turn to the OWASP Top 10 for LLM Applications at several points in the software lifecycle. Before deployment, you can use it as a checklist during design reviews, so risks like data poisoning or output handling flaws get caught before an application goes live. During procurement, the list gives you a framework for asking vendors direct questions about how their AI products defend against each named risk.
The list also supports ongoing operations. Security teams reference it while writing incident response playbooks for AI-specific threats, since traditional application security playbooks don’t always cover risks like prompt injection or excessive agent permissions. Training programs use the categories to teach developers what “secure by design” looks like for generative AI, rather than treating AI security as an afterthought bolted onto existing application security training.
Compliance and audit teams also map the list against broader frameworks, such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework or ISO/IEC 42001, to show that AI-specific risks are covered inside a larger governance program.
Limitations
The OWASP Top 10 for LLM Applications is a risk awareness tool, not a certification or compliance standard. Meeting every mitigation on the list doesn’t mean your AI application is fully secure, and no regulator or auditor treats it as a pass/fail requirement. The list also can’t cover every possible failure mode. New attack techniques against LLMs surface often, and a ranked list of ten categories will always simplify a wider, faster-moving threat landscape.
The guidance in each category is general by design, since it has to apply across many types of applications and architectures. That means you still need to translate each risk into specific technical controls for your own environment. The list also focuses on the LLM application layer. It doesn’t replace a full risk assessment that covers your infrastructure, identity controls, or data governance practices. Pairing it with a broader framework, like the NIST AI Risk Management Framework, gives you a more complete picture.
One area worth watching closely is identity and access for AI agents. As agents gain the ability to call tools, query systems, and act without direct human input, they need their own identity, access rules, and audit trail, not a shared login borrowed from a human account. JumpCloud treats AI agents as their own identity type alongside humans and devices, giving you one place to see, control, and manage access across all three as agentic AI takes on more work inside your environment.
Key Terms Appendix
Prompt injection: An attack where hidden or disguised instructions inside user input cause an LLM to ignore its original directions and follow the attacker’s commands instead.
Excessive agency: A risk category describing when an AI agent has more autonomy, permissions, or tool access than its assigned task actually requires.
Hallucination: When an LLM generates output that sounds confident and plausible but is factually incorrect or fabricated.
Data poisoning: An attack where manipulated or malicious data is introduced into a model’s training set or knowledge base to corrupt its future output.
Retrieval-augmented generation (RAG): A technique where an LLM pulls information from an external data source at query time, rather than relying only on its training data, to generate more accurate responses.
