OWASP LLM Top 10: What Comes Next for AI Security

When the OWASP Top 10 for LLM Applications arrived, it did the industry a real service. It gave security teams a stable, vendor-neutral vocabulary for a threat surface that was moving too fast to describe. Prompt injection, sensitive information disclosure, system prompt leakage, improper output handling, unbounded consumption: five of those ten account for the bulk of what actually shows up in red-team exercises and public incidents.

Every framework earns its keep by naming what defenders are already fighting. Which is exactly why the next part matters: the next lists are not coming. They have already arrived. Most security programs simply have not caught up with them yet.

The three OWASP lists that now define AI security

Three published OWASP lists now cover the AI stack end to end. Together they are the checklist most security programs are not yet resourced against:

OWASP Top 10 for LLM Applications (2025) – the conversation layer. Prompt injection, sensitive information disclosure, system prompt leakage, improper output handling and unbounded consumption are the five that dominate real incidents.

OWASP Top 10 for Agentic Applications (2026) – the systems that act. Released 9 December 2025 by the OWASP GenAI Security Project. Ten risks, prefixed ASI, covering what happens when AI stops answering and starts doing.

OWASP MCP Top 10 (MCP01:2025-MCP10:2025) – the connective tissue. Currently in beta (Phase 3, beta release and pilot testing), covering the Model Context Protocol layer that wires assistants to real systems and data.

The OWASP agentic and MCP Top 10 lists are published. The budgets are not.

In December 2025, the OWASP GenAI Security Project published the OWASP Top 10 for Agentic Applications (2026), covering the risks that appear when AI stops answering questions and starts taking actions: agent goal hijack (ASI01), tool misuse and exploitation (ASI02), identity and privilege abuse (ASI03), memory and context poisoning (ASI06), insecure inter-agent communication (ASI07) and rogue agents (ASI10).

Alongside it, OWASP’s MCP Top 10 project is codifying the risks of the Model Context Protocol, the standard that connects AI assistants to real systems and data. It is a young list, currently in beta – Phase 3, beta release and pilot testing, with entries carrying the MCP01:2025 through MCP10:2025 designation – and it is already one of the most useful documents in AI security, because it names the attack patterns defenders are meeting right now. Three of them deserve particular attention.

Command injection and execution (MCP05). An AI agent executes system commands built from untrusted input, without proper validation. An adversary who compromises the toolchain gets their work carried forward by the agent itself, with the agent’s legitimate permissions. The prompt was never touched. The model behaved exactly as designed. The compromise came in through the tools.

Tool poisoning (MCP03). The model is handed a list of tools it may call, and it trusts their definitions and their outputs. Poison either one and you have redirected the application’s behavior without a single suspicious token appearing in the conversation.

Insufficient authentication and authorization (MCP07). MCP servers, tools, and agents interacting without properly verifying identity or enforcing access controls. Who is allowed to call this tool? Which agent may delegate to which? In most environments today the answer is: whoever asks. That is not a vulnerability in a model. It is a missing identity layer for machines.

One more entry is worth a mention because it will sound familiar to anyone tracking shadow IT: shadow MCP servers (MCP09), the unapproved deployments running outside security governance, often on default credentials. The shadow AI problem now has an OWASP number. For the practical exposures behind these entries, see MCP Server Security: The Blind Spot in Your AI Stack.

None of these are theoretical. All of them get easier as organizations wire agents into more systems. And none of them have the household-name status that prompt injection now enjoys, which means the frameworks are ahead of most security programs’ resourcing. That is a new and uncomfortable position: the industry usually waits years for its threat lists. This time the lists arrived before the budgets.

History does not repeat, but it files tickets

We have watched this movie before. Monolithic applications got the OWASP Top 10. API-based applications got the API Security Top 10, because the attacks changed shape when the architecture did. AI applications and agents are the next architecture, and this time the pattern completed itself faster than ever: the LLM Top 10 for the conversation layer, the Agentic Top 10 for the systems that act, and the MCP Top 10 for the connective tissue between them.

So the catch-up question for security teams is no longer “what threats might emerge?” It is “which published OWASP entries is my program actually resourced against?” And the question for vendors sharpens the same way. Not “do you block prompt injection?” but “walk me through the MCP Top 10 and tell me what you see, and what you can control, beyond the prompt.”

The LLM Top 10 taught the industry to take AI threats seriously. The warm-up worked. The event has already started.

Frequently asked questions about the OWASP AI Top 10 lists

What is the OWASP LLM Top 10? The OWASP Top 10 for Large Language Model Applications is a vendor-neutral list of the ten most critical security risks in LLM-based applications, maintained by the OWASP GenAI Security Project. Prompt injection, sensitive information disclosure, system prompt leakage, improper output handling and unbounded consumption account for most of what shows up in red-team exercises and public incidents.

Is there an OWASP Top 10 for AI agents? Yes. The OWASP Top 10 for Agentic Applications (2026) was released on 9 December 2025. Its ten entries carry the ASI prefix and run from agent goal hijack (ASI01) to rogue agents (ASI10).

What is the OWASP MCP Top 10? A list codifying the security risks of the Model Context Protocol, the standard connecting AI assistants to real systems and data. Entries run MCP01:2025 to MCP10:2025 and include tool poisoning (MCP03), command injection and execution (MCP05), insufficient authentication and authorization (MCP07) and shadow MCP servers (MCP09). The project is in beta.

How many of the OWASP LLM Top 10 does Imperva address? Five out of the box: prompt injection and jailbreaking, sensitive information disclosure, system prompt leakage, improper output handling and unbounded consumption.

What should I ask an AI security vendor? Not “do you block prompt injection?” but “walk me through the MCP Top 10 and tell me what you see, and what you can control, beyond the prompt.”

Thales’s Imperva AI Application Security addresses five of the OWASP LLM Top 10 out of the box. We believe the industry’s next frameworks will be written about agents and toolchains. Thales announced the AI Security Fabric in December 2025, with an MCP security gateway and runtime access control for agentic AI interactions on the 2026 roadmap. Our forthcoming white paper, Beyond the LLM Top 10, maps that terrain.