OWASP Top 10 for LLM Applications 2026 - What Changed?
What is the OWASP LLM Top 10?
The OWASP Top 10 for LLM Applications is a security awareness and risk framework for applications that use large language models. The 2026 edition continues that work but significantly changes the ranking and scope of several categories.
One important change is how OWASP approached the ranking. The 2026 project compared practitioner opinion with evidence from 7,714 real incidents, with 6,639 incidents containing enough information to classify. Practitioner voting still carried most of the weight, while incident data contributed the remaining quarter.
OWASP found that what security practitioners fear and what appears in public incident data do not always match. Prompt Injection remained #1 despite not appearing in the incident-data top 10, while Misinformation moved upward because the evidence showed it occurring more frequently than practitioners expected.
Source: OWASP Top 10 for LLM Applications 2026.
2026 Top 10 at a Glance
| Rank | 2026 Risk | Description |
|---|---|---|
| 01 | Prompt Injection | Manipulating model behavior through direct, indirect or multimodal input |
| 02 | Sensitive Information Disclosure | Exposure of sensitive data through outputs, traces, embeddings, logs and side channels |
| 03 | Excessive Agency | Too much functionality, permission or autonomy |
| 04 | Supply Chain | Models, datasets, adapters, dependencies and model artifacts |
| 05 | Data and Model Poisoning | Manipulating training, fine-tuning or application data |
| 06 | Unbounded Consumption | Resource exhaustion, excessive cost and model extraction |
| 07 | Misinformation | False or misleading model output driving bad decisions |
| 08 | Hidden Context Exposure | Exposure of system prompts, rules, tool schemas and hidden context |
| 09 | Vector and Embedding Weaknesses | Weaknesses in embeddings, vector stores and retrieval |
| 10 | Improper Output Handling | Unsafe model output reaching downstream systems |
So, What Actually Changed?
1. Excessive Agency jumps to #3
This is arguably the biggest change in the 2026 ranking. Excessive Agency moved up to #3, reflecting the increasing importance of LLM applications that can actually perform actions.
The problem is not simply that an LLM generates a bad answer. It is what happens when that answer can trigger a tool, modify data, send an email, execute a command or interact with another system.
OWASP breaks the root causes into three areas: excessive functionality, excessive permissions and excessive autonomy.
2. Unbounded Consumption moves up four places
Resource exhaustion becomes a much more visible AI security problem in 2026. Unbounded Consumption moved four positions upward.
The risk goes beyond a traditional denial-of-service attack. LLM applications can consume tokens, GPU resources, API quota and money. Agentic workflows can amplify a single request into many downstream operations.
Recursive tool calls, excessive inference requests and model extraction attempts can therefore affect both availability and cost.
3. System Prompt Leakage becomes Hidden Context Exposure
One of the clearest naming changes is the move from the narrower idea of System Prompt Leakage to the broader Hidden Context Exposure.
The 2026 category includes more than just system prompts. Hidden context can include:
- System and developer instructions
- Tool and function schemas
- Permission and role information
- Internal behavioral rules
- RAG-provided policy information
- Output formatting and validation rules
The important lesson is that hidden context should not be treated as a security boundary. OWASP recommends keeping credentials, tokens and other sensitive data out of hidden context.
This exposure can also make other attacks easier by revealing authorization logic, tool capabilities and prompt-injection targets.
4. Misinformation moves higher
Misinformation is another interesting change because the ranking was influenced by incident evidence. Practitioners originally ranked it relatively low, but the incident record placed it much higher.
The concern becomes especially serious when incorrect model output is trusted by people or downstream systems. A false answer can become a false decision, alert, approval or automated action.
5. Improper Output Handling falls to #10
This is the biggest downward movement in the ranking. Improper Output Handling dropped from #5 to #10.
That does not mean it became unimportant.
The risk is still serious when LLM output reaches sensitive sinks such as:
LLM -> Shell / exec
LLM -> File path
LLM -> Browser
LLM -> Privileged extension
Unvalidated output can result in SQL injection, path traversal, remote code execution or misuse of privileged extensions.
The Biggest Conceptual Change: LLMs Are Becoming Actors
Perhaps the most important takeaway from the 2026 edition is the increasing importance of agentic systems.
A traditional LLM application might look like:
A modern agentic application can look more like:
That difference dramatically changes the potential blast radius.
OWASP draws an important boundary: when the model becomes an actor with tools, persistent memory and the ability to initiate downstream actions, organizations should also consider the OWASP Top 10 for Agentic Applications.
What Should Security Teams Do Differently?
The 2026 list points toward a more architectural approach to AI security.
Give AI tools only the permissions they actually need.
Never blindly trust model output before sending it to downstream systems.
Track models, datasets, adapters and dependencies with provenance and integrity controls.
Apply authorization before retrieval and protect vector stores.
Use rate limits, budgets and circuit breakers for expensive AI workflows.
Require confirmation for privileged, irreversible or externally visible actions.
Final Takeaway
The OWASP Top 10 for LLM Applications 2026 is not simply a reordered version of the previous list. It reflects how quickly LLM applications are evolving.
The security problem is no longer just: "Can someone jailbreak the chatbot?"
It is increasingly: "What can the AI access, what can it change, what data can it retrieve, what other systems can it influence, and what happens when the model gets something wrong?"
The 2026 mindset
Don't design an AI system assuming the model will always behave correctly. Design the surrounding architecture so that when the model is manipulated or makes a mistake, nothing important breaks.
Source: OWASP Top 10 for LLM Applications 2026, Version 2026. The 2026 document states that the list combines practitioner judgment with analysis of real incident data.