What the OWASP LLM Top 10 2026 Means for Your Data

OWASP rewrote the list in August 2026. Here is what changed and what it means for the data behind every model.

OWASP published a fully revised Top 10 for LLM Applications on August 4, 2026, and eight of the ten risks changed position. If your team is still defending against the 2025 order, the priorities in front of you are already out of date. Every entry on this list eventually reduces to the same question: what data can an AI system reach, expose or act on, and who is watching when it does. This post walks through all ten 2026 risks in plain language, then covers the concrete controls available for the three risks where data exposure is the direct, immediate consequence: prompt injection, sensitive information disclosure and data poisoning.

Ten Risks, Reordered by Real Incidents

For the first time, OWASP weighted the 2026 ranking using real-world incident data alongside community judgment, 75 percent vote and 25 percent evidence pulled from thousands of classified incidents. That is why the order below does not match what you may remember from 2025.

Prompt Injection (LLM01:2026)

Prompt injection happens when text hidden in a document, email or web page an AI system reads carries instructions the model was never meant to follow. No human has to type anything in a chat window. A single sentence buried in a file someone uploads for summarization can quietly redirect what the model does next. It is still ranked the top risk in the 2026 list, and Forcepoint has covered why filtering alone fails in more depth.

Sensitive Information Disclosure (LLM02:2026)

Sensitive information disclosure covers everything a model reveals that it should not: personal data memorized during training, confidential business content pulled into a response, or a retrieval system that returns a document nobody meant to share. This risk rarely starts with an attack. It starts with an AI tool doing exactly what it was built to do, using data nobody classified as off-limits first.

Excessive Agency (LLM03:2026)

Excessive agency is what happens when an AI system can act, not just answer, and holds more permission than its task requires. A model that can only read files is a data exposure risk. An agent that can also send, delete or modify data on its own turns that exposure into an action nobody approved. OWASP moved this risk from sixth to third in 2026 because agentic deployments made the theory concrete. A separate OWASP framework covers agent-specific security risks in more depth.

Supply Chain (LLM04:2026)

Supply chain risk covers every third-party component an LLM application depends on: pretrained models, datasets, plugins, fine-tuning frameworks and the libraries that serve them. A compromised model file or a malicious package slipped into a coding assistant's suggestions can introduce a backdoor long before anyone notices unusual output. The data risk is indirect but real: a compromised component can become the quiet channel that exposes or corrupts everything downstream of it.

Data and Model Poisoning (LLM05:2026)

Data and model poisoning happens when an adversary corrupts the material a model learns from, at pretraining, fine-tuning or retrieval, so harmful behavior gets built in rather than triggered later. It can stay dormant until a specific input activates it. Unlike most security bugs, this one cannot be patched. Fixing it means finding what was corrupted, then revalidating or retraining.

Unbounded Consumption (LLM06:2026)

Unbounded consumption covers what happens when nothing limits how much an AI system can be made to compute, cost or process. An attacker who spends almost nothing can trigger a request that fans out into dozens of downstream calls, an outcome security teams are starting to call denial of wallet. Forcepoint X-Labs modeled an unbounded agent racking up cost with no ceiling, call by call.

Misinformation (LLM07:2026)

Misinformation is what happens when a model produces output that is wrong but confident enough to be trusted and acted on. A hallucinated answer a human double-checks is an annoyance. The same answer feeding an automated workflow, with no person reviewing it, is a fault with no safety net. OWASP moved this risk up two places in 2026 partly on real incident data, not just practitioner opinion.

Hidden Context Exposure (LLM08:2026)

Hidden context exposure, renamed this year from system prompt leakage, covers everything assembled into a model's context that users are not meant to see: system instructions, retrieved policy text, tool definitions and internal rules. Once exposed, that content tells an attacker exactly how a system is built to be manipulated. OWASP's own guidance is to assume hidden context will eventually be discoverable, so what it reveals matters more than whether it leaks.

Vector and Embedding Weaknesses (LLM09:2026)

Vector and embedding weaknesses live wherever similarity search sits between a data source and a prompt: retrieval-augmented generation, agent memory and semantic caches. Because these attacks exploit the geometry of the vector space rather than the model's instructions, a multi-tenant deployment can leak one customer's documents to another through query patterns alone, without ever returning the text itself.

Improper Output Handling (LLM10:2026)

Improper output handling covers what happens when a model's response reaches another system without validation: rendered as HTML, executed as code or passed into a database query. This risk fell from fifth to tenth in the 2026 list, not because it got smaller, but because the fix is well understood: encode and validate output the same way any application would.

The 2026 List at a Glance

Where Data Security Closes the Gap

Three of the ten risks above are not just security problems. They are data exposure problems with a direct data control that reduces them.

Prompt Injection

Forcepoint stops the data behind a prompt injection attempt from leaving, not the injection itself. Prompt and response inspection classifies content against existing policy before anything moves, so a manipulated interaction has nothing valuable left to take.

Sensitive Information Disclosure

Forcepoint stops sensitive content from leaving through an AI interaction the same way it already stops it leaving through email or endpoint. Nearly 2,000 existing DLP classifiers extend to AI prompts, responses and uploads automatically, no new policy required.

Data and Model Poisoning

Forcepoint stops an over-permissioned training corpus from becoming the easiest way into a model. Data access governance closes off who can reach or modify the stores feeding a training or fine-tuning pipeline, the same discipline already applied to any other sensitive repository.

None of this is really ten separate problems. It is one problem wearing ten names: an AI system did something with data nobody watched closely enough. Forcepoint AI Data Security knows where sensitive data lives, adapts policy the moment risk changes and stops exposure at the point of AI interaction, across sanctioned tools, shadow AI and autonomous agents alike. The list will change again next year. The data will not stop moving.