The 2026 OWASP LLM Top 10 doesn't introduce ten brand new threats. Most of the risks you knew from 2025 are still here, but the order has changed, and the reasons behind that reshuffle tell you where AI security is heading.

Large language models no longer just answer questions. They call tools, send emails, query databases, and take action on behalf of the people who deploy them. The more a model can do, the more it matters what stands between the model and the consequences.

This post walks through all ten risks, explains what OWASP actually says about each one, gives a real attack scenario for each, and shows where a security layer like SPRO fits in.

Why the Order Changed

Earlier editions of the list were built on expert opinion. This year, OWASP tested that opinion against evidence. The team gathered 7,714 real incidents from public vulnerability databases and an AI harm database, then classified the 6,639 that carried enough detail to sort.

The final ranking weighs the community vote at 75% and the incident data at 25%. The vote and the data disagreed in a few useful places:

• Prompt Injection is ranked #1 by practitioners, yet it falls out of the top 10 in raw incident counts. OWASP's explanation is that teams defend against it so hard that few clean exploits reach public databases. The attack surface still exists everywhere a model reads untrusted input.
• Misinformation is the reverse. Voters placed it low, but the incident record placed it near the top. It landed in the middle at #7.
• Excessive Agency is where belief and evidence agree: agentic deployments are where the damage is landing.

OWASP's guiding message is worth remembering: stop trying to build a model that cannot be fooled. Build the system around it, so that when the model is fooled, nothing important breaks.

The 2026 List at a Glance

The Three Big Moves

Excessive Agency, #6 to #3. The most consequential move on the list. Models now hold tools, memory, and permissions, so one manipulated response can trigger a real action. Both the community vote and the incident record agree this is where the damage is landing.

Unbounded Consumption, #10 to #6. Practitioners now weigh resource and cost exhaustion much higher. AI is priced per token, so an attack can drain your budget just as easily as it can crash your service.

Improper Output Handling, #5 to #10. The biggest fall. It is still a real risk, but it overlaps with classic web security practice (validate and encode outputs), which many teams already follow.

Several entries also grew in scope. Prompt Injection now covers cross modal attacks that hide instructions in images or audio. Supply Chain covers promoted model artifacts that are not what they claim to be. Poisoning now includes fine tuning subversion. Improper Output Handling now spans the insecure code that AI assistants generate at scale.

The Ten Risks Explained

For each risk below you'll find OWASP's Definition, what it means in plain language, and an example attack scenario.

1. Prompt Injection

OWASP's Definition: A prompt injection vulnerability occurs when input to an LLM, whether direct user input, retrieved content, tool output, an image, audio, or even persistent memory, alters the model's behavior in ways the developer did not intend. LLMs make no architectural distinction between instructions and data, since both are tokens in the same stream.

What it means: The model can't reliably tell "what the developer told me to do" from "text I happened to read." So anyone who can get text in front of the model can try to give it orders. The text doesn't even need to be visible to a human.

Attack scenario: A user asks an assistant to summarize a web page. The page hides instructions that make the model insert a markdown image whose URL carries the private conversation to an attacker's server. The user only sees an image and never sees the instruction.

2. Sensitive Information Disclosure

OWASP's Definition: This happens when an LLM integrated system exposes confidential, regulated, privileged, or proprietary data through a channel the owner did not authorize. The channel is not only the final answer. Tool call arguments, reasoning traces, retrieved chunks, logs, and embeddings are all disclosure surfaces.

What it means: Anything the model can see, it can potentially repeat. That includes personal data, credentials, trade secrets, and other users' information, and it can leak through places teams rarely think to check.

Attack scenario: Prompt injection makes a support bot print its system prompt along with an embedded vendor API key.

3. Excessive Agency

OWASP's Definition: Excessive Agency is the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous, or manipulated model output, regardless of what caused the malfunction. The root cause is typically excessive functionality, excessive permissions, or excessive autonomy.

What it means: The model was given too much power. Too many tools, too broad access, or too little human approval. Whether the cause is a hallucination or an attacker, the result is the same: the system acts.

Attack scenario: An email assistant only needs to read mail, but the tool it uses can also send. A malicious incoming email tells it to scan the inbox and forward sensitive messages to the attacker. The fix is a read only tool, read only permissions, and a human clicking "send."

4. Supply Chain

OWASP's Definition: LLM supply chains are susceptible to vulnerabilities that affect the integrity of training data, models, adapters, conversion pipelines, and deployment platforms. The risk extends to third party pre trained models, datasets, and model artifacts, which can be manipulated through tampering, poisoning, or malicious replacement.

What it means: You didn't build everything you're running. Every model, adapter, package, and serving framework you pull in is something you're trusting without having checked it.

Attack scenario: An attacker publishes a tampered model under a trusted looking name. In the PoisonGPT proof of concept, a model with surgically modified parameters spread misinformation while still passing standard benchmark evaluation.

5. Data and Model Poisoning

OWASP's Definition: Poisoning is a class of attacks where an adversary manipulates data or model artifacts to embed harmful behavior, bias, or exploitable weaknesses. It can occur anywhere data is ingested, transformed, retrieved, or reused: pre training, fine tuning, embeddings, RAG, and model distribution. The system may still appear to work normally.

What it means: Corrupt what the model learns from or retrieves, and you corrupt how it behaves. Unlike a code bug, poisoning can't be patched. Fixing it can mean revalidating data, retraining, or replacing the model.

Attack scenario: A malicious insider injects mislabeled transaction data into a training dataset. The fraud detection model stops catching fraud, causing financial losses and compliance violations.

6. Unbounded Consumption

OWASP's Definition: This occurs when an LLM application allows excessive and uncontrolled inferences, enabling attackers to disrupt service availability, inflict unsustainable financial costs, or steal intellectual property through model cloning. A defining trait is cost asymmetry: attackers trigger expensive computation at negligible cost to themselves.

What it means: If nothing limits how much your AI can be used, someone can run up your bill, slow your service to a crawl, or copy your model through your own API. Agents make this worse, since one request can cascade into many downstream operations.

Attack scenario: Denial of Wallet. An attacker generates a flood of operations against a pay per use AI service until the costs become unsustainable for the provider.

7. Misinformation

OWASP's Definition: Misinformation occurs when an LLM produces incorrect, incomplete, or misleading information that appears credible enough to influence a human decision, an automated workflow, or an agent action. The core risk is that the incorrect output is trusted and acted upon.

What it means: The model sounds confident whether it is right or wrong. When that output feeds a decision or a tool call, a wrong answer becomes a wrong action. This is the entry where the incident data pushed hardest against the community vote.

Attack scenario: A customer service agent misreads a refund policy and approves a refund that violates the terms, resulting in financial loss. A related variant: a coding assistant recommends a plausible package that doesn't exist, and an attacker has already registered that name with malicious code.

8. Hidden Context Exposure

Formerly System Prompt Leakage.

OWASP's Definition: Hidden Context Exposure is the unauthorized extraction, inference, or reconstruction of hidden, non user facing instructions or operational context in a model's context window. That includes system prompts, developer instructions, retrieved policy text, and the schemas of the tools the model can call. OWASP advises designing under the assumption that hidden context is discoverable.

What it means: Your system prompt is not a safe. Whatever you put in the model's context can eventually be coaxed out, so don't put secrets there, and don't rely on the prompt's secrecy as your only security control.

Attack scenario: An attacker extracts the tool list and parameter schemas through conversational probing. No credential is leaked and no rule is overtly broken, but the attacker now has concrete targets for follow up prompt injection.

9. Vector and Embedding Weaknesses

OWASP's Definition: These are security risks in any application that converts text, images, code, or audio into numerical representations and uses similarity search to decide what the model sees. RAG is the most familiar case. OWASP offers a useful frame: poisoning makes the system wrong, inversion makes it leak, jamming makes it silent, and access control failure makes it indiscriminate.

What it means: The vector database behind your RAG system is part of your trust boundary. Attackers can exploit how similarity search works, even when no malicious instruction is in the retrieved content.

Attack scenario: A multi tenant app uses one shared vector index and filters by tenant only at the application layer. Tenant A sends probing queries, and timing differences, result counts, and score patterns reveal what kind of content Tenant B has stored, without A ever seeing a document.

10. Improper Output Handling

OWASP's Definition: This refers to insufficient validation, sanitization, and handling of LLM outputs before they are passed to other components. Since model output can be steered by prompt input, this is similar to giving users indirect access to extra functionality. Exploitation can result in XSS, CSRF, SSRF, privilege escalation, or remote code execution.

What it means: Treat model output like untrusted user input. If you pass it straight into a shell, a database, or a browser, you've handed the attacker a way to reach those systems through the model.

Attack scenario: A chat feature lets users craft SQL queries against a backend database. A user asks for a query that deletes all tables, and because the generated query is never scrutinized, every table is deleted.

The Pattern Behind All Ten

Look at the scenarios again. Nearly all of them end the same way: the model did something, and nothing stood between the model and the consequence.

An injected instruction reached a tool. A secret sat where the model could repeat it. A flood of requests hit a billing meter. Unvalidated output hit a database. You can't fully fix this inside the model, so you fix it in the layer around it.

That is also OWASP's boundary line for this list: it owns the risk when the model is a component inside your application. The moment the model becomes an actor, with tools, memory, and consequences of its own, you should pair it with the OWASP Top 10 for Agentic Applications.

Where SPRO Fits: A Security Layer Around the Model

SPRO, a product of Hrida AI, is built as a unified AI gateway and security platform, not just a routing layer. Instead of letting prompts and responses travel blindly between your applications and your LLM providers, SPRO puts a controlled, inspected checkpoint in the middle, with AI guardrails enabled by default rather than bolted on later.

Here is how that maps onto the OWASP list:

Real Time Prompt and Response Inspection

Every prompt and every returned tool call passes through SPRO's inspection layer instead of being trusted by default. That directly supports defenses against prompt injection, excessive agency, and improper output handling.

Credential and Sensitive Data Masking

SPRO masks sensitive data such as API keys, cloud credentials, source code, and confidential business information before it leaves the organization. Data that never crosses the wire in its original form can't leak through the model.

Observability and Policy Enforcement

Centralized visibility into what actually flows through your AI traffic means you can govern it as it happens, rather than discovering a problem after the bill arrives or the data is already out.

Model Agnostic Protection

Because SPRO sits as a dedicated gateway in front of any LLM provider, you get consistent protection regardless of which model is behind it. It is built as an alternative to routers like OpenRouter, where security is part of the design instead of an afterthought.

A gateway is a layer, not a silver bullet. Supply chain integrity, clean training data, and secure vector store design still need their own work. But one controlled place to inspect, mask, and enforce policy closes a large share of the gaps the Top 10 describes.

Recognized by OWASP

Hrida AI's work in AI and LLM security has been recognized in the 2026 OWASP GenAI security landscape publications, aligned with emerging OWASP GenAI security standards, with Hrida AI listed as a recognized partner. Take a look at our LinkedIn post for the announcement.

Key Takeaways

• The 2026 list keeps the same core threats but reorders them using both a community vote (75%) and real incident data (25%).
• Excessive Agency jumped from #6 to #3 because agentic deployments are where real damage is landing.
• Unbounded Consumption rose four places as cost and resource exhaustion became a bigger concern.
• Prompt Injection and Sensitive Information Disclosure hold the top two spots.
• System Prompt Leakage became Hidden Context Exposure, a broader view of the same failure: assuming hidden information stays hidden.
• The common thread across all ten: don't try to build a model that can't be fooled. Build the system around it so that being fooled isn't catastrophic.

Your 10 Minute Checklist

1. Assume the model will be fooled. Design so a successful injection has limited impact.
2. Audit every tool your LLM can call. Remove what it doesn't need and require approval for risky actions.
3. Keep secrets out of prompts. If it's in the context window, assume it can leak.
4. Set limits on input size, request volume, and spend.
5. Treat model output as untrusted before any downstream system acts on it.
6. Verify every model, adapter, and package you pull in.
7. Don't treat fluent output as correct output for decisions that matter.
8. Put an inspection layer between your apps and your models.

The Bigger Lesson

The 2026 OWASP LLM Top 10 isn't saying AI is unsafe. It's saying that as models move from answering questions to taking actions, the system around the model matters just as much as the model itself.

The question every team should keep asking is simple:

What happens to a prompt, a tool call, or a credential between the moment it is generated and the moment it is acted on?

Because in the age of agentic AI, securing the layer around the model is just as important as securing the model.

🚀 Explore SPRO

Ready to secure your AI infrastructure end to end?

Learn how SPRO protects AI traffic across any LLM provider with real time inspection, intelligent masking, policy enforcement, and enterprise grade security.

🌐 Learn More

• 🛡️ SPRO Product: https://spro.hridaai.com/
• 📚 Technical Blogs and Research: https://www.hridaai.com/blogs/spro
• 🏅 OWASP Recognition (LinkedIn): https://lnkd.in/p/geXkz_8u

Protect your prompts. Protect your models. Protect your enterprise.

Source: OWASP GenAI Security Project, Top 10 for LLM Applications 2026 (v1.0).