---
title: "OWASP’s Top 10 AI Risks: What Business Leaders Should Know"
description: OWASP’s 2026 LLM Top 10 shows how AI risk is changing. Learn the 10 threats, why agentic AI raises the stakes, and what businesses should do.
image: https://prevent-ransomware.com/hubfs/AI-Generated%20Media/Images/Diverse%20Team%20Discussing%20OWASP%20AI%20Risks%20In%20Modern%20Office.png
---

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# OWASP’s Top 10 AI Risks: What Business Leaders Should Know

![Tony Chiappetta](https://prevent-ransomware.com/hubfs/Tony%20LinkedIn.jpg)

 by [Tony Chiappetta](https://prevent-ransomware.com/blog/author/tony-chiappetta)   
September 28, 2026

What happens when AI stops simply answering questions and starts taking actions inside your business?

That is one of the central security questions raised by the [OWASP Top 10 for LLM and GenAI](https://genai.owasp.org/initiative/owasp-top-10-for-llm-and-genai/). The 2026 list reflects a world where large language models are increasingly connected to corporate data, APIs, applications, credentials, and tools. That creates productivity opportunities, but it also means an AI mistake or manipulation can potentially become a business action.

**Key Takeaway:** OWASP’s ten risks show that securing AI requires more than protecting the model. Organizations also need to control the data, permissions, tools, applications, and endpoint actions available to AI systems. As AI becomes more autonomous, enforced boundaries become increasingly important.

## So what changed in OWASP’s 2026 AI security risks?

The biggest change is the growing importance of agency. According to [CSO’s analysis of the 2026 list](https://www.csoonline.com/article/575497/owasp-lists-10-most-critical-large-language-model-vulnerabilities.html), OWASP incorporated real-world incident data alongside expert voting, while Excessive Agency moved from sixth place to third.

That matters because AI is moving beyond chatbots. Agentic systems can call APIs, use tools, access files, execute workflows, and sometimes make changes to production environments. A bad AI answer is one problem. A bad AI decision with permission to act is potentially much more serious.

## What are the 10 risks business leaders should understand?

These risks are technical, but their consequences are operational. Here is the business-level version of all ten:

**1. Prompt Injection:** An attacker manipulates instructions or content the AI consumes so the system behaves in an unintended way. The business risk can include unauthorized actions, information disclosure, or manipulation of downstream workflows.

**2. Sensitive Information Disclosure:** AI exposes confidential information it should not reveal. That could include customer information, proprietary data, credentials, internal documents, or regulated information.

**3. Excessive Agency:** An AI system has more functionality, permissions, or autonomy than it needs. A hallucination, malicious prompt, compromised component, or simple error can therefore produce a real and potentially destructive action.

**4. Supply Chain Vulnerabilities:** AI environments depend on models, datasets, plugins, libraries, Model Context Protocol servers, and other third parties. A weakness or compromise anywhere in that chain can create risk for the organization consuming it.

**5. Data and Model Poisoning:** Attackers or insiders manipulate training, fine-tuning, embedding, or other data so the AI produces misleading or malicious results.

**6. Unbounded Consumption:** Attackers abuse AI resources at scale, potentially degrading service or creating significant compute and API costs. This can become a denial-of-service or “denial-of-wallet” problem.

**7. Misinformation:** AI generates convincing but inaccurate information. The risk grows when employees or automated systems trust that output enough to make financial, operational, security, or customer-facing decisions without verification.

**8. Hidden Context Exposure:** Organizations place sensitive instructions, business logic, API keys, authentication details, or other information into context they assume users cannot discover. OWASP’s guidance is straightforward: hidden context should not be treated as a secure location for secrets.

**9. Vector and Embedding Weaknesses:** Retrieval-augmented generation and vector databases can introduce their own authorization and data-integrity problems. Poorly separated data can expose information across users or tenants, while poisoned sources can influence AI responses.

**10. Improper Output Handling:** AI output is passed to another application or system without sufficient validation. If that output reaches a shell, application, database, or automated process, unsafe AI-generated content can potentially become executable action.

## Why does Excessive Agency deserve special attention?

Because it changes the potential blast radius. OWASP recommends limiting the tools and functions available to AI, minimizing permissions, avoiding unnecessarily open-ended capabilities, and preserving user-specific authorization when an agent acts on someone’s behalf.

CSO highlighted real 2026 incidents involving AI coding agents and destructive actions, including cases where overly broad credentials and insufficient enforced gates contributed to serious data loss. The lesson is bigger than any single AI product: telling an agent not to perform an action is not the same as technically preventing that action.

That is a familiar cybersecurity principle. Policy is important. Enforcement is stronger.

## What do these ten AI vulnerabilities have in common?

Many have very different root causes, but several become more dangerous when an AI system can translate information into unrestricted action.

A prompt injection, poisoned dataset, hallucination, compromised plugin, or unsafe generated command may begin differently. Yet when the result reaches a Windows endpoint, the system may still need to launch processes, manipulate files, access memory, invoke applications, establish persistence, reach additional resources, or alter data.

**Changing the attack does not necessarily change the endpoint actions the attacker ultimately needs in order to succeed.**

This is an important distinction as organizations prepare for AI-generated, polymorphic, and increasingly autonomous threats. Unknown does not automatically mean unstoppable.

## Can an AI-driven attack be stopped without identifying it first?

In some endpoint scenarios, yes. Detection remains important, but it does not have to be the only control standing between an unknown action and business damage.

Isolation and Containment take a different approach. Instead of relying exclusively on identifying malicious code, they restrict what applications and processes are allowed to do. That can include constraining application behavior and limiting access to memory, files, and system resources.

**What if you did not have to detect the attack in order to stop the damaging endpoint behavior?**

This does not solve every OWASP AI risk. Endpoint containment will not correct misinformation, clean poisoned training data, or repair weak access controls in a vector database. But where an AI-driven event ultimately attempts restricted behavior on a protected Windows endpoint, containment can provide an independent enforcement layer.

[AppGuard](https://www.appguard.us/) is a proven endpoint protection solution with more than a decade of production history focused on prevention through Isolation and Containment. AppGuard does not need to know the name of every attack to restrict endpoint behaviors the attack may require. It complements, rather than replaces, Detect and Respond technologies.

## What Should Businesses Do Next?

Start by assuming AI systems will occasionally be manipulated, make mistakes, or encounter something their designers did not anticipate. Then build controls around that assumption.

- Give AI agents only the tools, credentials, permissions, and data required for the specific task.
- Require human approval or deterministic controls before destructive, high-value, or irreversible actions.
- Keep secrets and security controls outside prompts and hidden model context.
- Validate AI inputs and outputs before passing them to applications, APIs, shells, or automated workflows.
- Apply strong authorization and data separation to RAG, vector, and embedding systems.
- Vet models, plugins, MCP servers, datasets, and other AI supply-chain components.
- Assume some malicious or unsafe behavior will evade detection and add prevention layers that reduce execution freedom and blast radius.
- Consider Isolation and Containment where Windows endpoints are part of an AI-enabled workflow or a likely destination for AI-generated attacks.

## What is the larger cybersecurity lesson?

AI is dramatically increasing the speed and variety with which attacks, instructions, scripts, and workflows can be created. Trying to recognize every malicious variation will remain important, but organizations should not assume recognition is always possible before damage begins.

The objective is not to predict every attack. It is to restrict the actions an attacker needs in order to succeed.

**The attack may be new. The actions it needs to perform on a Windows endpoint often are not.**

For another example of how AI changes attack creation without eliminating the need for familiar endpoint actions, read [AI Built a Worm: What Happens When It Targets Windows?](https://prevent-ransomware.com/blog/ai-built-a-worm-what-happens-when-it-targets-windows)

###### Tags:

[AppGuard,](https://prevent-ransomware.com/blog/tag/appguard) [0-day,](https://prevent-ransomware.com/blog/tag/0-day) [Ransomware,](https://prevent-ransomware.com/blog/tag/ransomware) [AI](https://prevent-ransomware.com/blog/tag/ai)

![Tony Chiappetta](https://prevent-ransomware.com/hubfs/Tony%20LinkedIn.jpg)

Post by [Tony Chiappetta](https://prevent-ransomware.com/blog/author/tony-chiappetta)   
 September 28, 2026

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