The OWASP Top 10 for LLMs: What It Means and How to Build More Secure AI

Large Language Models (LLMs) have quickly gone from novelty to necessity. In under two years, they’ve become embedded into customer service bots, sales enablement tools, internal copilots, and security automation workflows. As enterprises look to scale their use of agentic AI, the temptation to move fast is hard to resist. But as with any transformational technology, speed often comes at the expense of security.

To help organizations adopt LLMs more responsibly, OWASP has released its Top 10 security risks for LLM applications. This list outlines the most pressing threats to LLM-based systems and highlights the gaps that teams often overlook in the rush to production. Below, we break down what each of these risks actually means, why they matter, and how you can reduce your exposure—especially by using tools like a hosted Model Context Protocol (MCP) platform to enforce policy and control.

A Closer Look at the OWASP Top 10 LLM Risks

1. Prompt injection takes the top spot—and for good reason. These attacks involve feeding malicious inputs into the LLM in order to hijack its behavior. Whether inserted directly by the user or hidden in external content, a well-crafted prompt can override instructions, extract confidential data, or trick the model into taking unintended actions.

2. Closely related is the risk of sensitive information disclosure, where a model accidentally reveals data it shouldn't—either from training data, context windows, or backend systems it has access to. When AI agents are connected to business systems via APIs or embedded in tools like CRMs or ticketing systems, the risk of overexposure increases rapidly.

3. Then there are supply chain vulnerabilities, which highlight just how reliant LLM applications are on third-party dependencies—pretrained models, plugins, vector databases, or API services. If one of those components is compromised, your entire application can be at risk.

4. Another major concern is data and model poisoning—when attackers inject tainted information during fine-tuning or context setup. This can bias outputs, introduce backdoors, or subtly alter the model’s behavior in ways that are hard to detect.

5. The OWASP list also flags improper output handling, where a system blindly trusts the LLM’s response. If a chatbot’s answer is fed into another system—say, a code executor or email sender—without review or sanitization, it opens the door to everything from injection attacks to automated spam.

6. As LLMs get integrated into more workflows, excessive agency becomes a serious risk. Giving an AI agent the ability to take actions—like resetting passwords, sending messages, or modifying configs—without proper checks can lead to unintended or dangerous behavior.

7. Another subtle yet serious issue is system prompt leakage, where the model inadvertently reveals the hidden instructions guiding its behavior. Once exposed, these system prompts give attackers a blueprint to manipulate the model.

8. Vector-based systems aren’t immune either. Embedding weaknesses occur when adversarial content is crafted to appear semantically similar to benign content, tricking retrieval-augmented generation (RAG) systems into surfacing irrelevant or harmful information.

9. And of course, LLMs are notorious for confidently hallucinating false facts. The risk of misinformation—particularly in regulated environments like healthcare or finance—can lead to real-world harm or compliance failures.

10. Finally, unbounded resource consumption reminds us that LLMs don’t come cheap. Poorly scoped prompts, recursive loops, or API misuse can result in runaway costs or denial-of-service conditions.

Why These Risks Are Often Overlooked

The reason these risks feel so ubiquitous is because LLM adoption often happens from the bottom-up. A developer plugs an API into a Slack bot. A team builds a quick internal co-pilot. Before long, those tools are in production—with no threat model, no permissions framework, and no security team involvement.

This mirrors what we’ve seen with past technology waves—from shadow IT in the cloud era to the rise of SaaS tools. The difference is, LLMs don’t just process data—they act. That means their blast radius is bigger. And that makes securing them not just a nice-to-have, but a foundational requirement.

How a Hosted MCP Platform Like Natoma Helps

This is where a hosted Model Context Protocol (MCP) platform like Natoma comes in. MCP is a framework that enables LLMs to interact with enterprise systems in a secure, governed, and compliant way. Rather than exposing raw APIs and unfiltered data, MCP defines what an agent can access, on whose behalf, and under what conditions.

A platform like Natoma implements MCP in a way that’s easy to deploy and manage. Here’s how it helps mitigate the OWASP Top 10:

• Context Isolation and Injection Protection: Natoma separates system prompts from user input and enforces structured context delivery—helping prevent prompt injection and prompt leakage.

• Access Control and Policy Enforcement: Through scoped, auditable access to tools and data, Natoma ensures that LLMs only do what they’re allowed to—minimizing excessive agency and reducing the risk of sensitive data leakage.

• Data Hygiene and Governance: By controlling what data enters the model and where it comes from, Natoma helps prevent poisoning and ensures that outputs are grounded in reliable, sanctioned sources—critical for combating misinformation.

• Audit Logs and Observability: Every action the agent takes via MCP is logged and attributable. This helps detect misuse, monitor performance, and ensure accountability—especially when LLMs are allowed to act on behalf of users.

• Built-in Quotas and Safeguards: Natoma helps avoid unbounded consumption by setting rate limits, token caps, and budget alerts, ensuring your AI deployments remain cost-effective and under control.

In short, Natoma gives security and IT teams a safety net—without getting in the way of innovation. As a developer, you still get to move fast and plug into enterprise tools, but you can do so inside a governed framework that enforces the right boundaries.

Final Thoughts

As generative AI moves from experimental to essential, the pressure to deploy is only growing. But as the OWASP Top 10 shows, LLMs come with a whole new class of risks that aren’t covered by traditional application security practices. Ignoring these risks won’t make them go away. It just makes your organization more vulnerable to them.

If you're building agentic AI systems, you need more than a good prompt. You need a strong access layer, observability, and policy enforcement baked into every interaction between the model and your systems. A hosted MCP platform like Natoma can help you do exactly that—so your LLMs are secure, scalable, and enterprise-ready from day one.