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What Is the OWASP LLM Top 10 and How Does It Apply to AI Agents?

The OWASP LLM Top 10 is a community-maintained list of the ten most critical security risks specific to applications built on Large Language Models. Think of it as the AI-era equivalent of the classic OWASP Web Application Top 10 — a shared vocabulary and prioritised checklist that helps security teams, developers, and product owners understand where LLM-powered systems fail and get exploited. When your product includes AI agents — systems that don't just generate text but also call tools, browse the web, execute code, or interact with APIs — every one of these risks becomes sharper and the blast radius of a mistake grows significantly.

The OWASP LLM Top 10 — A Plain-English Breakdown

Here are the ten categories and what they mean in practice:

• LLM01 — Prompt Injection: An attacker manipulates the model's input — directly or through content the model reads — to override its instructions. For an agent that browses the web or reads files, a malicious document can secretly redirect what the agent does next.
• LLM02 — Insecure Output Handling: The model's output is passed downstream without sanitisation. If an agent writes code that gets executed, or inserts text into a database query, the output becomes an injection vector.
• LLM03 — Training Data Poisoning: Adversarially crafted data introduced during fine-tuning or RAG pipelines corrupts model behaviour, causing it to produce biased, harmful, or attacker-controlled responses.
• LLM04 — Model Denial of Service: Crafting inputs that consume excessive compute or memory, degrading availability. Relevant when agents are exposed as public-facing services.
• LLM05 — Supply Chain Vulnerabilities: Third-party model weights, plugins, or tool integrations that carry hidden risks — malicious packages, compromised model providers, or unsafe MCP servers.
• LLM06 — Sensitive Information Disclosure: The model inadvertently reveals confidential data from its context window, system prompt, or training data. Agents that ingest private documents are especially exposed.
• LLM07 — Insecure Plugin Design: Tools and plugins wired to an agent accept insufficiently validated inputs or grant excessive permissions, letting an attacker pivot from the LLM into backend systems.
• LLM08 — Excessive Agency: The agent is granted more permissions, tool access, or autonomy than it needs. A compromised or confused agent can then take destructive real-world actions.
• LLM09 — Overreliance: Humans or downstream systems trust model outputs without verification, allowing errors or manipulated outputs to cause harm.
• LLM10 — Model Theft: Extracting proprietary model weights or reconstructing a model through systematic querying, undermining competitive advantage and enabling further attacks.

Why AI Agents Make Every Risk Worse

A standalone chatbot that only generates text is relatively contained. An AI agent is different — it has tools. It can send emails, call APIs, query databases, write and run code, and interact with external services. This tool-use changes the threat model in three important ways:

• Actions have real-world consequences. Prompt injection that hijacks an agent doesn't just produce bad text — it can exfiltrate data, delete records, or pivot laterally through your infrastructure.
• The attack surface is dynamic. Every MCP server, every tool registration, every external data source the agent touches is a new entry point. Most teams don't have visibility into this surface.
• Trust boundaries collapse. Agents often operate with the permissions of the user or service account that invoked them. Excessive agency (LLM08) means a single successful prompt injection can act with those full permissions.

OWASP's Agentic and MCP Extensions

OWASP has extended its guidance to cover agentic systems specifically — including an OWASP Agentic Top 10 and an OWASP MCP Top 10 that address risks unique to multi-agent architectures and the Model Context Protocol (MCP). These cover issues like agent-to-agent trust confusion, tool poisoning via malicious MCP servers, and rug-pull attacks where a server changes its tool definitions after approval. If you are shipping agents that use MCP — for example, agents running inside Claude, Cursor, or similar clients — these extended lists are essential reading alongside the core LLM Top 10.

What Good Remediation Looks Like

Addressing OWASP LLM risks in practice means:

• Enumerate your agent's tools and MCP servers — you cannot secure what you haven't mapped.
• Apply least-privilege to every tool. An agent that sends emails should not also have permission to delete files.
• Sanitise and validate all inputs and outputs that cross trust boundaries, especially content retrieved from the web or user-supplied documents.
• Audit your supply chain — scrutinise third-party plugins, MCP servers, and model providers the same way you would any third-party code dependency.
• Monitor agent behaviour continuously, not just at release time. Agent attack surface drifts as tools are added, permissions change, and new integrations land.
• Test adversarially. Static code review alone won't catch prompt injection paths — you need active red-teaming against the running agent.

How Pinaka Helps You Secure the AI Agents You Ship

Pinaka is an External Attack Surface Management platform that extends its continuous reconnaissance to the AI agents and MCP servers you build and ship. Its Agent Surface feature scans your repository locally — your source code never leaves your machine — and maps every MCP server and agent tool your code registers, flagging the risks it finds against the OWASP MCP Top 10, LLM Top 10, and Agentic Top 10.

Pinaka's broader platform also continuously monitors the external attack surface that surrounds your AI infrastructure — subdomains, open ports, cloud assets, exposed secrets, and CVEs — using 60+ automated scanners, 7,000+ Nuclei templates, and CVE intelligence with EPSS scoring and CISA KEV tracking. Monitoring runs every 6 hours so you see drift before an attacker does.

What makes Pinaka's approach trustworthy for security-sensitive work is its commitment to deterministic evidence: findings and risk scores are computed and reproducible, never hallucinated. Every hunt records what was tested, what was found, and what was ruled out — so you verify the work rather than trust it on faith. Pinaka integrates directly into Claude, Cursor, or any MCP client, so there's no context switching away from your existing workflow.

You can run a free security check on your domain — under a minute, no signup required — at pinaka.sh.

FAQ

Is the OWASP LLM Top 10 the same as the OWASP Web Application Top 10?

No. The classic OWASP Top 10 covers web application risks like SQL injection and broken access control. The LLM Top 10 is a separate list focused on risks that emerge specifically from using Large Language Models — such as prompt injection, excessive agency, and sensitive information disclosure through a model's context window. The two lists complement each other; if you are building an AI-powered web application, both apply.

What is prompt injection and why is it the top risk for AI agents?

Prompt injection occurs when an attacker crafts input — directly or embedded in content the agent reads — that overrides the model's intended instructions. For a pure chatbot this might cause a rude response. For an agent with tool access, a successful prompt injection can instruct the agent to exfiltrate data, call destructive APIs, or forward sensitive information to an attacker-controlled endpoint. Because agents act autonomously on instructions, there is often no human in the loop to catch the manipulation in real time.

What is the OWASP MCP Top 10?

The OWASP MCP Top 10 is an extension of the LLM guidance focused on the Model Context Protocol — the emerging standard that lets AI agents discover and invoke tools from external servers. It covers risks specific to that architecture, including tool poisoning (where a malicious MCP server advertises harmful tool definitions), rug-pull attacks (where a server changes its behaviour after a client has approved it), and trust confusion between agents that delegate tasks to one another.

How often should I reassess my AI agent's attack surface?

Continuously. Agent attack surface changes every time you add or update a tool, register a new MCP server, change permission scopes, or update a dependency. Point-in-time assessments done at release quickly become stale. Security platforms that monitor on a short, regular cadence — and alert on drift — are better suited to the fast-moving nature of agent development than periodic manual reviews.

Do I need to share my source code with a third party to get my agent's tools audited?

Not necessarily. Pinaka's Agent Surface analysis runs locally on your own repository, so your source code never leaves your machine. This makes it practical to use even for proprietary or sensitive codebases where sharing code externally is not acceptable.