The OWASP AI Top 10 identifies the most critical security risks in AI systems, including prompt injection, data leakage, and memory poisoning.
The first documented cyberattack carried out largely by AI agents did not start with a sophisticated exploit. It started with an email. Anthropic reported attackers using autonomous AI agents to execute an entire attack chain at a speed no human team could match, making thousands of requests per second. No keyboard mashing. No sleep. Just agents doing what they were told.
That is the new reality security teams are trying to catch up to, and most of the tools they have were not built for it. In this guide, we break down the OWASP AI Top 10, explain each risk with examples, and show how to secure modern AI systems.
Key Takeaways
- The OWASP AI Top 10 covers the most critical LLM security risks that developers and security teams need to know right now.
- Prompt injection is still the top threat, but agentic AI has introduced new attack surfaces like memory poisoning and inter-agent communication exploits.
- Gartner projects that 40% of enterprise applications will integrate AI agents by the end of 2026.
- Traditional security tooling was built to detect human behavior, not autonomous agent workflows.
- The OWASP LLM Top 10 is a starting point, but it needs to be paired with an actual AI security framework to be useful.
What Is the OWASP AI Top 10?
The OWASP AI Top 10 is a security framework developed by OWASP that identifies the most critical risks in AI and large language model (LLM) systems, including prompt injection, data leakage, tool misuse, and memory poisoning.
It helps developers and organizations identify vulnerabilities unique to LLM-powered applications and apply safeguards to reduce real-world security threats.
In 2023, they released the OWASP LLM Top 10, a dedicated framework covering the most critical risks in large language model applications. It has since been updated for 2025. And in early 2026, OWASP published a separate list specifically for agentic AI applications.
The OWASP AI Top 10 does not replace traditional security thinking. It layers on top of it, specifically for systems where the software is making decisions, not just responding to requests. That shift changes almost everything about the threat model.
Why OWASP AI Top 10 Matters for Agentic AI Security
Standard AI applications are reactive. You send a message, you get a response. Agentic AI is a bit different. These systems set goals and break them into steps. They execute tasks using external tools, retain memory across sessions, and coordinate with other agents.
McKinsey reports that 39% of companies are already running experiments with agentic AI. Most of those deployments are connecting agents to real systems: databases, APIs, code execution environments, and email clients. The attack surface that comes with that is not theoretical.
Here is the part that makes agentic AI security genuinely harder than traditional security:
- A single manipulated instruction can redirect an agent’s entire workflow.
- Agents inherit and sometimes retain credentials from other agents.
- Memory poisoning is persistent, not a one-shot attack.
- Most monitoring tools flag human anomalies, not autonomous ones.
An agent executing 10,000 perfectly sequenced API calls looks completely normal to a SIEM. That is the gap the OWASP LLM Top 10 and the agentic AI framework are trying to address.
How AI Security Differs from Traditional Security
AI systems, especially agentic ones, behave very differently from standard applications. The OWASP AI Top 10 reflects this shift by focusing on dynamic, decision-making systems rather than static inputs and outputs.
| Dimension | Traditional Security | AI / Agentic Security |
| Attack Surface | HTTP requests, form inputs | Prompts, documents, tool outputs, memory |
| Trust Model | User roles and sessions | Agent delegation and inherited permissions |
| Attack Persistence | Single-session attacks | Multi-session memory poisoning |
| Monitoring | Human behavior anomalies | Autonomous agent behavior patterns |
| Failure Mode | Predictable errors | Cascading, emergent failures |
OWASP AI Top 10 Risks (2026)
The OWASP AI Top 10 identifies the most critical security risks in AI systems:
1. Prompt Injection – Malicious instructions hidden in inputs manipulate AI behavior
2. Sensitive Data Exposure – AI systems leak confidential or training data
3. Insecure Output Handling – Unvalidated AI outputs lead to downstream attacks
4. Excessive Agency – Over-permissioned agents increase risk surface
5. Tool Misuse – AI agents misuse APIs or external tools
6. Insecure Memory – Persistent memory poisoning affects future decisions
7. Supply Chain Vulnerabilities – Third-party components introduce hidden risks
8. Model Denial of Service – Attackers overload AI systems with expensive inputs
9. Overreliance on LLMs – Blind trust in AI outputs leads to errors
10. Inadequate AI Governance – Lack of oversight increases system-wide risk
OWASP AI Top 10 Risks Explained
Here is a breakdown of the ten risks, what they look like in practice, and what actually needs to happen to address each one.
Prompt injection
Prompt injection is the top LLM security risk on the OWASP list, and it is not hard to see why. An attacker embeds malicious instructions inside content the agent is expected to process: a PDF, an email, a web page, or an API response. The agent reads it and treats it as a legitimate instruction.
The reason this keeps working is structural. LLMs use the same attention mechanism for instructions and data. They cannot fundamentally tell the difference between here is content to analyze and here is what you should do next.
Sensitive data exposure
LLMs can inadvertently leak sensitive information in several ways. They may reproduce training data that should have been scrubbed. They may include data from previous sessions in responses to new users. Or they may be manipulated into extracting and returning data they were never supposed to surface.
This is one of the most common AI security risks in production systems. Logs containing PII, system prompts with internal credentials, or training corpora that include private records. These all become potential exposure points.
Insecure output handling
This is kind of similar to prompt injection but distinct enough to deserve its own place. When an application takes LLM-based output and passes it directly to another system without validating it, the LLM becomes a vector for attacks.
If the output goes into a SQL query, you get LLM-mediated SQL injection. And if it goes into a shell command, you get remote code execution. If it goes into a browser renderer, you get XSS.
The fix is to treat LLM outputs the same way you would treat any untrusted user input. Validate before passing downstream. Sanitize for the context. Do not assume that because the model produced it, it is safe.
Excessive agency
An agent that can do too much is an agent that can break too much. Excessive agency is what you get when an LLM has more permissions, tool access, or autonomy than it actually needs to complete its task.
This is a least-privilege problem. If a customer support agent has read-write access to your entire database, and it gets manipulated, you have a very large problem. If it only has read access to the tickets it is supposed to handle, the blast radius shrinks considerably.
Reducing excessive agency means auditing tool permissions regularly, scoping access per task rather than per agent, and requiring human approval for any action that is hard to reverse.
Tool misuse
Agents don’t just think, they act. When an agent has access to APIs, shell commands, search tools, or communication services, it has the ability to cause real-world harm through misuse of those tools. The risk is not necessarily unauthorized tools. It is legitimate tools used in ways they were not supposed to be used.
OWASP’s agentic framework records scenarios like recursive API calls that exhaust budgets. This chains a low-privilege lookup with a high-privilege write operation.
Insecure memory
This one gets underestimated because the attack isn’t immediate. Through crafted inputs, agents with constant memory can also get their memory corrupted. And once corrupted, the false information continues across sessions and influences future decisions without any visible prompt manipulation.
For example, researchers demonstrated this by injecting false refund policies into an RAG knowledge base of a CRM system through a manipulated document. The agent learned and retained the wrong policy, then applied it to every customer interaction from that point forward.
Unlike a standard prompt injection attack (which affects one conversation), memory poisoning has a compounding effect. The mitigation is validating memory writes against source integrity signals, running periodic memory audits, and keeping high-trust and low-trust memory stores separate.
Supply chain vulnerabilities
AI agents often pull in external components at runtime, like MCP servers, prompt templates, third-party schemas, etc. Any of those components can be compromised before they reach your system.
A real-world incident documented on npm involved a malicious MCP server disguised as the Postmark email service. Every email processed by agents using that server was silently forwarded to an attacker. Private communications got leaked for weeks without being detected.
Supply chain risk for AI agents looks a lot like supply chain risk for software, just harder to audit because the components are often loaded dynamically and the behavior they inject is in natural language, not code.
Model denial of service
LLMs are expensive. A model denial of service attack exploits that by flooding the model with inputs designed to maximize processing time. For example, very long prompts, recursive context references.
The defenses are rate limiting, context window monitoring, input length validation, and setting hard stops on token generation on every request.
Overreliance on LLMs
This is both a technical problem and a human one. LLMs produce output that reads as confident and authoritative, regardless of its correctness. That creates pressure to trust the output without verifying it. And that happens especially in workflows where the agent’s response feeds directly into decisions or customer communications.
84% of developers now use AI coding assistants. The risk embedded in that number isn’t that AI tools are bad, but the accuracy doesn’t go hand-in-hand with the confidence of the output. A code suggestion that compiles and passes lint can still be wrong.
Over-reliance means designing UIs that surface uncertainty and requiring human review for high-stakes or irreversible actions. It also requires being explicit with users about the limitations of AI-generated outputs.
Inadequate AI governance
Every other risk on this list gets much worse without governance. Not having adequate AI governance means no clear ownership of AI system behavior and no audit trails when something goes wrong.
The AI risk management gap is more than just technical. Teams ship agents to production without documented tool inventories. And that too without any threat models and incident response plans that account for autonomous AI behavior. If something goes wrong, nobody knows who’s responsible or what to roll back.
Common Attack Patterns in Agentic AI Systems
Understanding the OWASP LLM Top 10 individually is useful. Seeing how the risks combine in practice is more useful.
Here are three attack chains that show up repeatedly:
Goal hijack through document injection. The attacker sends an email to an AI assistant that contains hidden instructions in the body or as an attached document. The agent reads the document as part of its task and executes the embedded instructions, forwarding data or modifying a workflow without any human triggering it.
Memory poisoning through RAG Attacker gets a manipulated document into a retrieval-augmented generation pipeline. The agent reads and stores the false information as part of its knowledge base. Every future interaction the agent has is now influenced by the poisoned memory, including decisions made weeks later.
Privilege escalation through agent delegation. A low-privilege agent delegates a task to a higher-privilege agent, which caches credentials for efficiency. A later interaction exploits those cached credentials, gaining access to resources the original agent was never authorized to touch.
Mitigation Strategies: How to Secure AI Agents
To address the risks outlined in the OWASP AI Top 10, security teams need a layered approach. The checklist below maps directly to real-world AI security risks.
Securing inputs and prompts
Don’t trust any external input, and that includes documents, emails, and inter-agent messages. Don’t pass them directly into agent instructions. Use structured validation to separate data from command context wherever the architecture allows.
For prompt injection specifically, maintain explicit instruction channels that can’t be overridden by any data. Always validate agent goals against a fixed policy before any action is taken.
Access control and permissions
Apply least-privilege per task. An agent that needs read access to a knowledge base for a task shouldn’t retain that access when it moves to another task. Use short-lived, task-based credentials. Flag and alert on any privilege escalations.
This is fundamentally the same problem as access control in traditional systems, but agent workflows make it harder to track. Document it explicitly.
Output validation
LLM outputs that feed into other systems need to be validated the same way any untrusted input would be. Check for SQL patterns before database queries. Sanitize for HTML context before rendering. Validate code before execution. Never assume model output is safe because the model produced it.
Monitoring and detection
Standard monitoring tools flag human anomalies. Agents don’t look like humans. Build detection specifically for agentic behaviors. It includes unusual tool call sequences, rate spikes, and unexpected memory writes.
Building an AI Security Framework
An AI security framework isn’t a document. It’s a set of operational practices that evolves with the systems they protect.
- Threat model per agent: what can this agent do, what data can it access, what happens if it is manipulated?
- Tool inventory and policy: Every tool the agent can call should be documented, rate-limited, and validated.
- Memory governance: log all memory writes, validate sources, and audit periodically.
- Incident response plan: who handles an agent going rogue? What gets rolled back and how?
- Human review gates: for any action that is costly or hard to reverse, require human approval regardless of agent confidence.
The AI risk management work that actually matters happens before deployment, not after. If you build an agent, ship it to production, and then add governance, you are doing it in the wrong order.
OWASP Top 10 vs. Traditional Security Models
The original OWASP Top 10 assumes a human is on one end of every transaction. The OWASP AI Top 10 and the newer agentic framework assume software is making the decisions.
That changes the threat model in several concrete ways:
| Dimension | Traditional Security | AI/Agentic Security |
| Attack surface | HTTP requests, form inputs | Prompts, documents, tool outputs, and memory |
| Trust model | User sessions with defined roles | Inherited agent credentials, delegation chains |
| Attack persistence | Usually single-session | Memory poisoning is multi-session |
| Monitoring | Log and alert on human anomalies | Must detect autonomous agent drift |
| Failure mode | Predictable error states | Emergent, cascading failures across agents |
Traditional security models are still necessary. They are just not sufficient for LLM security risks.
Final Thoughts
The OWASP LLM Top 10 and the agentic AI security framework that followed are the most practical starting points we have right now for understanding what goes wrong with AI systems in production. They are not complete answers. They are a vocabulary for having the right conversations before something breaks.
If you are building with LLMs or deploying agents, the question is not whether these risks apply to you. Most of them do. The question is which ones are the highest priority for your specific architecture and what you have actually done about them.
FAQs
Prompt injection is a critical risk in the OWASP AI Top 10 where attackers embed malicious instructions inside inputs such as documents, emails, or API responses. These instructions manipulate the AI system’s behavior, causing it to leak data or execute unintended actions.
The biggest risks in the OWASP AI Top 10 include prompt injection, sensitive data exposure, insecure output handling, and memory poisoning. These vulnerabilities allow attackers to manipulate AI behavior, extract confidential data, or exploit agent workflows in real-world systems.
Agents take actions, call tools, retain memory, and talk to other agents. That adds attack surfaces like tool misuse, memory poisoning, etc., that static LLM apps do not have.
No. You’ll still need access controls, output validation, and governance that assigns clear ownership over AI behavior. It’s a security guidance, not a security program.
