OWASP Agentic Top 10: What Every CISO Needs to Know About AI Agent Security
Jeff Sowell · 2026-04-16 · AI Security
The OWASP Top 10 for Agentic Applications is the first framework purpose-built for autonomous AI security. Here's what each risk means, why it matters, and what to do about it — in plain language.
A new framework for a new kind of risk
AI agents are no longer experimental. Coding assistants write and deploy production code. Workflow agents process invoices, manage tickets, and onboard customers. Security teams use agents for triage, log analysis, and threat hunting. These systems don't just answer questions — they plan, decide, and act.
The OWASP Top 10 for Agentic Applications (2026) is the first security framework designed specifically for this class of system. Published by the same community behind the OWASP LLM Top 10, it addresses the risks that emerge when AI systems have tool access, persistent memory, and the autonomy to chain multiple actions together without human approval at each step.
If your organization uses AI coding assistants, workflow automation, or any system where an AI agent can read, write, or execute — this framework applies to you.
How this differs from the OWASP LLM Top 10
The OWASP LLM Top 10 (2025) covers risks in LLM applications — prompt injection, data poisoning, information disclosure. Those risks still apply. But agentic systems introduce a new layer:
- LLM risks are about what the model *says* — wrong answers, leaked data, manipulated responses
- Agentic risks are about what the model *does* — unauthorized actions, cascading failures, rogue behavior
An LLM that hallucinates is a quality problem. An agent that hallucinates and then acts on it — sending an API call, modifying a database, deploying code — is a security incident.
The 10 risks, explained
ASI01 — Agent Goal Hijack
What it is: An attacker manipulates the agent's objectives so it pursues attacker-defined goals while appearing to operate normally.
How it happens: Through prompt injection, context poisoning, or goal substitution. Unlike basic prompt injection against a chatbot, goal hijack against an agent means the attacker's instructions get executed through real tools — file writes, API calls, code deployments.
Real-world scenario: A coding agent reads a poisoned markdown file in a repository that contains hidden instructions. The agent's goal shifts from "review this code for bugs" to "add a backdoor to the authentication module" — and it has the access to do it.
What to do:
- Implement goal verification at each reasoning step
- Use instruction hierarchies that prioritize system-level goals
- Require human approval for actions that deviate from expected patterns
- Log the full reasoning chain, not just inputs and outputs
Maps to: OWASP LLM Top 10 — LLM01 (Prompt Injection). Prompt injection is the primary attack vector for goal hijack.
ASI02 — Tool Misuse and Exploitation
What it is: Attackers trick the agent into using its tools in unintended ways — sending unauthorized API calls, writing malicious files, executing harmful commands.
How it happens: The agent has legitimate access to tools (APIs, databases, file systems, shells). The attack doesn't compromise the tool itself — it manipulates the agent into using the tool maliciou