Understand the Basics of OWASP Agentic Top 10
AI applications are evolving beyond simply getting a question answred or resolving an issue. AI agents can now plan tasks, use tools, access data, interact with other agents, and take variety of actions on behalf of users.
This increased level of flexiblity also introduces new security risks. The OWASP Top 10 for Agentic Applications 2026 focuses on these risks and provides a practical guideline for securing agentic systems.
OWASP Top 10 for Agentic Applications
The 2026 list contains top ten security risks identified:
| ID | Risk |
|---|---|
| ASI01 | Agent Goal Hijack |
| ASI02 | Tool Misuse & Exploitation |
| ASI03 | Identity & Privilege Abuse |
| ASI04 | Agentic Supply Chain Vulnerabilities |
| ASI05 | Unexpected Code Execution (RCE) |
| ASI06 | Memory & Context Poisoning |
| ASI07 | Insecure Inter-Agent Communication |
| ASI08 | Cascading Failures |
| ASI09 | Human-Agent Trust Exploitation |
| ASI10 | Rogue Agents |
1. Agent Goal Hijack
Agent Goal Hijack occurs when an attacker manipulates an agent's goals, instructions, or decision-making process. This can happen via direct prompts, malicious documents, web content, tool outputs, external data, or even messages from another agent. The main difference from a normal prompt injection is the potential impact on the agent's multi step actions.
For example, An agnet can be tricked to read a malicious document by changing its task and using an already available tool to send sensitive information to adifferent place.
Security focus: Treat external content/info as untrusted, always validate agent intention before performing any high-impact actions, apply least privilege, and add human approval stage where necessary.
2. Tool Misuse & Exploitation
Agents are often connected to tools such as databases, email systems, APIs, browsers, shells, or cloud services. Giving an agent access to a tool does not mean it will always use that tool safely. An agent might have permission to read customer information but accidentally expose it through another connected tool. A poorly controlled agent could also repeatedly call expensive APIs or chain several legitimate tools together to perform an unintended action.
Security focus: Apply least privilege to every tool, limit available actions, validate tool parameters, use rate limits, and require approval for destructive or high-impact operations.
3. Identity & Privilege Abuse
Agents need identities and credentials to interact with other systems. When those identities have excessive permissions or when privileges are inherited across agents it becomes an issue. For example, a high-privilege agent could pass a task to another agent while accidentally passing its full access context. A compromised lower-privilege agent could then use those permissions to perform unauthorized actions.
Security focus: Use separate agent identities, short-lived and scoped credentials, apply least privilege, implement per-action authorization, and re-validation when privileges/context change occur.
4. Agentic Supply Chain Vulnerabilities
Agentic systems can depend on much more than general software packages. They may dynamically use models, tools, plugins, MCP servers, agent cards, datasets, prompts, other agents, and external registries. A single compromised component can introduce a high risk by embedding malicious instructions or behavior into an agent's execution chain.
Security focus: Always maintain an inventory of AI components, verify provenance, use SBOMs and AIBOMs, pin dependencies, allowlist trusted sources, and continuously validate important components, packages, dependencies etc.
5. Unexpected Code Execution (RCE)
Some agents can interact directly with an operating system, generate and execute code, run shell commands and install packages. This creates a much larger impact when an attacker is able to influence the agent's generated code or execution flow. For example, a coding agent could be manipulated into running risky command or installing a malicious package.
Security focus: Never execute generated code without any clear undersatnding. Use sandboxed environments, restrict network and filesystem access, avoid eval() in production agents, scan code before execution, and separate code generation from execution.
6. Memory & Context Poisoning
At present, Agents use memory to retain information between tasks. This can include conversation history, summaries, embeddings, RAG data, or information stored by memory tools. If an attacker can insert malicious or misleading information into this context, the agent would unknowingly use it in future decisions.
For example, poisoned information added to a shared knowledge-base could influence future agents and eventually lead to unsafe actions or data leakage.
Security focus: Validate memory writes, isolate user and tenant contexts, track data provenance, limit retention, use trust controls, and support rollback or quarantine of suspicious memory.
7. Insecure Inter-Agent Communication
Multi-agent systems rely on agents communicating with each other through APIs, message buses, shared memory, and protocols such as MCP or A2A. If these communications are not properly protected, attackers may intercept, modify, replay, or spoof messages between agents.
Security focus: Use mutual authentication, encryption, message integrity checks, anti-replay protections, signed agent identities, secure discovery, and strict protocol validation.
8. Cascading Failures
One of the biggest differences with multi-agent systems is how quickly a single problem can spread. A poisoned memory entry, compromised tool, incorrect decision, or malicious message can be passed from one agent to another. Each agent may treat the previous agent's output as trusted, allowing a small issue to become a system-wide failure.
Security focus: Define trust boundaries, limit blast radius, isolate agents, use rate limits and circuit breakers, validate outputs between stages, and maintain strong logging and monitoring.
9. Human-Agent Trust Exploitation
People naturally tend to trust systems that appear confident, helpful, and knowledgeable. Attackers can take advantage of this trust by causing an agent to present unsafe recommendations as legitimate. For example, a finance agent could recommend an urgent payment based on manipulated information, and a user might approve it because the recommendation appears reasonable.
Security focus: Require confirmation for sensitive actions, clearly communicate uncertainty and data provenance, provide independent validation for high-risk decisions, and avoid interfaces that encourage blind trust in the agent.
10. Rogue Agents
A Rogue Agent is an agent that becomes malicious, compromised, or significantly deviates from its intended behavior or authorized scope. The difficult part is that individual actions may look legitimate while the overall behavior becomes harmful. In a multi-agent environment, a rogue agent could also influence other agents or attempt to maintain access to the system.
Security focus: Monitor agent behavior, establish expected behavioral baselines, isolate suspicious agents, maintain kill switches and credential revocation, and require verification before a quarantined agent returns to production.
Why Agentic Security Is Different
The traditional security model for an AI application often focuses on the model, its inputs, outputs, data, and integrations. Agentic systems add another layer: autonomous decision-making and action. An agent can receive an instruction, retrieve information, make a decision, call a tool, pass the result to another agent, and continue the workflow without a human checking every step.
This means a relatively small vulnerability can have a much larger impact when autonomy and multiple integrations are involved.
LLM security asks, "What can go wrong with the model and its inputs and outputs?"
Agentic security also asks, "What can the agent do, what does it have access to, who does it trust, and what happens when something goes wrong?"
Final Thoughts
Agentic AI brings useful capabilities, but autonomy also equally increases the potential attack surface. Tools, identities, memory, external agents, and automated workflows needs to be goverened carefully when designing the security model.
The OWASP Agentic Top 10 is a useful baseline for reviewing these risks for any professional. It could be used together with the OWASP Top 10 for LLM Applications and other relevant security controls/standards rather than as a replacement for them.
For teams/users building agentic applications, most important areas to focus on are least privilege, controlled autonomy, strong identity, secure tool usage, isolated execution, trusted data, and good observability.
Source: OWASP Top 10 for Agentic Applications 2026, OWASP GenAI Security Project - Agentic Security Initiative.