What is agentic security?

Agentic security is the practice of securing AI agents: systems that do not just generate text but take actions through tools, plan across multiple steps, hold memory, and operate with some autonomy. It focuses on the actions an agent can take, the tools and credentials it holds, and the blast radius if it is manipulated.

How is agentic security different from AI security?

AI security is the broad field of protecting AI systems and their outputs. Agentic security is the layer that matters once the AI starts acting. Traditional AI security worries about a wrong or leaked answer. Agentic security worries about a wrong action with real consequences, like an agent tricked into sending data out or running a command. They are nested, not separate: agentic security builds on AI security and adds the action layer.

Is agentic security just AI security rebranded?

Partly. A lot of what gets sold as agentic security is ordinary LLM security with a new label, because prompt injection, data leakage, and supply chain risk look the same with or without an agent. The part that is genuinely new is the action layer: which actions the agent may take, the tools and identities it holds, and the audit trail of what it did.

What is the OWASP Top 10 for Agentic Applications?

The OWASP Top 10 for Agentic Applications (ASI01 through ASI10) is a framework released in December 2025 by the OWASP GenAI Security Project. It ranks the most critical risks for autonomous AI systems, from agent goal hijack and tool misuse to memory poisoning, insecure inter-agent communication, and rogue agents.

How do you secure an AI agent?

Give the agent the least authority it needs, treat tool descriptions and outputs as untrusted input, keep private-data readers separate from external-send tools, require a human for irreversible actions, protect the agent memory from poisoning, and log every action it takes.

Agentic security, and why it is not just AI security.

Agentic security is AI security for systems that act. Traditional AI security protects a model and what it says. Agentic security protects what an agent does. Once an AI can call a tool, run code, query a database, move money, or email a stranger, the failure mode stops being a wrong answer and becomes a wrong action with real consequences. A jailbroken chatbot leaks some text. A jailbroken agent with tools wires the money.

What actually changes

Agency

A model predicts text. An agent takes that text and acts on it: a tool call, a shell command, a database write. The output is the start of a story, not the end of one.

Autonomy

An agent runs many steps and adapts as it goes, often with nobody reading each one, so a bad decision can compound before anyone notices.

Tool access

Agents reach real systems through MCP, APIs, shells, and databases. Every connection is a new privilege and a new way for a prompt injection to become a real action.

Memory and state

An agent reasons from prior context, and that memory can be poisoned. Poisoned context follows the agent into every future step.

Where the hype outruns reality

A lot of what gets sold as agentic security is ordinary LLM security with a new label. Prompt injection, data leakage, and supply chain risk are the same with or without an agent. What is genuinely new is the action layer: which actions an agent may take, the tools and credentials it holds, keeping a private-data reader and an external sender off the same agent, and auditing what it actually did.

The OWASP Top 10 for Agentic Applications (2026)

ASI01: Agent Goal Hijack
ASI02: Tool Misuse and Exploitation
ASI03: Agent Identity and Privilege Abuse
ASI04: Agentic Supply Chain Compromise
ASI05: Unexpected Code Execution
ASI06: Memory and Context Poisoning
ASI07: Insecure Inter-Agent Communication
ASI08: Cascading Agent Failures
ASI09: Human-Agent Trust Exploitation
ASI10: Rogue Agents

How to secure an AI agent

Give the agent the least authority it needs, per tool and per credential.
Treat tool descriptions, outputs, and retrieved content as untrusted.
Break the lethal trifecta: separate private-data readers from external-send tools.
Put a human in the loop for irreversible actions.
Protect agent memory from poisoning and cross-session leakage.
Log every action the agent takes and review it.

Related: AI security and MCP security.