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Prevent an AI agent from taking unintended actions by limiting what it can access, enforcing permissions in trusted code whenever it tries to use a tool, and requiring human approval for high-impact operations. Treat user input and retrieved content as untrusted, isolate execution, and keep logs and recovery controls. These safeguards reduce risk; none guarantees that an agent will always behave as intended.
Why an agent can act outside your intent
An agent can turn a misunderstanding—or hostile instructions embedded in a web page, email, or document—into a tool call. If it has broad permissions, that call may expose data, alter files, send a message, or affect an external service. OpenAI describes this threat as prompt injection and recommends layered defenses rather than relying on a single filter (Understanding prompt injections; Designing AI agents to resist prompt injection).
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The key distinction is between what the model proposes and what the system permits. A model’s judgment is not an authorization check. OWASP recommends least privilege and controls that constrain agent actions, while its excessive-agency guidance emphasizes enforcing authorization in downstream systems (OWASP AI Agent Security Cheat Sheet; OWASP LLM06:2025 Excessive Agency).
Build safeguards around each action
1. Give the agent only the access it needs
Inventory the agent’s tools and classify what each can do: read, create or update, delete, execute, send externally, administer, or spend money. Grant the narrowest permissions needed for the assigned task, scoped by tool, operation, and resource. Where practical, give each agent a separate identity rather than sharing a powerful user or service account. An agent that can read a folder but cannot delete its contents has less room to cause damage than one with unrestricted file access.
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OWASP’s guidance calls for least privilege and per-tool permission scoping. Anthropic likewise describes configurable tool permissions as a practical safeguard, not a guarantee of safe behavior (OWASP AI Agent Security Cheat Sheet; Trustworthy agents in practice).
2. Authorize every consequential tool call outside the model
Put authorization in trusted application code or the downstream service—not in a prompt asking the model to decide whether an action is allowed. Before an operation runs, check the agent’s identity, the requested operation, the target resource, and the arguments. Confirm that the identity owns or is permitted to use that resource, validate inputs, and reject unknown actions by default. The service receiving the request should enforce its own access rules as well.
This creates a firm boundary: the model can suggest an action, but policy code decides whether it executes. OWASP specifically warns against treating model classification as authorization (OWASP LLM06:2025 Excessive Agency).
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3. Require approval for high-impact actions
Set approval thresholds in system policy, not at the agent’s discretion. Require a person to review actions that are irreversible, externally visible, financial, administrative, or otherwise consequential. Before approval, show the exact operation, destination or target, data to be sent or changed, and likely consequences. A vague prompt such as “Allow the agent to continue?” does not give the reviewer enough information to make a useful decision.
Use a preview or draft when possible: let the agent prepare an email, deletion list, or account change, but do not execute it until the reviewer approves the specific action. OWASP includes human approval among its recommended controls for sensitive actions (OWASP AI Agent Security Cheat Sheet).
4. Treat retrieved content as data, not authority
A page, email, or document the agent reads may contain instructions aimed at manipulating it. Do not let those instructions change the agent’s permissions or override system policy. Separate content from trusted instructions in the workflow, constrain where the agent can send data, and limit its ability to transfer sensitive information. Because prompt injection can exploit how an agent interprets external content, combine these measures with narrow permissions and approval gates rather than relying on a text filter alone (OpenAI on prompt injections; OpenAI on designing agents to resist prompt injection).
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5. Isolate execution and prepare to stop or recover
Run tools in a sandbox with only the file, execution, and network access the task requires. Network restrictions can reduce the chance that untrusted content leads to an unauthorized transfer or connection. Sandboxing limits the possible impact of a bad action, but it does not replace access controls or approval checks.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the boundaries before relying on the agent
Test the complete tool path, including authorization checks and downstream services—not just whether the model follows its instructions. Try scenarios such as:
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- A web page or email tells the agent to ignore its task and reveal private information.
- The agent requests an operation or resource outside its assigned scope.
- A tool call contains malformed, unexpected, or excessive arguments.
- An instruction is ambiguous, or the agent requests a consequential action without enough context.
- A workflow must be interrupted after it has started, or a reversible change must be rolled back.
Confirm that unauthorized calls are rejected, high-impact actions pause for specific human approval, and the logs show what happened. Repeat these checks when tools, permissions, or workflows change. This is a practical testing approach based on the threat guidance above, not a published effectiveness benchmark.
Choose controls by the risk they actually reduce
When evaluating an agent platform or designing your own, compare the controls that govern its real operating path:
- Permission granularity: Can access be limited by tool, resource, and operation?
- Enforcement point: Does trusted code or the downstream service authorize each call?
- Approval quality: Are sensitive actions covered, and does the reviewer see the exact target, data, and operation?
- Isolation: Can file, network, and execution access be bounded?
- Operations: Are actions auditable, interruptible, and recoverable where feasible?
- Residual risk and friction: What can still go wrong, and how much delay or review burden do the controls add?
These criteria help expose trade-offs; they are not a comparative vendor ranking. Strong controls may add review steps, but broad permissions or approval prompts without specific action details can leave important risks unaddressed.
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