An agent framework can organize tool calls and make it easier to add approvals, but it cannot decide whether an action is authorized. Treat the framework as orchestration software: let the model propose actions, then enforce permissions and approvals in the component that actually performs them.
Why an agent framework is not a security boundary
An AI agent can plan a sequence of steps and use tools along the way. That means a failure can extend beyond an incorrect answer: depending on its connections, an agent may read data, send a message, modify a system, or trigger another side effect. Anthropic describes agent behavior as the result of the model, its harness, its tools, and its environment working together—not the framework alone. Anthropic’s guidance on trustworthy agents and the OWASP AI Agent Security Cheat Sheet both point toward controls outside the model’s judgment.
The useful distinction is between a proposal and an authorized operation. A model can request an action; a trusted execution path must independently decide whether the current actor may perform that specific action on that specific target with those parameters. A framework’s built-in approval feature can support this design, but a model-generated “approved” value or a generic approval flag is not authorization by itself.
How do I limit what an AI agent can do?
Start by reducing the tools and permissions
Expose only the functions required for the task. Prefer a purpose-built tool for reading a particular dataset or writing a particular kind of record over an open-ended shell, broad extension, or general-purpose tool. If a task only requires reading, grant a read-only scope rather than write access. Restrict both the tool’s operations and the connected system’s permissions; a narrow tool is less protective if its credentials still have broad access.
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Where practical, use a scoped identity tied to the user or task rather than a shared, highly privileged account. Separate agents or workflows that have different data access or side-effect requirements. OWASP’s guidance on excessive agency emphasizes limiting functionality, permissions, and autonomy to what the application needs.
Match controls to the action’s risk
Classify tool actions by what they can affect, not by the framework that invokes them. These questions help determine the right scope and review:
- Permission: Is the operation read-only or can it write, delete, or change access?
- Side effect: Could it send a communication, publish content, spend money, or trigger another system?
- Data sensitivity: Does it expose personal, confidential, or otherwise restricted information?
- Reversibility and impact: Can the action be undone, and how large is its possible effect?
- Independent authorization: Does the downstream system check the actor’s permission for this operation, or is it relying only on the agent?
Use this assessment to decide which actions need tighter scopes, human review, rate limits, or additional checks. It is a control-design framework, not a ranking of agent products.
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How do I stop prompt injection from using my agent’s tools?
Do not treat prompt filtering as the security boundary. Instructions or misleading content can arrive directly from a user or indirectly through retrieved documents, web pages, tool responses, or stored session material. Those inputs can influence a model even when they are not intended as privileged instructions. OWASP’s prompt injection prevention guidance and Microsoft’s Agent Safety guidance support treating such material as untrusted across the workflow.
Keep trust boundaries explicit. Do not promote external content into system-level authority, and do not assume that tool output is safe because it came from an internal connector. Validate and sanitize model-generated output before using it in a command, query, rendered page, or other sensitive operation. The critical safeguard is that untrusted text must not be able to grant itself permission: the execution path still has to check whether the requested operation is allowed.
Should agent tool calls require human approval?
Require review for actions whose consequences warrant a person’s judgment: for example, high-impact, hard-to-reverse, sensitive, or externally visible operations. Make the approval show the actual tool, target, and parameters that will be used. If any of those change after approval, require a new decision rather than carrying the old approval forward.
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For multi-step tasks, reviewing a meaningful plan can give a person a clearer view of the intended work than approving a succession of isolated, low-context prompts. Anthropic discusses plan review as one way to improve oversight. At the same time, OWASP warns about approval fatigue: repetitive click-throughs can train reviewers to approve without scrutiny. Reserve human review for meaningful risk, and keep routine, low-risk actions within narrowly scoped permissions.
Approval complements enforcement; it does not replace it. The system performing the side effect should still verify that the action is allowed. Anthropic summarizes the broader point: “Prompt injection illustrates a more general truth about agentic security: it requires defenses at every level, and on choices made by every party involved.” The statement appears in its April 9, 2026 article, “Trustworthy agents in practice.”
Where should authorization happen?
Check authorization immediately before the side effect—in the tool’s execution path or the downstream system. The check should use the current actor, operation, target, and normalized arguments, not just a prior model decision. A permission to update one record should not become permission to update another because the model changed a parameter after review.
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Bind approvals to the exact operation and its parameters. Protect against replay or duplicate execution, and require fresh approval if the action changes. If a required policy or approval service is unavailable, fail closed: do not perform the protected operation. Whenever the connected system can enforce its own access rules, keep those rules active rather than relying exclusively on checks in the agent layer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams test and contain agent failures?
Test the boundary with instrumented tools
Use harmless data and tools that record requests without carrying out real-world side effects. Exercise both direct attacks in user input and indirect attacks embedded in retrieved content or tool responses. Include requests for unauthorized actions, attempts to escalate privileges, and changes to targets or parameters after an approval.
For each test, record the tested agent and policy versions, the expected result, what the tool actually received, whether approval was requested or denied, and any remaining risk. The goal is not only to see whether a prompt filter catches a phrase; it is to verify that the protected operation is denied when policy says it is not allowed.
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Limit the damage if something goes wrong
Set resource and rate limits so a looping or misdirected agent cannot consume unbounded capacity or repeat actions indefinitely. Use scoped identities, monitor consequential operations, and retain audit records suitable for investigation. Handle logs carefully: Microsoft notes that trace-level logging can include message content and personally identifiable information, so log access, retention, and collection need their own privacy controls.
Practical review checklist
- Does each agent have only the tools, data, and system permissions its task requires?
- Can read-only tasks use read-only access, and can broad tools be replaced with purpose-built functions?
- Are user input, retrieved material, tool responses, session state, and model output treated as untrusted at sensitive boundaries?
- Does the execution path independently check the current actor, operation, target, and arguments before a side effect?
- Is approval tied to the exact action and invalidated if its parameters change?
- Are human reviews reserved for consequential actions and presented with enough detail to judge them?
- Do tests cover direct and indirect injection, unauthorized requests, privilege escalation, and altered parameters?
- Are resource limits, rate limits, monitoring, audit retention, and log privacy controls in place?
Framework features can help implement these controls, but they do not make the agent trustworthy by themselves. Judge the design by what the execution path permits when a model is wrong, manipulated, or operating on untrusted content.
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