The Tool Desk
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What least privilege means for an AI agent
An agent’s effective authority is the combination of its identity, the tools it can choose, the functions those tools expose, the credentials they use, and the downstream resources and actions those credentials permit. A tool with a narrow name can still be overpowered if its credential can reach unrelated data or perform unnecessary actions. OWASP groups the risks as excessive functionality, excessive permissions, and excessive autonomy in its LLM06:2025 Excessive Agency guidance.
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Apply the boundary across the whole chain—not just the agent’s tool list. Microsoft’s guidance likewise recommends least privilege for agent access and warns that individually narrow roles can combine into broad effective access. Review permissions together across tools and systems, not one grant at a time (Microsoft Learn: Least privilege for AI agents).
How to reduce an agent’s authority
- Define the task and its required access. Write down the specific data, resources, and actions the workflow needs. Treat anything not required as out of scope.
- Remove unnecessary tools and functions. If the task does not need a tool, do not expose it. For tools that remain, remove unused functions and replace broad capabilities such as arbitrary command execution with narrower operations where possible. OWASP identifies unnecessary functionality as one way an agent can gain excessive agency (OWASP).
- Scope each identity and credential. Limit access to the needed resource, tenant, fields, and actions. Check the combined permissions granted across services; several narrow roles can add up to more access than the workflow requires (Microsoft Learn).
- Enforce authorization at the point of action. Have the downstream API or a trusted policy enforcement layer decide whether each requested action is allowed. Do not ask the model to make the final authorization decision. OWASP puts it plainly: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” (OWASP).
- Separate low-impact work from side effects. Keep reading and drafting distinct from sending, submitting, updating, deleting, or changing permissions. Use a separate authorization check—and, when appropriate, a human approval step—for consequential actions.
- Review, monitor, and revoke. Log which identity acted, which user or workflow authorized the action, the tool and scope involved, and whether policy and approval checks passed. Review grants when the workflow changes and provide a way to revoke access. Step or rate limits may help limit damage, but they do not replace authorization (OWASP; Microsoft Learn).
Should the agent use its own identity or the user’s credentials?
Choose an access pattern based on who the agent is acting for and what the downstream service should authorize. Delegated access is appropriate when an agent acts on a signed-in user’s data and the service should enforce that user’s access. For background automation without a signed-in user, app-only access may fit; keep its permissions to the minimum the workflow needs. Where supported, a managed identity can avoid handling stored secrets for service-to-service access. An agent-specific identity can also improve attribution and lifecycle governance. These are patterns, not universal requirements for every platform (Microsoft Learn: Access patterns and controls for AI agents; Microsoft Learn: Least privilege for AI agents).
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| Pattern | When it fits | Authorization basis | Key checks |
|---|---|---|---|
| Delegated access | The agent acts on a signed-in user’s data. | The downstream service should apply the user’s access. | Confirm the user’s scope is appropriate for the task and that each action is authorized. |
| App-only access | Background automation has no signed-in user. | An application identity’s permissions. | Grant only the permissions the workflow needs; review the combined scope across connected services. |
| Managed identity, where supported | Service-to-service access where avoiding stored secrets is useful. | The managed identity’s assigned access. | Scope the identity to required resources and actions; do not treat secret handling as a substitute for authorization. |
For any pattern, consider whether authorization should follow a user or an application, whether access can be limited to the relevant resource or operation, how actions will be attributed, and how access can be reviewed and revoked. The pattern does not decide which actions are safe; that still depends on the task and its side effects.
Which actions should require human approval?
Require approval when an action has significant impact, affects other people, exposes sensitive data, changes access, or is difficult to reverse. Examples include sending a message externally, submitting a consequential request, deleting or modifying important records, or changing permissions. Keep read and draft actions separate so the agent can prepare work without automatically carrying out a consequential step. Microsoft’s agent safety guidance notes that an agent can call any function exposed as a tool and choose its arguments, which is why high-impact actions need controls beyond the model’s judgment (Microsoft Learn: Agent Safety).
Rank #2
Approval is an additional control, not a replacement for downstream authorization. The system should still verify that the identity is allowed to perform the approved action when the tool call is made (Microsoft Learn: Access patterns and controls for AI agents).
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How to reduce prompt-injection and argument risks
Treat model-generated arguments, retrieved content, and tool results as untrusted input. A document or email can contain indirect prompt injection intended to influence a later tool call; the content may be useful evidence, but it should not grant authority or override policy.
Rank #3
- Validate arguments against allow-lists, expected types and ranges, and permitted paths.
- Use parameterized queries rather than building queries from untrusted text.
- Keep instructions distinct from retrieved data, and avoid giving a tool broad, open-ended capabilities when a narrow operation will do.
- Make the policy layer check the requested action and resource independently of the model’s explanation or apparent intent.
These controls reduce opportunities for misuse, but they do not make prompt injection impossible. The security boundary must remain effective even when the model is influenced by hostile content (Microsoft Learn: Agent Safety; OWASP).
What to log and review
For each tool action, record enough information to reconstruct who or what acted and why the action was allowed:
- Agent identity and the user or workflow that authorized the run.
- Tool, requested action, resource scope, and relevant authorization context.
- Whether policy checks and any required approval succeeded.
- Outcome and time of the action, using the organization’s normal audit practices.
Monitor for unexpected tools, scopes, or action patterns; review grants as workflows and connected systems change; and maintain a prompt revocation route. Rate or step limits can help detect or constrain abnormal activity, but they are monitoring and damage-limiting measures rather than permission checks (OWASP; Microsoft Learn).
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Responsibility depends on the deployment model, but using a hosted model or agent platform does not automatically transfer authorization duties to the provider. Microsoft’s shared-responsibility guidance says customers remain accountable for agent identity and credential scope, action authorization, data, oversight, and governance (Microsoft Learn: AI agent shared responsibility model).
NIST NCCoE’s February 2026 concept paper raises open questions about least privilege when an agent’s necessary actions are not fully predictable, how authorization should respond to changing context, and how to bind agent actions to human authorization and verifiable audit records. It is a concept paper soliciting input, not a finalized standard (NIST NCCoE concept paper).
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