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An AI agent ends up with more authority than the user it serves when it acts through a broadly privileged identity, or when it is given more tools, data, or autonomy than its task needs. The fix is structural: limit the agent’s tools and data to the task, carry the user’s authorization scope into every action, have trusted code check each action before it runs, and require approval for sensitive operations. The model’s instructions do not limit what the agent can do. The permissions attached to its tools and credentials do.
Why an agent can end up with more power than the person using it
An agent’s real power comes from its tools, credentials, integrations, and execution environment. What the model is told to do, or says it intends to do, does not restrict those underlying powers. If a tool can delete records, the agent can delete records, whether or not the user who asked for a summary was ever allowed to do that.
The OWASP GenAI Security Project describes this risk as “excessive agency” in its LLM06:2025 Excessive Agency entry. It breaks the problem into three causes:
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- Excessive permissions: the tools or identities behind them can do more than the task requires.
- Excessive autonomy: the agent can take high-impact actions without a human checking them first.
In practice these causes often combine. A common pattern is a single generic, high-privilege service identity that the agent uses for every request. Each request then runs with that identity’s authority, not with the authority of the person who asked. OWASP’s guidance is that actions should run “in the context of that specific user, and with the minimum privileges necessary,” which is the opposite of that pattern.
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How to bring agent permissions back in line with user permissions
Work through these controls in order. Each one narrows what the agent can reach, and the later steps depend on the earlier ones being in place.
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Inventory every tool and integration the agent can call. List each tool, the system it touches, and the operations it allows. Remove any tool the agent’s stated purpose does not require. The point is to stop the agent from having capabilities nobody decided it should have.
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Scope each tool to specific resources and operations. A calendar tool that only needs to read free/busy times should not hold a write scope. Keep read-only and write-capable access as separate tools or identities. OWASP’s AI Agent Security Cheat Sheet states the principle directly: “Apply least privilege to all agent tools and permissions.”
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Propagate the user’s authorization scope. When a user asks the agent to do something, the downstream call should carry that user’s identity or a token derived from it, so the target system applies the user’s own rights. The agent should not be able to reach records the user cannot open.
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Enforce authorization in trusted code at execution time. The model may propose a tool call, but the application or execution layer should confirm that this actor is allowed to perform this exact operation on this exact resource. The OWASP DevSecOps Guideline’s section on AI Agent and MCP Security makes the same point: a tool being available, or being selected by the model, is not proof that the action is permitted.
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Give the agent its own identity. The agent should not run on a developer’s personal credentials. A distinct, attributable identity lets you see which actions the agent took, and lets you revoke its access without affecting people.
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Use short-lived, task-scoped credentials. Tokens should expire quickly and cover only the task at hand. Issue separate credentials for read-only and write-capable work.
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Require action-specific approval for high-risk operations. Deletions, payments, permission changes, and external messages should pause until a person approves that specific action. A general approval for “the agent” is not enough.
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Check each proposed action against the user’s original request. If the user asked for a summary of last quarter’s invoices and the agent proposes to change a vendor’s bank details, that mismatch should block the action or trigger review.
What “trusted code” means in practice
The check in step four has to live outside the model’s prompt and outside any tool description the model can read or change. In concrete terms, it is code in your application, API gateway, or tool runner that receives the requested action, looks up the user and agent identity, evaluates the permission, and returns allow or deny before the downstream system is called. If that check is missing, the model’s own reasoning is the only barrier, and that is the weak point the rest of this list is designed to avoid.
Why prompt injection makes narrow permissions essential
Prompt injection can reach an agent directly from a user, or indirectly through content the agent reads, such as web pages, documents, or emails. The OWASP LLM Prompt Injection Prevention Cheat Sheet covers the input-side defenses. The excessive agency entry adds the consequence side: OWASP describes a case where an injected instruction in an email leads an agent to misuse an email tool. If that tool has broad authority, the manipulation can do broad damage.
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You cannot rely on detecting every injection. Limiting what a compromised or confused agent can do is what keeps a single bad instruction from becoming a large incident. Narrow scopes, user-level authorization, and execution-time checks reduce the damage a manipulated agent can cause, even when the manipulation succeeds.
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How to audit an existing agent
Use these questions to review a deployed agent or a vendor’s agent platform. Each one maps to a control above.
- Scope: Which tools, resources, and operations can the agent reach? Are read and write permissions separate?
- Identity: Does the agent have its own attributable identity? Are its credentials scoped and short-lived?
- Enforcement: Does trusted code check the actor and the exact operation at the moment the action runs?
- Approval: Do high-risk actions require approval for that specific action?
- Intent and audit: Are proposed actions compared against the user’s original request, and does the log record which agent identity made each decision?
A “no” to the first question on scope is often the most useful finding, because it shows where the agent’s reach is larger than its job.
What the guidance does and does not establish
The OWASP sources above are design guidance, not measurements. They explain the failure pattern and recommend controls, but they do not estimate how often agents exceed user permissions or report incident counts. They also do not name products or rate specific vendor implementations, so the audit questions above are the practical tool here, not a ranking. The controls are a synthesis of OWASP’s recommendations and should be adapted to your own systems, data sensitivity, and regulatory obligations.
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