An AI agent that can read and send email may turn an instruction hidden in an incoming message into a data leak. The risk is not the word “agent”; it is the combination of what its tools can do, which permissions those tools carry, and how freely actions can proceed. To reduce that risk, inventory each capability, remove unnecessary authority, require approval for consequential actions, and enforce authorization outside the model.
What makes an AI agent too powerful?
OWASP calls the vulnerability “Excessive Agency”: damaging actions can follow unexpected, ambiguous, or manipulated model outputs when connected tools can carry them out. OWASP identifies three common causes:
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- Excessive functionality: The agent has tools or operations the task does not require.
- Excessive permissions: Its tools can access more data or change more systems than necessary.
- Excessive autonomy: The agent can proceed with consequential actions without an appropriate review boundary.
These factors interact. A narrow tool with read-only access has a different risk profile from a broad shell or email tool holding an administrator credential. OWASP’s definition and examples are in LLM06:2025, Excessive Agency.
Prompt instructions are not authorization controls. An agent can encounter malicious instructions embedded in ordinary content such as email, files, or webpages. NIST describes this as agent hijacking: untrusted content exploits weak separation from trusted instructions. The model’s promise to ignore such content cannot substitute for checks in the execution layer or the downstream system. See NIST CAISI’s discussion of agent-hijacking evaluations.
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How much access should an AI agent have?
Give it only the tools, data, permissions, and autonomy needed for its task, for only as long as it needs them. Start by recording what each agent can do—not just the name of the integration it uses.
Build a capability inventory
For each agent and tool, document:
- The operation enabled: for example, search records, draft a message, send a message, run code, or change a setting.
- The downstream systems and specific resources it can reach.
- The identity and credentials used, including their scope and lifetime.
- Whether access is read-only, constrained-write, or unrestricted write.
- Whether the environment and content are trusted, and whether the tool can execute code or communicate externally.
- Whether an action is reversible, externally visible, financial, administrative, or destructive.
- What approval and audit records apply, and what sandbox or network-egress limits exist.
NIST’s tool-use taxonomy includes capabilities for perception, reasoning, analysis, resource management, and actions such as computer use, code execution, software or physical extensions, and human interaction. Its categories help describe capability; they are not a universal risk score. The distinctions below summarize access levels and environment context described in NIST’s tool-use article, released August 5, 2025 and updated August 7, 2025.
| Access pattern | What it permits | Questions to assess |
|---|---|---|
| Read-only | Inspect data without changing it. | Is the data limited to the task, and could sensitive information still be exposed? |
| Constrained-write | Make changes within defined limits. | Are the allowed targets and operations enforced outside the model? |
| Write | Change state without the same narrow constraints. | Could the action affect other users, external systems, money, or production? |
Any of these patterns should be assessed in context: trust in the environment, sensitivity and scope of data, credential authority, reversibility, and how the tool is actually constrained all matter.
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How do you reduce an agent’s authority?
Remove unnecessary tools and narrow operations
- Disable tools and extensions the task does not need.
- Prefer a specific operation, such as “create a draft for review,” over an open-ended shell or URL tool when that meets the requirement.
- Split broad integrations into narrowly scoped operations rather than giving every task the full extension.
- Use read-only access where possible, and restrict writable resources to those required for the task.
Use dedicated, limited credentials
Use an identity dedicated to the agent rather than a person’s account or a shared administrator credential. Separate read-only and write-capable identities where feasible. Issue scoped, short-lived credentials, define explicit permission policies, attribute actions to the agent identity, and revoke old or unused access. OWASP’s AI Agent and MCP Security guidance describes these identity and access practices under its “least agency” principle: give an agent only the autonomy, tools, and access its task requires, for only as long as it needs them.
Enforce authorization at execution time
Have the execution component or downstream service check the actor, requested operation, target, and applicable policy for every request. A system should reject an unauthorized operation even if the model requests it confidently or a prompt says it is allowed. OWASP calls for downstream authorization to validate requests; model instructions alone do not enforce access control.
Isolate execution
Run code and tools in a sandbox or isolated, disposable environment. Limit filesystem mounts and network egress, and keep production credentials out of the agent environment. Treat permission prompts as an interface, not a security boundary against a manipulated agent; isolation and enforced controls must do that work. Implementation varies: an operating-system sandbox may not cover every file tool or MCP server, so assess each integration’s actual boundary.
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When should an AI agent ask for approval?
Set the boundary according to the consequences of the action, not a blanket rule for every tool call. A useful review compares read with write, reversible with irreversible, internal with externally visible, and low-impact with financial, administrative, or destructive operations.
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- Require review when an action changes state, reaches beyond the agent’s task, or has material external consequences.
- Use stronger controls, such as step-up authentication or a second authorization check, for high-impact operations.
Approval must authorize a specific action—not broadly “allow this agent.” Bind it to the tool, target, parameters, actor, and time window, and re-check that the approved action is the one about to execute. OWASP’s AI Agent Security Cheat Sheet recommends independently validating scope, privilege, and approval; using short-lived authorization artifacts; and failing closed if policy, approval, or audit checks fail. Idempotency, where available, can also help avoid duplicate effects when an action is retried.
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Test the workflow you actually deploy, including its tools, credentials, data sources, and approval steps. Include cases where untrusted content attempts to redirect the agent, where a tool is asked to exceed its scope, and where approval or policy checks are missing or unavailable. Verify that the execution layer rejects unauthorized actions rather than relying on the model to refuse.
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NIST CAISI recommends adaptive evaluations, task-specific assessment, and testing across multiple attempts. Its cited tests used Claude 3.5 Sonnet; that does not establish performance for other models or every deployment. An evaluation only covers the threats and setup it tests, so repeat and update it when tools, models, permissions, or workflows change.
Log tool calls, identities, targets, approvals, and outcomes so you can investigate activity. Monitoring and rate limits can help detect undesirable actions and limit damage, but OWASP notes they do not by themselves prevent excessive agency. Keep tests and logs focused on whether authorization, isolation, and approval boundaries are actually enforced.
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