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Separate what an agent can do from what it is asked to do
An agent can propose an action, but it should not decide whether that action is authorized. Prompts and model-generated risk assessments can guide behavior; they cannot replace an access-control decision enforced by a trusted component. OWASP’s AI Agent Security Cheat Sheet says the execution component should check the actor’s authorization and any required approval for the exact action.
This distinction matters because an agent’s inputs may include user messages, documents, web pages, and API responses. Any of these can contain instructions intended to redirect the agent. Treat outside content as data, validate inputs, constrain outputs, and ensure that content cannot grant permissions or override policy.
Build a permission boundary around each agent
Give the agent a distinct identity and accountable owner
Represent each agent as a distinct non-human actor rather than letting it use a person’s administrator credentials. Record the accountable owner, the agent’s purpose, approved tools, and the resources it may access. Separate identities make it possible to attribute actions, review grants, and revoke one agent without disrupting a person’s account.
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NIST IR 8596, identified as an initial public draft from 2025, discusses treating AI systems separately in permission and authorization policies. It also discusses signed and verified agent assertions and tokens as ways to support provenance checks. The document’s draft status matters: this article does not establish whether a later NIST version has been issued.
Define narrow grants for tools, operations, and resources
Specify the permitted tool, operation, resource, and relevant context for each grant. For example, an incident-record reader may need permission to retrieve specified records, but not to edit or delete them. Do not let a connector’s bundled capabilities dictate the agent’s effective access: scope permissions at the tool and resource level where the platform allows it.
OWASP recommends granting only the tools an agent needs and using per-tool scopes, such as read-only versus write access and resource-specific access. A deny-by-default policy—allowing only listed operations and rejecting the rest—is a practical way to implement those least-privilege principles; it is an implementation recommendation, not a quoted NIST mandate.
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Check every call at a trusted execution boundary
Place authorization in a tool proxy, API gateway, service, or equivalent component that the model cannot bypass. On every call, check the agent identity, requested operation, target resource, applicable policy, and any required approval. Do not treat a model’s plan, a prompt instruction, a classification, or a self-reported confidence score as the final permission check.
The check must cover the exact action and target, not merely whether the agent has access to a tool in general. If a proposed operation is denied, do not let the agent obtain the same effect by switching to another tool or breaking the task into smaller calls that evade the policy.
Match autonomy and approval to the action’s risk
Autonomy is not all-or-nothing. Let an agent handle bounded, low-impact, reversible work within its preapproved scope. Put an independent human checkpoint in front of high-impact, irreversible, administrative, financial, or externally visible actions. These categories are a risk-based design pattern, not a claim that one universal list fits every organization.
OWASP’s AI Agent Security Cheat Sheet and LLM06:2025 Excessive Agency support limiting agency and requiring authorization for sensitive actions. CISA and partner agencies announced Careful Adoption of Agentic Artificial Intelligence Services in 2026, with recommendations that include limiting autonomy, avoiding broad or unrestricted access to sensitive data and critical systems, and using layered defense, identity management, and oversight.
Bind approvals to the proposed action
An approval should identify what will happen and to which target. Make it specific enough that a reviewer can understand the consequence, rather than asking for blanket permission to continue. If the agent changes a material parameter or target after approval, require a fresh policy decision and, where applicable, a new approval.
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Constrain credentials and runtime behavior
Keep grants separate by agent role, task, and resource rather than sharing broad credentials among agents. Use bounded or short-lived credentials where the surrounding identity system supports them; the appropriate credential lifetime depends on the platform and task, so there is no universal duration to prescribe here.
Set operational limits for retries, tool chaining, recursion, duration, and cost. These controls contain runaway loops and reduce the chance that an agent compounds a mistake across a long sequence of otherwise permitted calls. OWASP recommends limits and cautions against unrestricted tool access.
Make consequential activity observable and revocable
For high-risk actions, preserve structured records that let an operator reconstruct what the agent attempted, what policy decided, and what happened. Record at least:
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- Agent identity and accountable owner
- Requested tool, operation, and target resource
- Policy outcome and the applicable approval identity, if approval was required
- Action result, including whether it succeeded or was denied
Monitor for anomalous activity and review access grants when the agent’s task, owner, tools, or integrations change. Protect logs so that recording activity does not expose credentials or sensitive data. Ensure there is a way to revoke or narrow an agent’s access when its purpose ends or its permissions are no longer appropriate.
These controls improve accountability and containment, but the cited guidance is control guidance, not measured evidence that a particular design or product will prevent incidents. No jurisdiction-specific legal duties are established here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the complete action path before and after changes
Test the system as a chain from input through policy check to tool execution, not just the model’s response. Include adversarial cases and confirm that the trusted enforcement point behaves as intended:
- Prompt injection or malicious instructions embedded in documents, web pages, or tool output
- A connector with broader permissions than the agent’s task requires
- A denied call, including attempts to retry through another tool or divide the action into smaller steps
- An approval bypass attempt, such as changing the target or parameters after approval
- A long or recursive chain of individually permitted tool calls
Retest after changes to prompts, tools, memory, retrieval, or providers, since those changes can affect the action path. OWASP recommends structured adversarial testing and retesting after such changes.
Compare safer and weaker permission designs
| Control area | Safer design signal | Weak design signal |
|---|---|---|
| Enforcement location | A trusted tool proxy, API, or service checks the exact action at runtime. | The model is expected to follow a prompt or its own risk score. |
| Permission granularity | Grants are scoped by agent, tool, operation, and resource. | Agents share human credentials or receive broad wildcard access. |
| Approval model | Policy requires approval at a defined high-impact boundary and binds it to the proposed action and target. | The agent decides whether review is needed or can retry through another tool. |
| Identity and accountability | Each agent has a distinct identity and attributable owner, with decisions and actions recorded. | Identity is shared, ownership is unclear, or action records are incomplete. |
| Operational containment | Retries, tool chains, duration, and cost are bounded, and access can be revoked. | Loops are unbounded and broad grants persist. |
| Input handling | External content is treated as untrusted data and cannot change authorization. | Retrieved text or tool output can silently change goals or privileges. |
Use guidance with its scope in mind
OWASP’s AI Agent Security Cheat Sheet is a living web document, and its LLM06:2025 Excessive Agency guidance addresses excessive agent permissions. NIST IR 8596 is identified here as a 2025 initial public draft, not a confirmed current final standard. Microsoft’s current material offers a vendor perspective on implementation controls, rather than a neutral standard. These sources support a general design pattern; they do not prove that a specific product implements it effectively.
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