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Secure an AI agent at runtime by enforcing permissions in the systems that execute its tools and access data—not by relying on the model to obey instructions. Give each task only the capabilities it needs, validate every request at the tool and downstream-service boundaries, isolate execution, and require independently verified approval for consequential actions.
This matters because an agent may encounter adversarial instructions in user prompts, documents, websites, email, retrieved content, or tool results. A model’s decision that an action seems appropriate is not proof that the actor is authorized to perform it. OWASP recommends placing authorization in downstream systems rather than relying on the model to decide whether an action is allowed (OWASP LLM06:2025 Excessive Agency).
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What should runtime security enforce?
Treat the agent as an untrusted decision-maker operating inside a system of trusted controls. The model can propose an action; the runtime and the service carrying it out must decide whether that action is permitted. A robust design checks the authenticated actor, operation, target resource, scope, and any required approval on each call.
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Use defense in depth: a narrow tool interface reduces what the agent can request, an executor enforces policy before acting, and the downstream application checks authorization again. If one layer is misconfigured or manipulated, another can still reject the request. OWASP’s guidance for agent security and excessive agency supports this layered approach (AI Agent Security Cheat Sheet; LLM06:2025 Excessive Agency).
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How do you define the agent’s trust boundary?
Start by writing down what the agent is supposed to do and what it must not do. This gives engineers and operators a concrete policy to enforce rather than a vague instruction such as “be careful.”
- Task: State the intended job and the operations needed to complete it.
- Data: Identify which data classes the task may read or change, and which are out of scope.
- Targets: Specify the permitted accounts, records, services, destinations, or environments.
- Prohibited actions: List actions the agent must never perform, including access outside the task or unauthorized disclosure.
- Untrusted inputs: Treat user-supplied content, retrieved documents, websites, email, and tool outputs as data—not as trusted instructions.
That last distinction helps contain indirect prompt injection: content encountered during a task may attempt to redirect the agent or induce it to misuse its tools. Instructions in the model context can help express the intended task, but they are not a security boundary; enforce the boundary in the executor and the services behind it.
How should you limit tools and permissions?
Expose only task-specific capabilities
Give the agent the smallest tool surface that can accomplish its assigned job. Prefer a narrowly defined operation—such as searching approved records or updating one named field—over a broad plugin or generic command interface with unrelated powers. Remove obsolete tools, and do not expose a capability merely because the framework makes it easy to add. OWASP specifically advises minimizing extensions and avoiding open-ended extensions (LLM06:2025 Excessive Agency).
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Separate operations with different risks
Read access and write access should not be bundled when the task needs only one. Likewise, sending, deleting, administering, and searching should be distinct capabilities when they require different authorization or approval. A tool’s name or description does not constrain what it can do; its implementation must enforce the operation boundary.
Scope identity and downstream access
When an agent acts for a user, use that user’s delegated authorization where practical rather than a shared, broadly privileged account. Keep API and database privileges limited to the data and actions required for the task. Validate the actor, requested operation, target, and scope at the executor and again at the downstream service. A request that passes the model’s own checks must still fail if either enforcement point denies it.
How do you make tool calls safe to execute?
Validate every parameter
Define typed parameters with appropriate length and pattern limits, and reject values outside explicit allowlists. Check that a requested resource is one the actor may access; syntactically valid input is not necessarily authorized input. Avoid letting the model construct unrestricted URLs, queries, file paths, or commands when a constrained operation can perform the same task.
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Isolate code and command execution
If an agent must execute code or commands, run them in a sandbox or container under a low-privilege identity. Deny host filesystem, process, credential, and network access by default, then allow only specific resources needed for the task. Isolation is not a substitute for input validation or authorization: a weakly validated request combined with a permeable sandbox and excessive privileges can still expose sensitive resources. OWASP Cornucopia identifies these risks in Agentic AI AAI8.
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Which actions need human approval?
Classify actions by impact and reversibility before execution. Require human approval for actions that are destructive, financial, administrative, externally visible, or security-sensitive—such as changing permissions or infrastructure. Routine, reversible actions within an explicitly authorized scope may not need the same step-up control.
Approval must be a runtime control, not merely a message in the conversation. Bind it to the exact action and parameters so it cannot authorize a materially different request; make it expire; prevent replay; and have the execution component validate it independently before acting. If the requested action changes permissions or configuration, apply established change-management controls and explicit operator approval. OWASP’s AAI9 guidance says agents should be subject to the change-management controls used for human administrators, with additional guardrails for autonomous operation (Agentic AI AAI9).
How should you protect data, memory, and logs?
Minimize what enters context
Include only the data needed for the current task. Scope memory and retrieval by user or session so one person’s context is not silently available to another. Treat retrieved material as untrusted input even when it comes from a familiar source.
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Keep structured records of tool invocations and downstream outcomes so operators can investigate what happened. Redact credentials and sensitive information rather than copying them into ordinary logs. Monitoring should make activity and policy decisions observable without creating a second store of exposed data. OWASP’s agent-security guidance addresses data exposure, logging, and monitoring controls (AI Agent Security Cheat Sheet).
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Limit the damage from repeated or abnormal activity
Use monitoring and rate limits to help detect or constrain unexpected tool activity. These controls complement authorization: a rate limit may reduce the speed or scale of misuse, but it does not make an unauthorized action permissible.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you test before deployment?
Test the actual execution path, including the executor and downstream service—not just whether the model gives an appropriate answer. Include denial cases and verify that a confident or persuasive model request is still rejected when policy disallows it. OWASP recommends testing agent security controls and monitoring behavior (AI Agent Security Cheat Sheet).
- Try prompt overrides and indirect instructions embedded in retrieved or user-provided content.
- Request tools or operations the task does not authorize, including access to another user’s data.
- Attempt privilege escalation, data exfiltration, and access beyond the intended target or scope.
- Test poisoned memory, approval bypass, expired or replayed approvals, and changes to approved parameters.
- Exercise recursive or repeated tool use and confirm monitoring and rate limits behave as intended.
Keep these cases as regression tests and rerun them after material changes to prompts, tools, memory, retrieval, policies, or model providers. A control that worked for one version of the execution path should not be assumed to work after that path changes.
How should you evaluate a runtime security design?
When comparing an architecture or implementation, examine where decisions are enforced and whether those checks can be verified. Framework middleware, a separate policy service, the tool executor, and the downstream application may all have roles; a single layer should not be assumed to provide complete protection.
- Enforcement: Which components validate identity, operation, target, scope, and approval? Do downstream services enforce their own authorization?
- Identity: Does the agent use per-user delegated access or a shared service identity? What scope does it receive, and how long do credentials remain valid?
- Tool design: Are capabilities narrow and constrained, or can the agent invoke an open-ended shell, URL, or API?
- Isolation: What process or container boundaries apply? What filesystem, network, host, and secret access is denied or allowed?
- Approval handling: Is approval bound to the exact request, time-limited, protected against replay, and checked fail-closed at execution?
- Operations: Can teams audit invocations and outcomes, alert on abnormal activity, and maintain the policy without exposing secrets?
- Verification: Can denial, adversarial-input, and regression cases be exercised in CI/CD?
These are practical evaluation dimensions drawn from OWASP’s control recommendations, not a published vendor scoring system. Assess the behavior of the actual implementation rather than assuming that a framework feature or policy description guarantees enforcement.
What does OWASP’s Agent Control Standard establish?
OWASP’s Agent Control Standard page, dated September 1, 2026, describes middleware hooks and declarative safety-policy enforcement intended to be portable across agent frameworks. The page states a direction centered on inspectability, traceability, runtime control, and visibility into what agents can access and do (Agent Control Standard (ACS)). That stated purpose is not, by itself, evidence that every framework supports the standard or that a particular implementation enforces its controls; verify support and behavior for the system you operate.
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