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Keep an AI agent within a narrow, independently enforced set of permissions, isolate the environment where it acts, validate proposed actions outside the model, and require informed human approval for consequential operations. No prompt, sandbox, approval dialog, or classifier makes an agent safe by itself: security depends on how these controls work together.
Why AI agents need more than a good prompt
An AI agent combines a model’s decisions with tools, external content, and often multiple steps of execution. It may read a web page, email, or document and then use a connected tool to act on what it found. That content can contain instructions intended to manipulate the agent, even though the user never gave those instructions directly.
OpenAI describes this kind of manipulation as prompt injection and compares it to phishing: a third party tries to mislead the model through material that enters its context. The analogy has a key difference: an agent may retrieve the misleading material itself while browsing or processing a file. The risk is therefore not just whether the model recognizes a hostile instruction. It is also what authority the agent has if it follows one.
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Anthropic’s response to NIST on agentic security describes four interacting layers: model capability, available tools, the orchestration harness, and the execution environment. Anthropic’s concise formulation is: “The failure is identical. The consequences are not.” The same mistaken or manipulated model decision can have very different effects depending on what its tools allow and what the environment prevents.
Build safety as a set of independent controls
Think of agent safety as a system property, not a model setting. A useful design limits authority before the task begins, contains execution, checks actions before they take effect, and leaves enough evidence to investigate what happened. Each layer should remain meaningful even if another layer fails.
Limit permissions to the task
Give the agent only the tools, data, resources, and operations needed for its assigned task. Scope access by both resource and operation: for example, an agent that summarizes messages may need read access but not permission to send, delete, or forward them. Keep tools with different trust levels separate, and do not expose an account or connector the task does not require.
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Prefer a defined task boundary over broad instructions that let an agent act on arbitrary email or web content. OpenAI cites logged-out mode as one example of reducing access when an account is unnecessary. Least privilege does not prevent every bad decision; it limits what a bad decision can reach.
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Contain execution with a sandbox
A sandbox sets technical limits on where code can run and what it can affect. Depending on the deployment, those limits can cover filesystem access, protected paths, network access, and isolation from credentials or sensitive systems. The execution environment—not the model’s promise to behave—must enforce them.
Sandboxing reduces the consequences of an unsafe or manipulated action; it does not establish that the agent’s reasoning is correct. OpenAI’s guidance on running Codex safely treats sandboxing and approval as distinct controls: the sandbox constrains execution, while approval governs whether a person authorizes particular actions. Neither substitutes for the other.
Keep authorization checks outside the model
The model can propose an action, but a separate policy or execution component should check whether the action is permitted, within scope, and properly approved before carrying it out. Do not rely solely on the model to interpret a policy or to decide that its own tool call is safe. OpenAI’s API guidance for sensitive cybersecurity workflows recommends checking tool calls against approved scope, denying unauthorized actions, and failing closed if review is unavailable.
For the same reason, a system should not treat a classifier’s judgment that an action is low-risk as authorization. A component that enforces permissions still needs to verify that the actor may perform the operation on the target resource.
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When to require human approval
Reserve human review for actions whose consequences merit a person’s attention: sensitive, ambiguous, high-impact, destructive, financial, administrative, or externally visible operations. Examples include changing access permissions, deleting important data, sending a consequential message, or making a security-sensitive change. The exact threshold depends on the task and the potential harm.
Approval should be tied to a specific proposed action, not used as a general “let the agent proceed” switch. OWASP recommends binding approval to the actor, tool, target resource, normalized parameters, timestamp, and expiry; for irreversible operations, it also recommends replay protection. This helps ensure that approval for one action cannot be reused for a materially different one.
Show reviewers what they are approving
A useful review presents enough information to assess the action and its consequences, including:
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- Which tool will perform it and which resource it will affect.
- The parameters or content that will be sent, changed, or deleted.
- Relevant context for the request and any applicable policy result.
Approval that hides the target or meaningful parameters is not informed review. The system should also bind the reviewer’s decision to the exact action and reject an expired, changed, or replayed approval.
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Avoid approval fatigue
OpenAI warns that frequent, poorly explained prompts can lead people to approve without understanding or to broaden permissions just to avoid interruptions. Make approvals selective and meaningful. Low-risk actions can proceed within well-defined technical boundaries; synchronous review is most useful when a person can understand the proposed action and its consequences.
Validate untrusted content and proposed actions
Treat retrieved web pages, email, documents, and tool outputs as untrusted input, even when the agent needs them to complete a legitimate task. Where possible, extract only the structured fields the task requires, then validate those fields before they can influence a tool call. Structured extraction narrows what can pass through; it does not make the source trustworthy.
Use guardrails as an initial layer rather than a complete defense. OpenAI’s agent safety guidance recommends combining them with structured extraction, approvals, and evaluation. A separate enforcement component should check action scope, privilege, and approval status immediately before sensitive execution.
Monitor actions and keep useful evidence
Record enough of the agent’s activity to understand and investigate a decision: the user request, proposed and executed tool actions, approval decisions, tool results, and relevant policy outcomes. Monitoring can reveal unexpected behavior and show where controls need adjustment; evaluation and trace review can help test and improve those controls.
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Agent traces may contain sensitive information, so logs need appropriate privacy protections and access controls. Auditability is not a reason to collect everything without restriction: retain the evidence needed for oversight while limiting who can see it and how it is used.
What published agent-safety evaluations can—and cannot—show
In a 2026 report, OpenAI Alignment Research described Auto-review, a system intended to review agent actions without synchronous human oversight. The report gave these results for its specific internal deployment and evaluation design:
| Measure | Reported result and scope |
|---|---|
| Auto-approval of escalated actions | 99.1% on internal Codex sandbox-escalation traffic. |
| Effective approval rate | 99.93% across all actions in the internal Codex deployment. |
| Recall on overeagerness cases | 90.3% on synthetic cases. |
| Recall on prompt-injection cases | 99.3% on synthetic cases in the reported remote-code-execution, secret-exfiltration, and external-upload categories. |
| Recall on MonitoringBench Hard | 96.1% under the report’s criterion that any rejected action in a trajectory counts. |
These are publisher-reported results, not independent validation or a guarantee for other agents. The report says its usability measure used a sample of internal Codex trajectories from March 2026, and its safety evaluation relied on synthetic datasets as well as a benchmark. OpenAI also cautions that Auto-review can be misled and does not provide deterministic security guarantees. Such results are evidence about the tested system and criteria—not proof that an agent will be safe in a different deployment or against every attack.
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Use a practical deployment checklist
- Define the task boundary: specify what the agent may do and what it must not do.
- Minimize authority: expose only necessary tools, data, resources, and operations; prefer read-only access when writing is not required.
- Set execution limits: define filesystem, network, protected-path, and credential boundaries appropriate to the task.
- Enforce policy independently: check scope and authorization outside the model before actions take effect.
- Choose approval thresholds: require review for consequential actions and bind each approval to the exact proposed operation.
- Handle external content defensively: treat retrieved material and tool outputs as untrusted; validate structured fields before use.
- Plan for review failure: for sensitive workflows, decide whether the system must stop when approval is unavailable; OpenAI recommends failing closed for sensitive cybersecurity actions.
- Test and inspect: evaluate relevant adversarial cases, review traces, and watch for false blocks, unnecessary friction, or habitual overrides.
- Protect the audit trail: log requests, actions, decisions, and results with suitable privacy and access controls.
No single architecture is universally safest. Compare deployments by permission scope, execution boundaries, approval integrity, input and tool validation, visibility, and evaluation evidence. OWASP’s practices are guidance, not a product certification, and Anthropic notes that agent architectures and deployment patterns continue to evolve.
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