Sandboxing, allowlists, and human approval protect different boundaries, so they work best together rather than as alternatives. A sandbox limits what code can access; an allowlist limits where an agent can connect; and approval pauses selected actions for review before they happen. None replaces narrowly scoped permissions, protected credentials, monitoring, or enforcement at the tool that causes a side effect.
What each control protects
| Control | Boundary it constrains | Useful for | What it does not guarantee |
|---|---|---|---|
| Sandbox | Compute, filesystem, processes, and execution environment | Running generated code, manipulating files, or providing a workspace | It cannot make every in-sandbox action appropriate. Code can access credentials and data available inside the environment. |
| Allowlist | Network destinations or permitted tools | Restricting connectivity to services the agent actually needs | A reachable destination does not mean every request to it is authorized or safe. |
| Human approval | A selected action before execution | High-impact, irreversible, externally visible, financial, or administrative actions | A prompt alone is weak if approval is not tied to the precise action and verified by the component executing it. |
OpenAI’s sandbox security guidance puts the risk plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” The practical question is therefore not simply whether an agent is sandboxed, but which resources it can reach from inside the sandbox and what other controls govern them.
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How to choose controls for an action
Start with the authority the action needs and the consequence if it goes wrong. Read-only search may need little interruption; deleting records, sending email, executing code, or transferring funds deserves tighter controls. OWASP presents these as illustrative risk categories, not a universal taxonomy.
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- Constrain execution: Use a sandbox when the agent runs code or handles files. Separate untrusted execution from trusted orchestration where practical.
- Constrain destinations: Allow only required network endpoints and tool surfaces. Account for where each connection originates: local executor tools and remote tools may connect from different environments.
- Gate consequential side effects: Require explicit approval for actions with high impact or poor reversibility, and make the approval specific to the action being proposed.
- Enforce authorization independently: Do not rely on the model to decide whether its own action is permitted. Validate at the side-effecting tool boundary.
There is no sourced universal winner among these controls. Choose according to the threatened boundary, action impact and reversibility, credential exposure, audit needs, and the cost of interrupting a user.
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Design a sandbox without handing it unnecessary authority
OpenAI describes sandboxing as an execution plane—filesystem, commands, packages, mounts, ports, and state—separate from a trusted harness responsible for orchestration, tools, approvals, and recovery. Keeping that separation helps limit what generated code can affect if it behaves unexpectedly.
- Keep orchestration and long-lived credentials outside the untrusted execution environment where practical.
- Do not inject broadly privileged secrets into the sandbox. Generated code can read credentials available to its environment, including an injected environment key. Where suitable, use an external secret broker or proxy and expose only narrowly scoped access.
- Isolate workloads that should not share data, and grant only the filesystem, processes, packages, mounts, and ports needed for the task.
- Remember that a sandbox limits the environment, not the legitimacy of every operation performed within it. Accessible data and credentials remain exposed to code running there.
See OpenAI’s Sandbox Agents guide for the execution-plane model and sandbox security guidance for environment and network considerations.
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Make allowlists narrow and meaningful
An allowlist should express which destinations or tool surfaces are necessary, not merely which ones are convenient. OpenAI advises: “Allow outbound traffic only to approved endpoints, including the executor’s required hosts.” That policy reduces reachable services, but it does not establish permission for every operation against an approved service; application authorization still needs to check the actor, target, and requested operation.
- List only destinations required for the task and keep the policy aligned with where the relevant tool connection runs.
- Consider tool access and network access separately: permission to invoke a tool is not necessarily the same as permission for every network request it can make.
- Pair the network boundary with service-side authorization and scoped credentials. A permitted endpoint should not become a route to broader privileges than the agent needs.
Make approval a real execution gate
For a consequential action, approval should occur before the tool runs—not after the agent has already caused the side effect. OpenAI’s documented interruption workflow pauses a pending tool call, lets the application approve or reject it, then resumes from saved state. OWASP likewise recommends explicit approval for high-impact or irreversible operations.
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- Classify the proposed action. Apply application policy to the tool, target, operation, and parameters; do not leave risk classification solely to model judgment.
- Show a useful preview. Present the exact intended change—such as recipient and message, records to delete, or amount and destination—so the reviewer can assess its consequence.
- Bind approval to that proposal. Associate it with the actor, tool, target, normalized parameters, time, and an expiry. Reject replayed approvals or any call whose parameters changed after review.
- Validate at execution time. The component performing the action should independently confirm scope, privilege, and approval state before allowing the side effect.
- Record the decision and outcome. Preserve enough information to investigate what was requested, approved or rejected, and executed.
OpenAI’s guidance states: “Put validation next to the tool that creates the side effect.” OWASP’s AI Agent Security Cheat Sheet similarly emphasizes independent enforcement, action previews, audit trails, and interruption or rollback. If risk classification, approval validation, policy lookup, or audit logging fails, fail closed for the consequential action rather than executing without the control.
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In a multi-step workflow, a check on one agent does not automatically cover every later call. OpenAI documents that input guardrails run only for the first agent, output guardrails only for the final agent, and tool guardrails only on attached function tools. Put authorization, validation, and approval checks at each tool boundary that can create a side effect; do not assume an upstream guardrail protects a downstream operation.
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Preserve operational evidence
Security controls are more useful when a team can determine what happened and why. Record policy decisions and execution outcomes, including approvals, tool results, and relevant network decisions. OpenAI’s account of its Codex deployment describes agent-aware telemetry for tool approvals, execution results, MCP use, and network-proxy decisions; it is an operational example, not a controlled comparison of security effectiveness.
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NIST’s Computer Security Resource Center lists COSAiS use cases for single-agent and multi-agent systems and describes adapting or supplementing SP 800-53 controls for AI-related applications. The page, updated January 8, 2026, also points to SP 800-218A and draft AI 800-1 resources. This is ongoing standards-oriented work, not a complete final agent-security standard. See NIST’s COSAiS use cases.
For an operational example of layered controls, see OpenAI’s May 8, 2026 article, “Running Codex safely at OpenAI”. It describes a deployment approach involving constrained execution, network policies, and agent-aware telemetry; it does not establish a measured risk reduction or prove one control universally superior.
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