Sandboxing, allowlists, and human approval protect different boundaries, so they work best together. A sandbox limits what code can access in its execution environment; an allowlist restricts which tools or network destinations it can reach; and approval pauses selected actions until a person reviews them. None replaces narrowly scoped permissions, protected credentials, enforcement at the point of action, or audit logging.
How the three controls differ
| Control | Boundary it constrains | Best fit | Key limitation |
|---|---|---|---|
| Sandbox | Compute, filesystem, processes, and execution environment | Running code, manipulating files, or working in a persistent workspace | It does not make every in-sandbox action appropriate. Code can still access credentials and data exposed to that environment. |
| Allowlist | Network destinations or permitted tools | Restricting connectivity to services and tool surfaces the agent needs | A reachable destination does not authorize every request or operation against it. |
| Human approval | A selected action before it executes | High-impact, irreversible, externally visible, financial, or administrative actions | A prompt is weak unless approval is bound to the exact action and verified by the execution component. |
OpenAI describes the sandbox as an execution plane—with its own filesystem, commands, packages, mounts, ports, and state—separate from a trusted harness that handles orchestration, tools, approvals, and recovery. That separation helps limit exposure, but does not make the sandbox itself a complete security boundary for every kind of data or authority. OpenAI’s sandbox-agent guide and sandbox security guidance explain these roles.
What each control can—and cannot—prevent
Sandboxing contains execution resources
Use a sandbox when an agent needs to run generated code, alter files, or maintain a workspace. Restrict its filesystem, processes, packages, mounts, ports, and state to what the task requires. Keep orchestration and long-lived credentials outside an untrusted execution environment where practical: agent-generated code can read credentials made available inside it, including an injected environment key. A narrowly scoped secret broker or proxy can reduce direct exposure, but must itself enforce which credentials and operations the agent may use. See OpenAI’s sandbox security guidance.
Allowlists narrow connectivity
Permit outbound traffic only to the destinations required for the task, including necessary executor hosts. Apply the policy where each connection originates: a local executor and a remote tool may use different network environments. An allowlist blocks unapproved destinations; it does not decide whether a request to an approved service is allowed for this user, agent, or task. Keep authorization checks at the tool or service that performs the operation. OpenAI’s network guidance describes endpoint restrictions.
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Approval gates selected side effects
Use a human review gate for actions whose consequences justify interruption. Examples include sending email, deleting data, executing code with meaningful privileges, changing administrative settings, or transferring funds. These are risk examples, not a universal classification: assess the action’s impact, reversibility, scope, and context. OWASP recommends risk-based autonomy and explicit approval for high-impact or irreversible operations in its AI Agent Security Cheat Sheet.
Approval should occur before the tool runs. The user should see a useful preview, and the approval should apply only to the action shown—not to a later modification. OpenAI documents a workflow that pauses a pending tool call, lets the application approve or reject it, and resumes from saved state. Its guidance emphasizes placing validation next to the tool that creates the side effect: Guardrails and human review.
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How to combine them in an agent system
- Define the authority the task needs. Scope access to the user, data, tools, and operations required. Do not treat a network destination being reachable as permission to use every capability it offers.
- Isolate code execution. Give generated code only the files, processes, packages, mounts, and other resources it needs. Keep orchestration and long-lived secrets out of the execution environment where practical; use a constrained broker or proxy when credentials must be accessed.
- Restrict network and tool access. Allow only necessary endpoints and tools, and enforce the policy in the environment where each connection actually runs.
- Classify actions by risk in application policy. Treat reading or searching differently from writing, sending, deleting, executing, or transferring. Set the thresholds for your system’s users, data, and consequences rather than adopting illustrative categories as universal rules.
- Preview and approve consequential actions. Before execution, show the actor, tool, target, and normalized parameters. Bind approval to those exact details, its time and expiry; reject replayed approvals or changed parameters.
- Enforce at the side-effecting tool. Have the execution component independently check scope, privilege, policy, and valid approval state immediately before acting. Fail closed if risk classification, approval validation, policy lookup, or audit logging fails.
- Log decisions and results. Preserve enough information about approvals, tool use, execution outcomes, and network decisions to investigate what happened and support recovery.
This separation matters in multi-step and multi-agent workflows. An upstream check should not be assumed to cover every later invocation: OpenAI’s guidance notes that input guardrails run only for the first agent, output guardrails only for the final agent, and tool guardrails only for attached function tools. Put checks at each boundary where a tool can cause a side effect. OpenAI’s guardrails documentation describes these limits.
Choose controls by impact, reversibility, and friction
- For code or file work: prioritize a constrained sandbox, then limit credentials and network access available to it.
- For access to external services: allowlist required destinations, but authorize each operation separately at the tool or service boundary.
- For consequential actions: require a preview and action-specific approval, then verify that approval immediately before execution.
- For low-impact, reversible work: automation may be appropriate under narrowly scoped permissions and monitoring; approval for every step can add friction without addressing the underlying authority boundary.
- For high-impact or hard-to-reverse work: use layered restrictions and an explicit approval gate. The more difficult an action is to undo, the less suitable it is to leave solely to model judgment.
There is no established universal winner among these controls. The cited guidance describes different security roles; it does not provide a controlled comparison showing that one control is always more effective. NIST’s COSAiS use cases, on a page updated January 8, 2026, show standards-oriented work mapping familiar SP 800-53 controls to single-agent and multi-agent systems, alongside references to SP 800-218A and draft AI 800-1 resources. This is ongoing control-overlay work, not a complete final agent-security standard.
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Monitor and preserve evidence
Controls are harder to operate and investigate if the system cannot show what was allowed, blocked, approved, or executed. Record policy decisions, approval details, tool calls, execution results, and network proxy decisions in a form that supports incident review. OpenAI’s May 8, 2026 account of its Codex deployment describes constrained execution, network policies, and agent-aware telemetry for approvals, execution results, MCP use, and proxy decisions; it is an operational approach, not a measured comparison of security effectiveness. Read the Codex deployment account.
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