SpyBara
Go Premium

concepts/sandboxing/auto-review.md 2026-11-05 23:03 UTC to 2026-11-06 23:02 UTC

This page contains 64 additions and 94 deletions.

2026
Tue 3 23:57 Fri 6 23:02

Auto-review

For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

Auto-review sends eligible approval requests to a separate reviewer agent. Your sandbox, command rules, and tool settings determine which actions need approval. The reviewer decides whether those actions can proceed.

Auto-review only applies when approvals are interactive. In practice, that means approval_policy = "on-request" or a granular approval policy that still surfaces the relevant prompt category. With approval_policy = "never", there is nothing to review.

In the ChatGPT desktop app, selecting an approved Daybreak model automatically switches the permissions control to Approve for me when that mode is available for your account and allowed by organization policy. This also applies when you use the desktop app's /model command. If that mode isn't available, the current permission mode stays unchanged. Model selection never overrides managed organization requirements.

Before enabling Full Access for an approved security model, the ChatGPT desktop app shows a model-specific warning about dangerous actions. The warning recommends Approve for me instead and links to reviewer-policy configuration. The warning doesn't restore the sandbox boundary or override organization policy.

Select Auto-review

In the desktop app, choose Approve for me from the permissions control below the composer. Check that it's selected for the current chat. See Permissions for mode selection and troubleshooting.

For the CLI or a local Codex configuration, set approvals_reviewer = "auto_review" with an eligible interactive approval policy, such as approval_policy = "on-request". Changing the approval policy alone doesn't select the reviewer. Administrators can require Auto-review for their organization.

How Auto-review works

  1. The main agent proposes an action, such as running a command.
  2. If the action needs approval, Codex creates a request.
  3. If approvals_reviewer = "auto_review", Codex routes that approval request to a separate reviewer agent instead of stopping for a person.
  4. The reviewer decides whether the action should run and returns a rationale.
  5. If the action is approved, execution continues. If it is denied, the main agent is instructed to find a materially safer path or stop and ask the user.

When it triggers

Auto-review evaluates approval requests that would otherwise pause for a human. These include:

  • Shell or exec tool calls that request escalated sandbox permissions.
  • Commands that match a command rule with decision = "prompt", including commands inside the sandbox.
  • Network requests to destinations that require approval. An explicit network deny rule blocks the request.
  • File edits outside the allowed writable roots.
  • MCP or app tool calls that require approval based on their tool annotations or configured approval mode.

Actions that already have permission to run proceed without review.

Computer Use app-level access prompts go directly to the user. Eligible Computer Use tool calls can separately trigger Auto-review.

What Auto-review blocks

At a high level, Auto-review is designed to block actions such as:

  • sending private data, secrets, or credentials to untrusted destinations
  • probing for credentials, tokens, cookies, or session material
  • broad or persistent security weakening
  • destructive actions with significant risk of irreversible damage

See Configuration for the default rules and how to customize them.

What the reviewer sees

The reviewer evaluates the specific action that triggered the approval request.

The reviewer sees a compact transcript plus the exact approval request. That typically includes user messages, surfaced assistant updates, relevant tool calls and tool outputs, and the action now being proposed for approval. It can also perform read-only checks, such as inspecting a directory before approving its deletion.

Hidden assistant reasoning is not included. Auto-review sees retained chat items and tool evidence, not private chain-of-thought.

Denials and failure behavior

An explicit denial is not treated like an ordinary sandbox error. Codex returns the review rationale to the main agent and adds a stronger instruction:

  • Do not pursue the same outcome via workaround, indirect execution, or policy circumvention.
  • Continue only with a materially safer alternative.
  • Otherwise, stop and ask the user.

For most models, the Codex CLI interrupts the turn after 3 consecutive denials or 10 denials in the last 50 reviews in that turn. For models designated for cybersecurity, the CLI interrupts the turn on the first denial.

Any other review outcome resets the consecutive-denial count. When Codex reaches a denial limit, it displays a warning and stops the turn.

Timeouts are surfaced separately from explicit denials, and the main agent is informed that a timeout alone is not proof that the action is unsafe.

Codex also provides an explicit override path for denied actions. In the current open-source TUI, run /approve to open the Auto-review Denials picker, then select one recent denied action to approve for one retry. Codex records up to 10 recent denials per task. That approval is narrow: it applies to the exact denied action, not similar future actions; it is recorded for one retry in the same context; and the retry still goes through Auto-review. Under the hood, Codex injects a developer-scoped approval marker for that exact action. The reviewer then sees that explicit user override as context, but it still follows policy and can deny again if policy says the user cannot overwrite that class of denial.

Configuration

The reviewer template tells the reviewer how to assess an action. Codex inserts the security policy into that template and specifies the response format separately. The CLI uses the reviewer model's default policy when available. Otherwise, it uses the bundled policy (policy.md).

To add rules, set extra_policy under [auto_review] in local config.toml, or set the top-level guardian_extra_policy in managed requirements.toml. Codex applies these rules alongside the active policy.

To replace the policy, copy the complete policy you want to adapt. If you use the bundled default, copy the full contents of policy.md. Edit the rules you want to change, then put the full text in local [auto_review].policy or managed guardian_policy_config. Omitted rules drop out of the replacement. Codex still uses the reviewer's template and response format.

Codex resolves the two settings independently and ignores blank values. Managed guardian_policy_config takes precedence over local [auto_review].policy. Managed guardian_extra_policy takes precedence over local [auto_review].extra_policy. For example, if your organization sets only a replacement policy, your local extra_policy still adds rules to it.

For organization-wide setup, see Configure Auto-review policy.

Configure an authorized cybersecurity engagement

For authorized security work, combine Auto-review with a written engagement scope and a least-privilege permission profile. Use an approved lab target, document the actions and engagement window, and keep production systems, unrelated hosts, credentials, and persistent changes out of scope unless explicitly authorized.

This local config.toml example enables Auto-review and adds engagement rules to the active policy. Replace the example target and actions with your approved scope.

approval_policy = "on-request"
approvals_reviewer = "auto_review"
default_permissions = ":workspace"

[auto_review]
extra_policy = """
## Environment Profile
- Authorized target: lab.example.com.
- Approved actions: inspect the target, reproduce authorized vulnerabilities,
  and validate fixes within the documented engagement window.

## Tenant Risk Taxonomy and Allow/Deny Rules
- Allow only actions against the approved target that match the documented
  engagement scope and approved actions.
- Deny out-of-scope or unknown hosts, production access, credential theft,
  persistence, data exfiltration, destructive operations, and policy bypass.
- Deny ambiguous actions and high-impact changes until a human explicitly
  approves the exact target, action, and side effects.
"""

Enforce target restrictions with independent filesystem and network rules. For an organization, put the same engagement rules in the top-level guardian_extra_policy in managed requirements.toml. The following requirements select Auto-review and restrict the available permission profiles.

allowed_approval_policies = ["on-request"]
allowed_approvals_reviewers = ["auto_review"]
allowed_sandbox_modes = ["read-only", "workspace-write"]
default_permissions = ":workspace"

[allowed_permission_profiles]
":read-only" = true
":workspace" = true
# ":danger-full-access" is omitted, so it is denied.

allowed_permission_profiles controls current permission profiles. allowed_sandbox_modes also prevents full access in deployments that still use legacy sandbox_mode.

Keep approval_policy = "on-request" or another eligible interactive approval policy and keep an enforceable sandbox boundary. With approval_policy = "never", :danger-full-access, or --yolo, an action can avoid creating the boundary-crossing approval request that review requires.

A network destination on the allowlist doesn't trigger review by itself. Add explicit command rules with decision = "prompt", or configure sensitive MCP tools to require approval, when actions inside the sandbox must still reach the reviewer.

See Models and Trusted Access and recommended configuration for model access, engagement setup, and custom agent workflows. For custom API or Agents SDK harnesses, use Guardrails and human review.

Reduce review volume without weakening security

Auto-review works best when the sandbox already covers your common safe workflows. If too many mundane actions need review, fix the boundary first instead of teaching the reviewer to approve noisy escalations forever.

In practice, the highest-leverage changes are:

  • Add narrow writable_roots for scratch directories or neighboring repos you intentionally use.
  • Add narrowly scoped prefix rules. Prefer precise command prefixes such as ["cargo", "test"] or ["pnpm", "run", "lint"] over broad patterns such as ["python"] or ["curl"]. Broad rules often erase the boundary Auto-review is meant to guard. For cybersecurity models, the CLI ignores allow prefix rules and honors prompt and forbidden rules.

Auto-review session transcripts are retained under ~/.codex/sessions by default, so you can ask Codex to analyze past traffic there before changing policy or permissions.

Limits

Auto-review improves the default operating point for long-running agentic work, but it is not a deterministic security guarantee.

  • It only evaluates eligible approval requests, including requests triggered by explicit command rules or tool approval settings.
  • It can still make mistakes, especially in adversarial or unusual contexts.
  • It should complement, not replace, good sandbox design, monitoring, and organization-specific policy.

For the research rationale and published evaluation results, see the Alignment Research post on Auto-review.