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Settings names and availability can vary by operating system and Codex surface. The configuration guide is the authoritative place to check current behavior; it documents configuration locations and precedence, but does not establish one universal desktop-app menu path. OpenAI Codex configuration reference
Start by checking which configuration actually applies
Codex settings can come from more than a single preferences panel. OpenAI documents user and project configuration alongside profiles, managed defaults, system configuration, command-line overrides, and built-in defaults. Organization requirements may further constrain the values a user can select. A visible preference is therefore not necessarily the effective setting for a particular run.
User-level settings are stored in ~/.codex/config.toml. Project or subfolder settings can be stored in .codex/config.toml, but project-scoped configuration applies only to trusted projects. The configuration guide documents the IDE extension route as gear icon > Codex Settings > Open config.toml; that route should not be assumed to describe every desktop app version or operating system.
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Check project trust before relying on repository settings
When a project is untrusted, Codex skips project-scoped .codex/ configuration, including project-local settings, hooks, and rules. User and system configuration still load. Trust therefore determines whether repository-provided Codex instructions and automation are loaded; it is not itself a substitute for choosing an appropriate approval policy or sandbox.
Account for managed and higher-authority settings
In a managed organization, requirements can restrict or override local choices. Ask your administrator which policies apply if Codex is used in a company or school environment. OpenAI describes managed configuration across desktop, CLI, and IDE local surfaces in its security guidance, but deployment details and available controls can differ. OpenAI: Running Codex safely at OpenAI
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Review approval policy and sandbox separately
Approval policy determines when Codex must pause and ask before executing a generated command or taking an action. The sandbox determines the technical boundary for command execution: what paths can be read or written and whether commands can reach the network. As OpenAI puts it, “Approvals and sandboxing work together.” An approval is not a filesystem restriction: once a command is allowed to run, the sandbox is the control that bounds its access.
Approval policy: decide when you want a checkpoint
The configuration guide documents approval_policy = "on-request" as a common choice and explains the behavior difference between on-request and never. Review the current policy and understand when Codex will ask before proceeding. Do not treat fewer prompts as stronger protection, or assume that a prompt policy alone limits an approved command’s capabilities.
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Sandbox mode: decide what commands can do
OpenAI lists three built-in permission profiles: read-only, workspace-write, and danger-full-access. In practical terms, compare them by the files Codex can read, where it can write, and whether command execution can access external services. Choose a profile based on the work required; broader access can make tasks easier but expands the execution boundary.
Implementation details are not identical across operating systems. OpenAI recommends elevated Windows sandbox mode for native Windows use, with unelevated mode as a fallback if administrative permissions are unavailable or setup fails. Check the current platform-specific documentation rather than assuming a setting has identical effects on macOS, Linux, and Windows.
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Inspect file access and network access
Know what the active sandbox can read and change
OpenAI’s Windows engineering account says Codex runs with a real user’s permissions by default, then describes sandbox constraints; the same article characterizes the default approach as permitting broad reads while restricting writes to the workspace. The exact behavior depends on platform and sandbox implementation, so verify the active profile and protected paths for the environment in which you will run Codex. OpenAI: Building a safe, effective sandbox to enable Codex on Windows
Treat command networking and web search as distinct controls
Command network access is a sandbox boundary. Allowing it may be useful for downloading dependencies or other workflows, but it can also increase exposure to prompt injection, credential leakage, and code with license restrictions. Decide whether the task genuinely requires network access, and review that permission independently of approval behavior.
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Web search has a separately documented setting: cached (the default), indexed, live, or disabled. Do not assume that choosing a web-search mode determines whether commands can reach the network; these are related security considerations, not the same control. OpenAI Codex configuration reference
Choose a restrictive baseline when broad access is unnecessary
OpenAI Help Center guidance identifies sandbox_mode = "read-only" with approval_policy = "on-request" as a restrictive alternative to the retired untrusted approval policy. The article says that untrusted approval policy is no longer supported in specified recent versions; do not confuse it with project-level trust_level = "untrusted", which that guidance says remains supported. Check the current version guidance before depending on behavior that may be version-specific. OpenAI Help Center: Using Codex with your ChatGPT plan
This baseline is a practical starting point when Codex needs to inspect a repository but does not need to modify files or use the network. If the task requires edits or external access, widen only the relevant capability and retain the approval checkpoints appropriate to your workflow.
Pre-access review checklist
- Identify the effective configuration: account for user, trusted-project, managed/system, profile, built-in, and command-line layers.
- Confirm project trust: know whether project-local
.codex/settings, hooks, and rules will load. - Check approval behavior: understand when Codex asks before executing a command or action.
- Check filesystem scope: confirm what can be read and where writes are permitted under the active sandbox.
- Check network permission separately: decide whether commands need external access, and review web-search mode independently.
- Verify organizational policy: ask an administrator about managed requirements if the device or account is organization-managed.
- Use platform-specific guidance: confirm current controls for the operating system and app, CLI, or IDE surface you will use.
For eligible enterprise and education customers, OpenAI describes OpenTelemetry events and Compliance Platform activity logs as audit options. That description concerns OpenAI’s own deployment and does not establish identical logging setup for every user. OpenAI: Running Codex safely at OpenAI
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