--- 格式版本: 2 标题: "Sandbox | ChatGPT Learn" 原文链接: "https://developers.openai.com/codex/sandboxing" 发布日期: "2026-08-16" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:local:strict_original_body" 发布时间证据: "Published Time: Sun, 16 Aug 2026 07:23:49 GMT" 发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body" 发布时间校准置信度: "high" 发布时间候选数量: 4 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-08-17T00:07:48+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-08-16T23:54:06+08:00" 入库时间: "2026-08-16T16:07:48.263Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://developers.openai.com/blog" 匹配关键词: - "deployment" - "performance" - "latency" - "throughput" - "AI" 相关厂家: - "OpenAI" - "Microsoft" - "AWS" - "Google" - "Oracle" 相关专家: [] 内容类型: "网页" 抓取工具: "Jina Reader" 清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 5 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "该页面为OpenAI开发者文档导航，内容为API、Codex等使用说明，未涉及超节点、AI Rack、机柜级基础设施、供电散热互连等任何相关主题，属于明显无关页面。" AI质检模型: "ali-deepseek-v4-flash" AI质检时间: "2026-08-17T00:07:54+08:00" AI主题相关性: 0 AI来源权威性: 5 AI新颖性: 0 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 5 AI摘要: "该页面为 OpenAI 开发者文档中关于 Codex 沙箱（Sandbox）的说明页，页面仅提供 ChatGPT、Codex、API 等产品文档的导航目录与搜索入口，所给原文未包含沙箱机制、隔离或安全配置的具体正文内容。" AI摘要模型: "ali-deepseek-v4-flash" AI摘要时间: "2026-09-07T03:27:06.208Z" 采集批次: "2026年8月16日20点37分10秒" 采集批次ID: "20260816-203710-016" 去重键: "https://developers.openai.com/codex/sandboxing" --- Title: Sandbox | ChatGPT Learn URL Source: https://developers.openai.com/codex/sandboxing Published Time: Sun, 16 Aug 2026 07:23:49 GMT Markdown Content: For the complete documentation index, see [llms.txt](https://developers.openai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to the page URL. 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[FAQ](https://developers.openai.com/codex/security/faq) ### Cyber safety * [Models & Trusted Access](https://developers.openai.com/codex/cyber-safety) * [Recommended configuration](https://developers.openai.com/codex/cyber-safety/recommended-configuration) ![Image 2](https://developers.openai.com/images/codex/surface-icons/chatgpt-app.webp)ChatGPT desktop app Copy Page # Sandbox How sandboxing works across ChatGPT and Codex clients ![Image 3](https://developers.openai.com/images/codex/surface-icons/chatgpt-app.webp)ChatGPT desktop app Copy Page The sandbox is the boundary that lets the agent act autonomously without giving it unrestricted access to your machine. When a local chat runs commands in the **ChatGPT desktop app**, **Codex CLI**, or **IDE extension**, those commands run inside a constrained environment instead of running with full access by default. That environment defines what the agent can do on its own, such as which files it can modify and whether commands can use the network. When a task stays inside those boundaries, the agent can keep moving without stopping for confirmation. When it needs to go beyond them, the approval flow takes over. Sandboxing and approvals are different controls that work together. The sandbox defines technical boundaries. The approval policy decides when the agent must stop and ask before crossing them. ## What the sandbox does The sandbox applies to spawned commands, not just to built-in file operations. If the agent runs tools like `git`, package managers, or test runners, those commands inherit the same sandbox boundaries. Codex uses platform-native enforcement on each OS. The implementation differs between macOS, Linux, WSL2, and native Windows, but the idea is the same across surfaces: give the agent a bounded place to work so routine tasks can run autonomously inside clear limits. ## Why it matters The sandbox reduces approval fatigue. Instead of asking you to confirm every low-risk command, the agent can read files, make edits, and run routine project commands within the boundary you already approved. It also gives you a clearer trust model for agentic work. You aren’t just trusting the agent’s intentions; you are trusting that the agent is operating inside enforced limits. That makes it easier to let the agent work independently while still knowing when it will stop and ask for help. ## Getting started The default permissions mode applies sandboxing automatically. ### Prerequisites On **macOS**, sandboxing works out of the box using the built-in Seatbelt framework. On **Windows**, Codex uses the native [Windows sandbox](https://developers.openai.com/codex/windows/windows-sandbox#windows-sandbox) when you run in PowerShell and the Linux sandbox implementation when you run in WSL2. On **Linux and WSL2**, install `bubblewrap` with your package manager first: Choose an option Ubuntu/Debian Fedora `sudo apt install bubblewrap` `sudo dnf install bubblewrap` Codex uses the first `bwrap` executable it finds on `PATH`. If no `bwrap` executable is available, Codex falls back to a bundled helper, but that helper requires support for unprivileged user namespace creation. Installing the distribution package that provides `bwrap` keeps this setup reliable. Codex surfaces a startup warning when `bwrap` is missing or when the helper can’t create the needed user namespace. On distributions that restrict this AppArmor setting, prefer loading the `bwrap` AppArmor profile so `bwrap` can keep working without disabling the restriction globally. **Ubuntu AppArmor note:** On Ubuntu 25.04, installing `bubblewrap` from Ubuntu’s package repository should work without extra AppArmor setup. The `bwrap-userns-restrict` profile ships in the `apparmor` package at `/etc/apparmor.d/bwrap-userns-restrict`. On Ubuntu 24.04, Codex may still warn that it can’t create the needed user namespace after `bubblewrap` is installed. Copy and load the extra profile: ``` sudo apt update sudo apt install apparmor-profiles apparmor-utils sudo install -m 0644 \ /usr/share/apparmor/extra-profiles/bwrap-userns-restrict \ /etc/apparmor.d/bwrap-userns-restrict sudo apparmor_parser -r /etc/apparmor.d/bwrap-userns-restrict ``` `apparmor_parser -r` loads the profile into the kernel without a reboot. You can also reload all AppArmor profiles: `sudo systemctl reload apparmor.service` If that profile is unavailable or does not resolve the issue, you can disable the AppArmor unprivileged user namespace restriction with: `sudo sysctl -w kernel.apparmor_restrict_unprivileged_userns=0` ## How permissions work Use the permissions control for your surface to change how Codex handles local actions. Approvals determine when Codex pauses before an action, while the sandbox determines which files and network resources commands can access. When an approval offers different scopes, such as approving once or for the session, choose the narrowest scope that lets the task continue. Keep the project boundary as the default; use separate projects or worktrees instead of broadening access across unrelated repositories. ChatGPT Work runs code and shell commands in a managed, isolated environment. Workspace policy and tool-specific controls determine which capabilities are available. When the setting is available, use **Settings > Data controls > Work network access** to manage network access for code and shell commands. Turn on **Allow public internet access** to let those commands reach the public internet. When it’s off, commands can reach only required hostnames from a managed allowlist. Web search, plugins, and the remote browser have separate controls. Changes take effect after the current code or shell run finishes and Work refreshes its execution environment. ChatGPT web doesn’t expose the local Codex sandbox or approval-mode selector. In the ChatGPT desktop app, use the permissions control beneath the composer. Depending on your configuration, the menu can include **Ask for approval**, **Approve for me** for eligible approval requests, **Full access**, and named or custom permissions profiles. Ask Codex anything. Ask for approval Ask for approval Approve for me Full access Custom (config.toml) 5.6 Sol Medium #### Ask for approval Codex can read and edit files in the current workspace and run routine local commands. It asks before using the internet or going beyond the workspace boundary. Sandbox`workspace-write` Approvals policy`on-request` Reviewer`user` In the CLI, enter [`/permissions`](https://developers.openai.com/codex/developer-commands?surface=cli#cli-update-permissions-with-permissions) to open the permissions picker and change the active permissions profile. In the IDE extension, use the permissions control beneath the composer. Depending on your configuration, the menu can include **Ask for approval**, **Approve for me** for eligible approval requests, **Full access**, and named or custom permissions profiles. ![Image 4: Codex approval mode selector in the IDE extension](https://developers.openai.com/images/codex/ide/approval_mode.png) [](https://developers.openai.com/codex/sandboxing) ## Configure defaults To start with the same behavior every time, set defaults in `config.toml`. [Config basics](https://developers.openai.com/codex/config-file/config-basic) explains how it works, and the [Configuration reference](https://developers.openai.com/codex/config-file/config-reference) documents the exact keys for `sandbox_mode`, `approval_policy`, `approvals_reviewer`, and `sandbox_workspace_write.writable_roots`. Use those settings to decide how much autonomy the agent gets by default, which directories it can write to, when it should pause for approval, and who reviews eligible approval requests. At a high level, the common sandbox modes are: * `read-only`: The agent can inspect files, but it can’t edit files or run commands without approval. * `workspace-write`: The agent can read files, edit within the workspace, and run routine local commands inside that boundary. This is the default low-friction mode for local work. * `danger-full-access`: The agent runs without sandbox restrictions. This removes the filesystem and network boundaries and should be used only when you want the agent to act with full access. The common approval policies are: * `untrusted`: The agent asks before running commands that aren’t in its trusted set. * `on-request`: The agent works inside the sandbox by default and asks when it needs to go beyond that boundary. * `never`: The agent doesn’t stop for approval prompts. When approvals are interactive, you can also choose who reviews them with `approvals_reviewer`: * `user`: approval prompts surface to the user. This is the default. * `auto_review`: eligible approval prompts go to a reviewer agent (see [automatic review](https://developers.openai.com/codex/sandboxing/auto-review)). Full access means using `sandbox_mode = "danger-full-access"` together with `approval_policy = "never"`. By contrast, the lower-risk local automation preset is `sandbox_mode = "workspace-write"` together with `approval_policy = "on-request"`, or the matching CLI flags `--sandbox workspace-write --ask-for-approval on-request`. You can then keep `approvals_reviewer = "user"` for manual approvals or set `approvals_reviewer = "auto_review"` for automatic approval review. If you need the agent to work across more than one directory, writable roots let you extend the places it can modify without removing the sandbox entirely. If you need a broader or narrower trust boundary, adjust the default sandbox mode and approval policy instead of relying on one-off exceptions. When a workflow needs a specific exception, use [rules](https://developers.openai.com/codex/agent-configuration/rules). Rules let you allow, prompt, or forbid command prefixes outside the sandbox, which is often a better fit than broadly expanding access. For IDE-specific settings entry points, see [Codex IDE extension settings](https://developers.openai.com/codex/developer-settings?surface=ide). Automatic review, when available, doesn’t change the sandbox boundary. It’s one possible `approvals_reviewer` for approval requests at that boundary, such as sandbox escalations, blocked network access, or side-effecting tool calls that still need approval. Actions already allowed inside the sandbox run without extra review. For the reviewer lifecycle, trigger types, denial semantics, and configuration details, see [automatic review](https://developers.openai.com/codex/sandboxing/auto-review). Platform details live in the platform-specific docs. For native Windows setup, behavior, and troubleshooting, see [Windows](https://developers.openai.com/codex/windows/windows-sandbox). For admin requirements and organization-level constraints on sandboxing and approvals, see [Agent approvals & security](https://developers.openai.com/codex/agent-approvals-security). [Previous Profiles](https://developers.openai.com/codex/permissions)[Next Auto-review](https://developers.openai.com/codex/sandboxing/auto-review) Ask AI ## Docs agent Loading docs agent...