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Give an AI coding agent the smallest workspace, tool set, network reach, and credential set that can complete its task, then require narrow, contextual approval whenever it needs to cross one of those boundaries. The sandbox is the enforcement boundary; an approval dialog is a decision point, not a substitute for isolation. This layered design reduces blast radius while keeping routine edits fast.
The controls below apply whether the agent runs in a local IDE, a command-line session, a hosted cloud worker, or a self-hosted environment. Product labels and configuration syntax differ, so treat the examples as policy patterns and verify the current documentation for your agent and operating system.
Start with two separate controls: sandbox and approval policy
A sandbox answers what the agent can technically reach: filesystem roots, protected paths, network egress, processes, and credentials. An approval policy answers when the agent must stop and ask, such as when it requests a path outside the workspace or an unfamiliar network destination. OpenAI describes these as complementary controls in its guidance on running coding agents safely: sandboxing and approvals work together.
| Control | What it enforces | What it cannot do alone |
|---|---|---|
| Filesystem sandbox | Limits readable and writable roots and preserves protected paths. | It does not decide whether a legitimate in-scope edit is safe to merge. |
| Network policy | Blocks or permits outbound destinations, usually with an allowlist or proxy. | It does not validate the content returned by an allowed site. |
| Tool grants | Scopes shell, MCP, browser, connector, desktop, and application tools individually. | It does not make a dangerous parameter safe merely because the tool is approved. |
| Approval gate | Pauses before a defined action, expansion, or external effect and records a human decision. | It is not a boundary if the agent already has unrestricted access or if users approve blindly. |
| Review and audit | Provides diff, test, tool, result, and approval history for investigation. | It cannot undo a destructive action that was never blocked. |
Design the sandbox first. Then add approvals for exceptions and consequential actions. If you reverse that order, a user can approve an operation whose technical blast radius is much larger than intended.
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1. Inventory the agent’s real access
Write down the access that exists today, not only the permissions shown in the agent’s chat panel. Include indirect paths such as shell utilities, inherited environment variables, mounted volumes, browser sessions, and desktop control.
- Filesystem: record the starting directory, every mounted root, writable directories, protected files, temporary directories, and whether symlinks can leave the workspace.
- Commands and processes: list interpreters, package managers, build tools, compilers, container commands, version-control commands, and process-management capabilities.
- Network: identify DNS, HTTP, package registries, source-control hosts, artifact stores, cloud APIs, browser URLs, and proxy routes the agent can reach.
- Integrations: enumerate MCP servers, IDE tools, issue trackers, deployment connectors, database tools, browser automation, and desktop or computer-use features.
- Credentials: locate environment variables, credential files, SSH agents, cloud metadata endpoints, OAuth sessions, signing keys, and tokens exposed through mounted home directories.
- External effects: mark actions that can merge code, publish packages, rotate infrastructure, send messages, alter tickets, spend money, or touch production systems.
GitHub notes that its Copilot CLI defaults to the directory where it starts, while optional computer use can interact with desktop applications outside that directory. That distinction means a directory setting is not a complete defense when desktop or browser control is enabled; see the GitHub application card for Copilot Agents.
2. Choose an isolation boundary for each workload
Use a task-specific workspace
Create a workspace containing only the repository, fixture data, and configuration needed for one task. Mount the repository at a known path, keep secrets and unrelated repositories outside it, and destroy or reset the workspace after work that handles untrusted input. Read-only investigation can use a checkout with no writable source tree; implementation can add one explicit writable root.
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An ephemeral container or hosted worker limits persistence between tasks. GitHub describes its cloud agent as running in an ephemeral, firewalled environment, whereas its CLI has a local starting-directory default. Those are different threat models: a local CLI inherits the host’s exposure unless you add operating-system isolation, while a hosted worker may be isolated but still needs scoped repository, network, and credential permissions.
Separate users and workloads in self-hosted deployments
Do not place unrelated users or tasks in one self-hosted environment merely because they use the same agent image. OpenAI’s self-hosted guidance warns that shared environments can share files, credentials, and other resources, and recommends isolation by user or workload. Read the self-hosted sandboxes guidance before deciding that a shared worker is acceptable.
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Account for browser and desktop reach
If the agent can control a browser or desktop, model that surface separately from terminal access. A terminal sandbox may leave browser cookies, logged-in applications, clipboard contents, or visible windows available to a computer-use tool. Disable desktop control unless the task requires it, and place such tasks in a disposable user profile with no personal or production sessions.
3. Set filesystem permissions deliberately
Use the least permissive mode that supports the current phase. Start with inspection and planning, then grant a single writable root for implementation, and keep sensitive paths protected even when the workspace is writable.
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| Work phase | Recommended access | Typical approval trigger |
|---|---|---|
| Investigation or planning | Read-only checkout; no write access to source, home directory, or system paths. | Request to create files, modify configuration, or inspect a protected path. |
| Normal implementation | Read access to required dependencies and one explicit writable repository root. | Write outside the root, follow a symlink out of it, or change protected configuration. |
| Build and test | Workspace write plus narrowly scoped build caches and temporary directories. | Install a system package, access a new mount, or run a command requiring elevated privileges. |
| Release preparation | Separate release workspace with read-only source inputs and a distinct signing or publishing step. | Tag, publish, deploy, sign, or alter a shared or production resource. |
OpenAI’s managed-configuration examples include read-only and workspace-write modes, explicit writable roots, and protected paths. These examples illustrate the policy shape, not a universal configuration file; exact names and capabilities vary by agent and version. Never assume that restricting the current directory blocks access through another enabled tool.
4. Constrain network egress and integrations
Begin with no broad outbound access. Add destinations one at a time for the task: a package registry for dependency installation, a source-control host for fetching a repository, or a documentation domain for reference material. A proxy or filtering gateway can enforce this centrally and provide a second place to log requests.
Keep control planes separate
Terminal network restrictions do not automatically govern browser navigation, URL-fetch tools, MCP servers, or IDE connectors. Microsoft Visual Studio Code documents terminal sandboxing separately from tool and URL approvals in its approvals and permissions guidance. Create a policy entry for each tool surface, and test that a denied shell destination is also denied through any alternate fetch mechanism.
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Use destination approvals for exceptions
For an unfamiliar domain, require the agent to show the destination, the requested method, and the reason it needs access. OpenAI’s managed network example combines allowed and denied domains with approval for unfamiliar destinations. An allowlist should contain exact domains or controlled subdomains; avoid permitting an entire public suffix when one service is sufficient.
Scope integrations individually
Give an MCP server only the methods required for the task. A read-only issue tracker should not receive mutation methods; a documentation connector should not receive repository-write access. Disable unused connectors rather than relying on the model to ignore them.
5. Keep credentials outside the agent’s reach
Credentials are permissions with a long shelf life. Do not inject a developer’s personal token, production cloud role, or unrestricted SSH key into an agent environment simply to avoid a prompt.
- Issue a separate identity for agent work, with the minimum repository, API, and environment scope.
- Use short-lived credentials where the platform supports them, and make revocation and rotation routine.
- Keep application-level API keys outside a self-hosted sandbox when possible. OpenAI’s self-hosted documentation distinguishes an environment key that authorizes connection to the environment from the application API key and advises keeping the application key outside the sandbox.
- Store supported OAuth credentials in an operating-system keyring or other secure store rather than plaintext files exposed to the workspace.
- Do not mount a developer home directory, SSH agent socket, cloud metadata endpoint, or global credential directory unless the task explicitly needs it.
- Assume that any secret deliberately placed where agent code can read it may be copied into logs, generated files, tool arguments, or external requests.
When a task needs a secret, prefer a brokered operation that returns a narrowly scoped result instead of exposing the raw credential. For example, a deployment service can accept a reviewed artifact and perform the publish step under its own identity, while the coding agent never receives the production token.
6. Design approval gates around risk, not annoyance
A useful gate is narrow, understandable, and attached to the exact action. It tells the reviewer which tool will run, which parameters it will receive, what files or destinations are affected, and whether the action can be reversed.
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| Action | Gate design | Reviewer should verify |
|---|---|---|
| Read a protected file | Per-request approval; deny by default. | Path, reason, sensitivity, and whether a redacted input would suffice. |
| Write outside the task root | Per-action approval with the complete path list. | That each path is required and no symlink or glob expands the scope. |
| Use an unfamiliar URL or API | Per-destination approval; record domain and method. | Destination ownership, data sent, response handling, and whether an approved mirror exists. |
| Run a destructive command | Per-command approval; never blanket-allow destructive patterns. | Exact command, target, backup or rollback, and expected result. |
| Invoke an external side-effecting tool | Require a separate human-owned approval step. | Tool name, parameters, affected account or system, and authorization. |
| Deploy, publish, merge, or sign | Keep outside the coding session; require an authorized release workflow. | Reviewed diff, test evidence, identity, environment, and change record. |
Prefer one-action or limited-session approvals over a global allow switch. If an agent changes its requested parameters, treat that as a new request. Approval records should include the original user request, policy decision, tool name, parameters, result, and reviewer identity.
7. Assemble the policy in an implementation sequence
- Inventory: capture filesystem roots, commands, network destinations, MCP and other tools, credentials, browser or desktop capabilities, and external effects.
- Isolate: create a task-specific or ephemeral environment; separate users and workloads that should not share files, state, or secrets.
- Start read-only: let the agent inspect and plan without write access. Add one explicit writable repository root only when implementation begins.
- Deny broad egress: block general network access where feasible, then allow required domains individually through a proxy or policy layer.
- Grant tools one by one: enable shell, MCP, browser, connector, and desktop capabilities separately, with read-only methods where available.
- Add contextual gates: require approval for boundary expansion, sensitive-file access, unfamiliar destinations, destructive commands, and shared or production resources.
- Keep secrets out: use restricted identities, secure key storage, and brokered operations; do not mount general developer credentials.
- Log and review: preserve prompts, tool activity, approvals, results, network decisions, diffs, and tests. Require a distinct authorized step for deployment or other consequential changes.
The following is an illustrative policy sketch, not a drop-in product configuration. It shows the decisions that should exist regardless of vendor syntax:
mode: read-only
writable_roots:
- /workspace/repository
protected_paths:
- /workspace/repository/.env
- /workspace/repository/production
network:
default: deny
allow_domains:
- registry.example
- source.example
unfamiliar_destination: require_approval
tools:
shell: restricted
mcp: explicit_allowlist
browser: disabled
desktop: disabled
approvals:
outside_workspace: per_action
sensitive_files: per_action
destructive_commands: per_action
publish_or_deploy: separate_authorized_step
8. Understand how major agent implementations differ
Do not copy one product’s labels into another product and assume the same boundary exists. Compare implementations by filesystem, network, tool surface, credentials, approval scope, isolation, and audit behavior.
| Implementation | Documented behavior | Practical implication |
|---|---|---|
| OpenAI managed coding-agent controls | Describes sandbox modes, writable roots, protected paths, allowed and denied network destinations, unfamiliar-destination approval, keyring storage, and logging in Running Codex safely at OpenAI. | Use the documented controls as layers; verify current names and defaults before deployment. |
| Anthropic Claude Code auto mode | Its March 25, 2026 article describes an input-side prompt-injection probe for tool results and an output-side transcript classifier for actions. The article states: "The transcript classifier gates tool calls; the prompt-injection probe screens tool results." | Model-assisted review can reduce interruptions but is not a replacement for a technical sandbox or human ownership of high-impact actions. |
| GitHub Copilot agents | The application card distinguishes a cloud agent in an ephemeral, firewalled environment from a CLI whose default filesystem scope is its starting directory; optional computer use can reach desktop applications. | Assess cloud, CLI, SDK, and desktop modes separately instead of assuming one common blast radius. |
| Visual Studio Code agent approvals | The documentation, updated October 7, 2026, says terminal sandboxing is independent of permission level, warns that allow-all or autopilot skips prompts, and calls command auto-approval best-effort. It identifies sandboxing as Preview on macOS, Linux, and WSL2 and Experimental on Windows in the accessed documentation. | Keep sandbox and permission settings distinct, and recheck platform status because preview labels can change. |
| OpenAI self-hosted Agents API environments | Shared environments can share files, credentials, and resources; the guidance recommends isolation by user or workload and separates environment keys from the application API key. | Use separate environments when sharing state or secrets is not an explicit requirement. |
9. Reduce approval fatigue without removing the boundary
Frequent prompts usually indicate that the policy is too coarse, not that all prompts are unnecessary. Measure which benign actions repeat, then create a narrow, explainable rule for that class: for example, allow read-only tests in the task workspace while continuing to gate writes, network expansion, and destructive commands.
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Safer ways to reduce interruptions include:
- Approve a bounded session for one workspace and one tool, with an expiry time.
- Allowlist a specific executable and argument pattern rather than every shell command.
- Cache approval for an exact destination and method, not for an entire domain family.
- Move repetitive low-risk work into a prebuilt build service that has no production credentials.
- Keep a visible escalation path so the agent can explain why a new permission is needed.
Do not solve prompt fatigue by globally disabling prompts while leaving broad machine, network, and credential access in place. A user who presses approve automatically has converted the dialog into decoration.
10. Test the controls before trusting them
Run adversarial and ordinary tests in a disposable environment. Record the expected policy decision and the observed result.
- Workspace write test: verify that an allowed file can be created or changed and that a sibling directory is denied.
- Protected-path test: attempt to read a protected secret file and confirm that the request pauses or fails without revealing contents.
- Symlink and archive test: try a symlink, glob, or extracted archive that points outside the workspace; confirm canonical-path enforcement.
- Network test: call a denied domain through shell, browser, URL-fetch, and MCP paths; then test an approved domain and verify the log.
- Credential test: inspect the environment and common credential locations to ensure broad tokens, SSH sockets, and cloud metadata are absent.
- Tool-parameter test: change a previously approved tool argument and confirm that a new approval is required.
- Prompt-injection test: place instructions in a fixture, webpage, issue, or tool result that ask the agent to reveal secrets or weaken policy; verify that the agent cannot expand its technical access.
- External-effect test: simulate merge, publish, deployment, messaging, and signing actions and confirm they require a separate authorized step.
- Recovery test: revoke a task credential, destroy the environment, and reconstruct the workspace from source control and logs.
Review the resulting diff and test output as a human before merging. Logs support security triage, but logging does not make an unsafe action safe after the fact.
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| Failure mode | Why it happens | Fix |
|---|---|---|
| Approval dialog treated as the sandbox | The agent already has broad access and the user approves routine prompts. | Reduce filesystem, network, tool, and credential scope first; gate only the remaining exceptions. |
| Workspace-only assumption | Other tools, symlinks, mounted homes, or desktop control bypass the apparent directory boundary. | Enforce canonical paths and audit every enabled tool surface, including browser and computer use. |
| Shared self-hosted environment | Multiple tasks inherit the same files, credentials, caches, or processes. | Use per-user or per-workload isolation, or explicitly remove shared secrets and state. |
| Network allowlist is incomplete | Shell traffic is restricted but browser, MCP, package, or IDE connectors remain open. | Apply destination policy separately to each route and test alternate paths. |
| Overbroad credential injection | A personal or production token is mounted for convenience. | Use a restricted identity, short-lived access, a secure store, or a brokered operation. |
| Auto-approval classifier mistake | Model-assisted review misreads a command, prompt injection, or parameter. | Keep technical deny rules and human gates for high-impact actions; treat automated review as best-effort. |
| Approval habituation | Repeated benign prompts train users to approve without reading. | Measure repeated actions, create narrow allowlists, and preserve gates for boundary crossings. |
12. Operate and review the policy
Least privilege is a living configuration. Revisit it when the repository, agent version, operating system, tool set, or deployment workflow changes.
- Review the access inventory at each major agent or IDE upgrade.
- Expire temporary domain, tool, and writable-root grants automatically.
- Rotate task credentials and verify revocation after a workspace is destroyed.
- Sample approval records for unexplained path expansion, unusual destinations, or repeated denials.
- Compare declared tools with the tools actually invoked; remove unused integrations.
- Require a reviewed diff and tests before merge, and a separate authorized identity for deployment.
- Document who owns each approval decision and how to contact that owner during an incident.
Policy changes should be explainable: state the task requirement, the new capability, the expiration or rollback condition, and the evidence that the narrower option was insufficient.
The practical outcome
A secure coding-agent workflow is not a choice between constant prompts and unrestricted autonomy. Put the hard boundary in an isolated workspace with explicit filesystem, network, tool, and credential grants. Use approvals for narrowly defined exceptions and external effects, keep deployment decisions human-owned, and tune only the low-risk actions that create measurable friction. This preserves useful automation while keeping an agent’s worst possible mistake inside a deliberately small blast radius.
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