Give an AI coding agent the smallest workspace, tool set, network reach and credential scope that can complete its task, then require explicit approval when it needs to cross one of those boundaries. A sandbox is the enforcement boundary; an approval policy is the decision layer. Use both. An approval dialog by itself does not stop an agent that already has broad filesystem, network or credential access.
What least privilege means for an AI coding agent
Least privilege is a running configuration, not a single checkbox. For each task, the agent should have only the access needed to inspect files, edit a defined working tree, run approved checks and call explicitly permitted services. Everything else should be unavailable or require a deliberate exception.
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Keep two controls separate:
- Sandbox and isolation: enforce where the agent can read and write, which processes it can start, which network destinations it can reach and which environment or identity it uses.
- Approval policy: decide when the agent must stop and ask before expanding scope, invoking a sensitive tool or causing an external effect.
OpenAI describes these as complementary controls in its account of sandboxing and approvals: Running Codex safely at OpenAI. A prompt that says “allow this command” is useful only if the underlying environment has already constrained what that command can affect.
Start with an access inventory
Before changing settings, document the agent’s actual blast radius. Inventory the configuration that is easy to overlook, including integrations enabled by a desktop or browser extension.
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| Area | Questions to answer | Evidence to record |
|---|---|---|
| Filesystem | Which roots can the agent read? Which can it write? Are home directories, SSH folders, build caches or other repositories mounted? | Mounts, working directory, protected paths and a test of access outside the project. |
| Processes | Can it run arbitrary shell commands, package managers, compilers, scripts or background services? | Allowed command classes, process restrictions and whether child processes inherit access. |
| Network | Is outbound traffic disabled, proxied or unrestricted? Which domains are needed for dependencies, issue trackers or APIs? | Allowlist, denylist, proxy policy and whether rules apply to browser and integration tools as well as the shell. |
| Tools | Which MCP servers, connectors, browser controls, desktop controls and application APIs are available? | Tool names, parameters, scopes and approval behavior for each tool. |
| Credentials | Which environment variables, files, keyrings, tokens and cloud identities are visible? | Identity, permissions, expiration, rotation owner and the location from which each secret is loaded. |
| External effects | Can the agent merge code, open a pull request, send a message, change an issue, deploy, publish or alter shared infrastructure? | Required approvals, target systems and the accountable human owner. |
Include browser or computer-use capabilities in this inventory. GitHub notes that optional computer use can interact with desktop applications beyond the CLI’s starting directory, so a directory restriction does not describe the complete reach of an agent with desktop control enabled: GitHub’s Copilot Agents application card.
Choose an isolation boundary before granting permissions
Use a task-specific workspace or an ephemeral environment whenever possible. Mount only the repository and supporting files needed for the task, and separate workloads that must not share credentials, caches or state.
Local workspace
A local workspace is convenient for fast edits, but it inherits the operating system user’s access unless an additional sandbox limits it. Create a dedicated checkout, remove unrelated repositories and keep production credentials out of the user profile visible to the agent.
Container or ephemeral cloud environment
A disposable container or cloud workspace makes reset and user separation easier. Destroy it after the task, or explicitly scrub any retained artifacts. GitHub describes its cloud agent as running in an ephemeral, firewalled environment, while its CLI defaults to the directory from which it starts. Those are different deployment patterns, not interchangeable settings.
Self-hosted environment
If several users or tasks share one self-hosted environment, they may share files, credentials and other resources. The OpenAI self-hosted sandboxes guide recommends isolating by user or workload unless shared access is intentional. Treat a shared environment as a deliberate trust boundary, document who is allowed to see its state and avoid placing one task’s secrets where another task can read them.
Define read and write boundaries
Begin every investigation or planning task in read-only mode. For implementation, expose only the working tree that needs edits and name writable roots explicitly. Preserve protected paths such as deployment configuration, secret stores, signing keys and organization-wide policy files.
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| Task phase | Recommended filesystem policy | Approval trigger |
|---|---|---|
| Explore or plan | Read-only access to the task repository; no writes outside temporary output. | Any request to create or modify a file. |
| Implement | Read and write only in the selected repository or worktree; temporary build output in a disposable directory. | Access to another repository, home directory or protected path. |
| Run tests | Same workspace plus only the caches and fixtures required by the test runner. | Test requires a new mount, privileged process or unapproved service. |
| Prepare release | Read-only access to release metadata until a separate release step is approved. | Tagging, publishing, deployment or changing shared infrastructure. |
OpenAI’s managed configuration examples distinguish read-only and workspace-write modes and identify a writable root. Exact option names vary by agent and version, so map the principle to the product’s current configuration instead of copying syntax between tools.
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Test the boundary with harmless probes: attempt to list a parent directory, read a known protected file and write outside the worktree. The expected result is a denial or an approval request, not silent success. Repeat the test after enabling any new connector or desktop capability.
Constrain network egress and integrations
Set outbound network access to deny by default where the task permits. Add destinations one at a time, with a reason, owner and expiration. A dependency install may need a package registry; a documentation task may need one documentation host; neither automatically needs unrestricted internet access.
- List every destination required by the build, tests and explicitly requested integrations.
- Resolve redirects and secondary endpoints so an allowlist does not accidentally fail open through an unreviewed host.
- Allow only those domains through a proxy or filtering layer when possible.
- Require approval for an unfamiliar destination, a change from read to write access or a request to upload source, logs or credentials.
- Remove temporary exceptions when the task ends.
OpenAI’s managed-network example uses allowed and denied domains and approval for unfamiliar destinations: OpenAI’s sandbox and approval description. Keep shell rules separate from browser, URL-fetch, MCP and application-tool rules. Microsoft states that VS Code terminal sandboxing governs terminal commands, while tool and URL approvals are separate controls in its documentation updated October 7, 2026: Manage approvals and permissions.
Do not confuse dependency access with data egress
Allowing a package registry does not mean the agent should be able to post source code to arbitrary endpoints. Use distinct rules for downloading dependencies, querying an API and uploading artifacts. Log the destination, method and volume where the platform supports it.
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Grant tools individually
Model each tool as an independent permission. Shell, file editing, browser navigation, MCP servers, issue trackers, source-control operations and desktop control have different failure modes and should not inherit one broad “agent” grant.
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- Shell: permit the commands needed for the task; block privilege escalation, destructive operations and access to unrelated roots.
- File editing: restrict paths and preserve protected files even when the shell is available.
- MCP and connectors: enable only named servers and narrow their operations; read-only issue lookup is not equivalent to issue mutation.
- Browser or URL fetch: apply URL approval and upload controls separately from terminal rules.
- Desktop or computer use: keep disabled unless required, because it can reach applications outside a repository boundary.
For each tool, define the operation, target, data passed, expected side effect and approval level. A tool that can both read and delete should expose separate operations or require approval for the destructive one.
Keep credentials outside the agent’s reach
Credentials are permissions with a long tail. Do not inject a broad developer token, production cloud role or personal SSH key merely to make a task convenient. Use a separate identity with the smallest scopes, short expiration and a clear revocation owner.
- Keep application-level API keys outside a self-hosted agent sandbox where the platform supports that separation.
- Use restricted environment credentials for the task, not a user’s all-purpose identity.
- Store supported OAuth credentials in an operating-system keyring or other secure store rather than plaintext files.
- Never place a secret in a repository, prompt, fixture, build log or tool argument unless the operation specifically requires it.
- Rotate or revoke task credentials after a high-risk action, suspected prompt injection or unexpected data transfer.
The OpenAI Agents API overview and its self-hosted environment guidance distinguish an environment key, which authorizes connection to the environment, from the application API key, which should remain outside the sandbox. The general rule is simple: an agent can read any key deliberately placed where its code can read it.
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Design approval gates around consequences
Ask for approval when an action expands reach or creates an external consequence, not for every harmless read. A useful gate is narrow, contextual and understandable from the tool name and parameters shown to the reviewer.
| Action | Suggested default | What the reviewer should see |
|---|---|---|
| Read files inside the task workspace | Automatic | Path scope and operation. |
| Edit ordinary source files in the worktree | Automatic in implementation mode | Paths and proposed diff. |
| Read secrets, identity files or protected configuration | Manual approval | Exact path, reason and data that may enter tool context. |
| Write outside the worktree or add a mount | Manual approval | New path, duration and affected users or services. |
| Use an unfamiliar URL or external service | Manual approval | Tool, destination, method, payload and response handling. |
| Run destructive or privileged commands | Manual approval per action | Full command, working directory and predicted effect. |
| Modify shared systems, merge, publish or deploy | Separate authorized step | Diff, target, rollback plan and accountable approver. |
Prefer a one-action approval or a short, bounded session grant. If a platform offers organization policy, require manual approval for specific high-impact tools even when a user has enabled an automatic mode. Review the tool name and parameters, not merely the model’s explanation.
Reduce approval fatigue without removing the boundary
Repeated prompts are a usability problem, but globally skipping them while leaving broad access enabled converts fatigue into uncontrolled authority. Tune the policy from observed, low-risk repetition:
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- Collect the command, tool, parameters, path and reason for each repeated prompt.
- Group only actions with the same limited effect, such as read-only tests in one workspace.
- Create a narrow allow rule with an owner, expiry and a clear explanation.
- Keep prompts for boundary crossings, sensitive files, external services and destructive operations.
- Review the exception after the task and remove it if it is no longer needed.
Anthropic reports that Claude Code users approve 93% of permission prompts in its March 25, 2026 engineering article, and presents auto mode as an alternative to both constant manual approval and disabling all prompts. Its article says, “By default, Claude Code asks users for approval before running commands or modifying files.” The mechanism is product-specific: “The transcript classifier gates tool calls; the prompt-injection probe screens tool results.” Model-assisted review can still make mistakes, so treat it as an additional check rather than a replacement for sandbox enforcement: How we built Claude Code auto mode.
Log actions and make review a separate control
Preserve enough context to reconstruct what happened:
- Original task request and identity of the initiating user.
- Sandbox mode, writable roots, network policy and enabled tools.
- Every tool call, parameter, target, result and error.
- Approval request, approver, decision, time and duration of any exception.
- Network-policy decisions, including denied destinations.
- Final diff, test results and any merge, release or deployment action.
Review the diff and tests before merging. Require a distinct authorized step for deployment or another consequential external change, even if the agent prepared the change successfully. Logs support incident triage and policy tuning; telemetry fields differ by product, so verify what your deployment actually retains and protect the logs from the agent itself.
Apply the pattern to common agent deployments
OpenAI-managed or self-hosted environments
Use the documented sandbox modes and writable-root controls as the technical boundary, then configure network destinations, keyring-backed credentials and approval rules around exceptions. In self-hosted deployments, isolate users and workloads rather than assuming an environment is private because it has a different name. Confirm current configuration names in the relevant documentation before rollout.
GitHub agent variants
Do not transfer assumptions between GitHub’s cloud agent, CLI, SDK and desktop experiences. The cloud agent is described as ephemeral and firewalled; the CLI’s default filesystem scope is its starting directory; optional computer use can act across desktop applications. Set policy for the actual surface your team enables, and test each one.
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Set permission level and terminal sandboxing independently. Microsoft documents allow-all or autopilot modes as skipping prompts and describes command auto-approval as best-effort. Its accessed documentation lists terminal sandboxing as Preview on macOS, Linux and WSL2 and Experimental on Windows; verify the current platform status before relying on it. Add explicit tool and URL approvals because terminal restrictions do not cover every integration.
Failure modes and recovery
The agent can read outside the repository
Check inherited mounts, the process user’s home directory, symlinks, workspace extensions and desktop controls. Remove unnecessary mounts, run under a dedicated identity and retest with an outside-path probe. Do not treat a starting-directory default as a complete filesystem sandbox.
A command appears safe but reaches the network
Inspect child processes, package-manager hooks, scripts and redirects. Apply egress rules at the environment or proxy layer, not only in a command allowlist, and require approval for newly discovered destinations.
Prompts are constant
Inspect which low-risk action is repeated and create a narrowly scoped, expiring rule for that class. Keep gates on sensitive paths, external tools and irreversible operations. If the platform cannot express a safe narrow rule, retain manual approval rather than enabling a global bypass.
A model-approved action was wrong
Revoke task credentials, preserve logs and stop the agent. Review tool parameters and the affected systems, then tighten the sandbox or policy. Automated classifiers and command parsing are fallible; recovery must not depend on the model recognizing its own mistake.
Two tasks expose each other’s state
Move them to separate environments or workspaces, remove shared credentials and caches, and rotate any secret visible to the wrong task. Shared state should be an explicit requirement, not an accidental default.
A practical rollout sequence
- Inventory: record filesystem roots, commands, destinations, tools, credentials and external effects.
- Isolate: create a task-specific or ephemeral environment and separate users and workloads that should not share state.
- Start read-only: allow investigation and planning without writes, network expansion or credential injection.
- Expose the worktree: add only the repository and writable root required for implementation.
- Constrain egress: deny general outbound access where feasible, then allow named destinations individually.
- Grant tools: enable shell, MCP, browser, desktop and application tools one by one with separate scopes.
- Set gates: require approval for scope expansion, sensitive files, unfamiliar URLs, destructive commands and shared or production systems.
- Use restricted credentials: short-lived, least-scoped identities; keep application keys outside the agent environment when possible.
- Exercise the controls: run harmless boundary probes, inspect logs and verify denied actions remain denied.
- Tune carefully: allowlist only explainable low-risk repetitions, with expiry and an owner; review after the task.
This sequence gives the agent room to be useful while preserving a technical stop line. The human approver then concentrates on decisions that genuinely change the agent’s authority or affect people and shared systems.
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