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Give an AI coding agent only the access required for the current task: a bounded workspace, explicitly enabled tools, no network by default, short-lived and narrowly scoped credentials, human approval for consequential actions, and an audit trail. Enforce those limits in the runtime sandbox rather than relying on the agent to behave, and fail closed when a required review or policy service is unavailable.
Use a task-specific permission contract
“Allow the agent to use the computer” is too broad to be a useful security setting. Treat every run as a contract that names the files, tools, destinations, credentials and decisions the agent may use. Start from deny-by-default, then add one capability when the task genuinely needs it. Remove the exception when the task ends.
OpenAI describes the sandbox as the technical boundary: it determines where an agent can write, whether it can reach the network and which paths remain protected. Approval policy is a separate mechanism that asks a person before an action outside the pre-approved boundary. Keeping those functions separate prevents a prompt such as “please approve this” from becoming the only protection.
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|---|---|---|---|
| Filesystem read | Task repository and explicitly named documentation | The task needs another directory | Mount read-only and exclude unrelated personal, credential and system paths |
| Filesystem write | Relevant workspace only | The agent must edit or generate project files | Technically enforce writable roots; protect repository metadata and security configuration |
| Command execution | Tests, formatters and other listed development commands | A command is necessary for the task | Use narrow rules; require approval for destructive, privileged or externally visible effects |
| Package installation | Disabled | A dependency must be added or a build environment prepared | Pin versions, use a controlled cache or registry and review lockfile changes |
| Network | Off | The task needs a specified service, registry or endpoint | Independently enforce an outbound allowlist, including for child processes |
| Credentials | None | A narrowly defined integration cannot work otherwise | Use a short-lived, destination-scoped credential or a trusted broker; never expose broad application keys |
| Approval | Human review for out-of-scope or high-impact actions | An action changes exposure, data, production or security posture | Show the exact command and side effects; do not train reviewers to click through prompts |
| Audit | Record every request, decision and tool result | Always | Retain logs long enough to investigate and restrict who can alter them |
Scope the filesystem before starting
Filesystem access is usually the agent’s most valuable and most dangerous authority. A generated command can search every readable file, not just the files mentioned in the prompt. Give the process a workspace containing the repository and the minimum supporting files; do not start it with your home directory, an entire drive or a shared build host mounted read-write.
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Choose the workspace boundary
- Identify the repository or task directory that must be changed.
- Mount that directory read-write only if edits are required. Mount reference material, caches and toolchains read-only.
- Keep password stores, SSH keys, cloud credentials, unrelated repositories, personal documents, operating-system directories and security-policy files outside the mount.
- Add an additional writable path only for a named need, such as a temporary build directory. Give it a cleanup owner and expiry.
- Check the effective permissions from inside the sandbox, not just the configuration file that was supposed to create them.
OpenAI’s Windows sandbox description discusses ACLs and write-restricted tokens and identifies .git, .codex and .agents as protected locations in that design. Those paths are an implementation example, not a universal rule for every agent or operating system. Preserve repository history, agent configuration and security controls wherever your platform provides an equivalent protection. See OpenAI’s Windows sandbox design.
Separate read, write and execute authority
Being able to read a source file does not imply permission to overwrite it, and writing a script does not imply permission to execute it. Model these as separate capabilities. A documentation task may need read access and a renderer but no write access to application code. A refactor may need source writes and tests but no package installation. A release task may need a deployment client, but that client should run in a separate, tightly reviewed job rather than in the same broad coding session.
Use ownership and mode controls, container mounts, ACLs or the equivalent runtime enforcement. A deny rule in an agent prompt is useful context but is not a filesystem boundary.
Allow commands by effect, not by convenience
Command execution spans a wide range of consequences. Reading a status, running a unit test and deleting a directory should not share one approval class. Define a small command set for the task and make the rule visible to the operator.
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- Low impact: repository status, file listing within the workspace, deterministic formatters and tests that write only to approved temporary directories.
- Review required: editing dependency manifests, installing packages, changing build scripts, invoking tools that contact an external service or modifying generated files outside the task root.
- Block or isolate: recursive deletion, disk or partition operations, credential-store access, privilege escalation, changes to access controls, production deployment and commands that pipe downloaded content directly into a shell.
Rules should match actual side effects, not only the first executable name. A wrapper script can invoke a dangerous child process, and a benign-looking package script can run arbitrary commands. OpenAI’s guidance describes rules that allow benign commands while requiring approval for or blocking dangerous patterns; adapt that approach to your runtime and test the rules with representative wrappers. See the rules and sandbox discussion.
Keep network access off, then build an allowlist
Most coding tasks can inspect a checked-out repository and run local tests without internet access. Leaving the network disabled prevents accidental source exfiltration and blocks an agent from fetching unreviewed instructions or binaries. Turn it on only for a stated requirement, such as downloading a dependency from an approved registry or calling a test API.
Design the exception
- Name each required hostname, port and protocol. Prefer a specific registry or service over unrestricted HTTPS.
- Decide whether DNS, redirects, object-storage endpoints and package mirrors are also needed; include only the destinations you can review.
- Enforce egress at the sandbox, container, VM or host firewall. Apply the rule to child processes and subprocesses, not only to the agent’s first process.
- Capture allow and deny decisions with the command that caused the connection.
- Remove the exception when the task or approval window expires.
Do not treat an environment variable such as an HTTP proxy setting as a security guarantee. OpenAI’s Windows write-up notes that programs can ignore proxy variables, bypass the configured PATH, or open sockets directly. An independently enforced network boundary is therefore required. The API sandbox guidance likewise recommends allowing outbound traffic only to approved endpoints; see Sandbox security.
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Keep credentials out of the agent whenever possible
Agent-generated code can read the files, credentials and network resources available to its environment. A key that is harmless in a narrow service may become a general-purpose exfiltration tool when placed in an agent-readable environment variable. Do not mount a developer’s home credential directory or copy a production key into the workspace.
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Prefer a brokered, short-lived capability
- Use a trusted proxy or secret broker that authenticates the agent run, checks the requested destination and returns only the operation it is authorized to perform.
- Issue credentials with the smallest scope: one repository, one API, one environment and one short expiry. Make them read-only when writes are unnecessary.
- Keep the application API key separate from any environment key used to connect the sandbox. OpenAI notes that a key readable by sandbox code should be restricted to connecting that environment, not treated as the application’s master credential.
- Never inject stored secrets merely because a tool might need them. If injection is unavoidable, make the exposure explicit, prevent unrelated network destinations and revoke the credential after the run.
After a run, inspect command output and logs for accidental secret disclosure and rotate any credential that may have been printed, copied into an artifact or read by an untrusted process. More detail on this threat model is in OpenAI’s sandbox security guidance.
Make approval proportional to blast radius
Approval should be tied to a specific action and its side effects, not granted as a blanket “trust this session.” A useful prompt shows the command, files or destinations affected, identity used, expected result and what cannot be undone. The reviewer should be able to reject one action without stopping safe local work.
| Action | Typical decision | What the reviewer should verify |
|---|---|---|
| Edit files inside the approved workspace | Automatic if the path and operation are bounded | Diff remains inside the task scope |
| Install or upgrade a dependency | Approve per change | Package source, version or lockfile diff, lifecycle scripts and license policy |
| Open a new network destination | Approve before connection | Hostname ownership, data sent, protocol and expiry of the exception |
| Read or use a secret | Approve per credential and purpose | Scope, recipient, lifetime and revocation path |
| Change access control, infrastructure or production data | Require a separate human-controlled workflow | Change plan, rollback, maintenance window and second-person review |
| Ambiguous or security-sensitive operation | Pause; do not guess | Clarified intent and documented authorization |
Two failure modes are common. Blanket auto-approval turns every later prompt into an unchecked escalation. Conversely, prompting for every harmless file read causes approval fatigue, so people click without inspecting. Keep routine, low-impact actions pre-approved and reserve prompts for genuine boundary crossings. OpenAI’s cybersecurity guidance recommends denying unauthorized actions and pausing ambiguous or high-risk changes for human approval; read Cybersecurity checks.
Log enough context to reconstruct the run
An audit record should answer who requested the work, what policy applied, what the agent attempted, which decision was made and what actually happened. At minimum retain:
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- the user or service identity, task text, repository or workspace identifier and policy version;
- each permission request and approval or denial, including the reviewer and timestamp;
- tool name, exact arguments, working directory, exit status and material output;
- files changed, hashes or a diff reference, and any artifact published;
- network destination, allow or deny result, process identity and transferred-data summary; and
- credential or broker request identifiers, never the secret value itself.
Protect logs from alteration and restrict access because they may contain source excerpts or sensitive command arguments. OpenAI says Codex can export OpenTelemetry events for user prompts, tool approval decisions, tool execution results, MCP usage and network proxy decisions. Those agent-aware events supplement ordinary process, file and connection logs; see the telemetry section.
Fail closed when safeguards are unavailable
If the approval service, policy evaluator, identity check or network enforcement layer cannot respond, stop the operation rather than silently switching to a broader mode. A temporary outage must not become an implicit authorization. Preserve the pending request and error context so an operator can retry or make a deliberate decision.
This is especially important for sensitive cybersecurity work, where an unavailable review mechanism can turn an ambiguous request into an irreversible action. OpenAI explicitly recommends failing closed when review is unavailable. The same principle applies to deployment, credential issuance and access-control changes.
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| Task profile | Workspace | Commands and network | Credentials |
|---|---|---|---|
| Code explanation or review | Read-only repository | Local parsing or tests only; network off | None |
| Feature implementation | Read-write task repository; protected metadata | Approved build and test commands; network off unless a dependency is explicitly required | None by default |
| Dependency update | Read-write repository and isolated package cache | Allowlisted registry; package scripts reviewed; lockfile diff required | Registry credential through a broker, if needed |
| Database migration rehearsal | Repository plus disposable local database | Only the test database endpoint; no production route | Ephemeral test credential |
| Production deployment | Separate release workspace | Dedicated deployment workflow with explicit destination and two-person or equivalent review | Short-lived deployment token, never a developer’s broad key |
These profiles are starting points, not universal defaults. Product implementations differ, so verify the selected agent’s current documentation and inspect its real behavior on your operating system. Do not infer that a control exists merely because a user-interface label suggests it does.
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Implementation checklist
- Write the task statement: list required files, commands, services, outputs and completion criteria.
- Create the sandbox: mount only the named workspace, make unrelated paths inaccessible and protect metadata and security files.
- Define command rules: pre-approve deterministic local operations; classify installers, wrappers, destructive commands and privileged tools separately.
- Set network policy: leave egress off or enumerate approved endpoints with an expiry; enforce it outside the shell.
- Handle secrets: remove ambient credentials and configure a brokered, least-scoped token only if the task cannot proceed without one.
- Configure approvals: require a human for boundary crossings, ambiguous requests and high-blast-radius changes; show side effects in each prompt.
- Turn on logging: capture prompts, decisions, tool calls, results, file changes and network outcomes without storing secret values.
- Test the boundaries: attempt an out-of-scope write, a blocked connection, a child-process bypass and a denied credential request. Confirm each fails.
- Review and revoke: inspect the diff and logs, remove temporary mounts and allowlist entries, and revoke or rotate exposed credentials.
Common mistakes and fixes
Giving the agent the whole computer
Why it fails: a prompt or dependency script can read unrelated secrets or modify system state. Fix: run in a disposable, bounded workspace and add paths one at a time.
Relying on prompts instead of enforcement
Why it fails: generated code can ignore instructions and use any authority granted to its process. Fix: enforce filesystem, process and network boundaries in the runtime, then use prompts for deliberate exceptions.
Allowing unrestricted internet for one package
Why it fails: the same permission can fetch arbitrary content or leak source. Fix: allowlist the registry or API, constrain child processes and expire the rule.
Injecting a permanent cloud key
Why it fails: every command and generated script can potentially read it. Fix: use a short-lived, operation-specific broker token or move the privileged action to a separate controlled service.
Approving an unclear request
Why it fails: the reviewer may authorize a side effect they did not understand. Fix: pause, clarify the intended files, destination and rollback, then approve the smallest resulting action.
Continuing during a policy outage
Why it fails: the safety decision becomes an undocumented default. Fix: fail closed, retain the request and retry after the review mechanism is healthy.
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