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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Running an AI agent on your own computer does not automatically protect your data. If the agent’s process can see a directory, environment variable, socket, browser profile or network route, code it generates may be able to use it. OpenAI’s security documentation states the rule plainly: “Agent-generated code can access the files, credentials, and network available to its environment.”
The safe design is therefore an operating-system or container boundary around the agent, a workspace containing only the files needed for the task, no long-lived secrets in the execution environment, restricted network egress and human approval for consequential actions. Keep authentication, policy, logging and recovery in a trusted harness; let the sandbox perform only the model-directed work you have deliberately exposed.
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What “local” means—and what it does not
“Local” describes where the model call or tool process runs. It does not describe what that process can reach. An agent launched from a terminal on your laptop may inherit your user permissions, home directory, SSH keys, cloud credentials, browser data, local sockets and unrestricted internet access. A prompt that says “do not read secrets” is not an isolation boundary: generated code can ignore the instruction.
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A useful mental model is an untrusted build job. Treat repository code, downloaded archives, documents and model-generated scripts as potentially hostile. The agent can be helpful while still being allowed to inspect only a staged directory, write only to a disposable output area and connect only to explicitly approved services.
Choose an isolation boundary that matches the work
Different local-agent implementations provide very different protection. Verify the exact SDK version, operating system and configuration rather than assuming that a product label such as “sandbox” means the same thing everywhere.
| Execution choice | What the documented behavior provides | When it fits | Important limitation |
|---|---|---|---|
| Ordinary host process | Runs with the permissions and environment of its user account. | Only for code and inputs you already trust, with a narrowly prepared account. | No inherent filesystem, network or credential confinement. |
UnixLocalSandboxClient on Linux |
The Agents SDK quickstart says the Linux path adds no OS-level command confinement. | Convenient trusted development after you have independently constrained the account and workspace. | Do not treat it as protection for untrusted commands or files. |
UnixLocalSandboxClient on macOS |
Its macOS path applies filesystem restrictions through sandbox-exec. |
Tasks needing local execution with a filesystem policy. | The documented path does not provide network isolation. |
| Configured container or hosted sandbox | Can provide a separate filesystem, user, process environment and network policy when configured correctly. | Untrusted repositories, downloaded content and commands that need a stronger boundary. | A container is not secure by default; mounts, capabilities, sockets, user identity and host integration determine the result. |
| External isolation such as a dedicated VM or service | Places execution outside the workstation’s normal user session. | High-impact or highly untrusted workloads. | Still requires least-privilege mounts, credential handling, network rules and retention controls. |
The Agents SDK quickstart recommends a configured Docker or hosted sandbox, or another external isolation mechanism, for untrusted commands. See the caveats in the Agents SDK quickstart before selecting a local client.
Build the setup in seven deliberate steps
1. Inventory everything the agent could inherit
Before starting an agent, map the execution context rather than guessing. Record the operating-system user and groups, current working directory, home directory, environment variables, mounted paths, available interpreters, local sockets, tool and MCP-server connections, browser or credential-store access and outbound routes. The goal is to answer “what can this process read, change or contact?”
- Check the account and groups used by the agent. A service account with no administrative privileges is preferable to your everyday account.
- List environment variables and remove application keys, cloud tokens, package-manager credentials and signing keys from the child process.
- Inspect mounts and the home directory. A broad home-directory mount can expose SSH configuration, shell history, browser profiles and unrelated projects.
- Identify tools that make their own network connections. A policy applied to the agent process may not cover a remote MCP server or a helper running elsewhere.
- Map local sockets such as container engines, desktop automation bridges and database sockets. A mounted control socket can be equivalent to administrative access.
For a trusted administrator’s inventory shell, commands such as id, env, findmnt (Linux), mount (macOS and Unix), and a review of the agent configuration are useful. Do not ask the agent itself to dump secrets while checking its environment; perform the inspection from the trusted side and redact logs.
2. Select the boundary based on trust
Classify both the instructions and the inputs. A personal script you wrote and a local text file may justify a convenient local client. A cloned repository, an attachment from an unknown sender, generated code that installs packages, or a task involving regulated data deserves a configured container, hosted sandbox or external isolation.
Trust is not binary. You can permit a trusted agent to edit a project while denying it network access, or allow a network connection to one API while making all source mounts read-only. Write down the specific actions that are allowed before choosing the runtime, then verify that the runtime can enforce them.
3. Create a narrow, disposable workspace
Stage only the repository files and reference material needed for the task. Keep personal directories, credential stores and unrelated projects outside the sandbox. If the agent only needs to inspect sensitive source data, prefer a copy or a read-only mount; if it must modify files, give it a disposable working copy and review the result before exporting it.
OpenAI’s sandbox guidance describes mounting task inputs and reviewing generated artifacts before moving them outside the sandbox. A practical layout is an input directory, a separate writable work directory and an output directory that a human reviews. Do not mount the entire home directory merely to make path discovery easier.
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- Use read-only access for source trees, reference documents and configuration that the agent should not alter.
- Use a copy for data that could be damaged by an accidental write or deletion.
- Keep output separate so a reviewer can inspect diffs, archives and generated files before publication or deployment.
- Remove temporary workspaces after the task, subject to your retention requirements.
State can survive a resumed session. The Agents SDK concepts guide notes that a resumed session may preserve an existing workspace, so a newly supplied manifest does not necessarily describe every file that is still present. Inspect the live workspace at resume time and clear or rebuild it when a clean state matters. See the workspace and sandbox concepts.
4. Keep long-lived credentials in the trusted harness
Do not put production API keys, cloud credentials, SSH private keys, signing keys or password-manager exports in the agent’s environment, prompt, task file, source tree, generated artifact or log. A secret manager protects a value only while the value remains outside the agent’s reachable process; injecting it into the environment makes it readable to generated code and any tool in that process.
Separate credentials by purpose:
- Control-plane credentials: Keep model-provider authentication, billing and administrative tokens in the trusted harness. The sandbox should not need to read them.
- Executor access: If the sandbox must authenticate back to an executor, provide a narrowly scoped, short-lived key with no unrelated permissions.
- Third-party services: Put a trusted proxy or server between the agent and the service. The proxy can enforce an allowlisted destination, method, tenant and data shape, then use its own narrowly scoped credential.
- One-time operations: Prefer an approval that causes the trusted harness to perform the action, rather than handing the agent a reusable secret.
OpenAI documents this separation, brokered third-party access and credential rotation in its security guide. If a token may have been exposed, revoke or rotate it immediately and investigate logs for use; do not rely on deleting the visible file alone.
5. Set network policy deliberately
The safest default for a file-editing task is no outbound network access. If the agent needs a package registry, documentation site or one API, allow only those destinations and ports. An allowlist is easier to reason about than a long denylist, especially when a dependency can redirect through another host.
Apply the rule where the connection originates. A local tool may connect from the harness, while a hosted executor, MCP server or helper process may connect from a different network. The policy must cover each location; restricting the parent process does not automatically restrict a remote tool.
Network controls also prevent accidental exfiltration. A generated script can upload a whole mounted directory, resolve an internal hostname or call a paste service without displaying the data in the chat. Restrict DNS and egress, and log destination, time, requesting tool and approval decision where the platform supports it.
6. Gate consequential actions with approval
Require a human decision before an agent can modify broad system state, transmit data externally, install or execute an untrusted dependency, publish a change, delete files outside its workspace or use a production service. The approval prompt should show the exact command or request, paths affected, destination, files or data being sent and the identity under which it will run.
Keep approvals specific and short-lived. “Allow network access” is weaker than “allow a GET request to the documentation host for this task.” A denial should stop the tool call, not merely tell the model that it failed.
Some deployments also record prompts, approvals, tool outputs, MCP use and network-policy decisions. OpenAI describes these practices in its account of running Codex safely. Those controls are an example of one deployment’s governance, not a universal feature set. If your agent lacks telemetry, capture equivalent events in the trusted harness.
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7. Test the boundary with harmless probes
Validate the policy before giving the agent real data. Use a disposable workspace and non-sensitive test values, then verify both success and denial:
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- Confirm that an allowed input file can be read and an allowed output file can be written.
- Attempt to read a path outside the workspace and confirm that the operation fails.
- Attempt to write to a read-only mount and confirm that the write is denied.
- Check which environment variables are visible; the test process should not see long-lived application keys.
- Try an approved network destination, then a clearly unapproved destination, and record the results.
- Resume a session and inspect whether old files remain in the workspace.
- Request a risky command and verify that the approval gate pauses it before execution.
These probes test your actual configuration, not a product description. Repeat them after changing the SDK, operating system, container image, mounts, tools or network policy.
Filesystem controls that prevent accidental disclosure
Mount less than you think you need
An agent rarely needs your entire home directory. Mount the project subdirectory, a selected set of reference files and an empty output directory. Avoid exposing ~/.ssh, cloud-provider configuration, browser profiles, password-store directories, source-control credential helpers and local database sockets.
Read-only protection is valuable but not absolute: an agent may still copy readable data into its writable area or encode it in an output file. Pair read-only inputs with network restrictions and an artifact review. If the data itself is highly sensitive, provide a transformed or redacted copy containing only the fields required for the task.
Review artifacts before export
Inspect diffs, file names, archives and metadata. Look for embedded credentials, hidden files, copied source outside the requested scope, unexpected binaries and outbound URLs. Treat generated documentation and test fixtures as data that can contain secrets, not as automatically safe text.
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Only after review should the trusted harness copy selected artifacts to the host project, publish them or pass them to another service. Keep the export operation outside the agent’s permissions so the model cannot silently bypass review.
Credential designs that remain useful without exposing secrets
Use a broker for third-party APIs
Suppose an agent must look up issues in a ticket system. Instead of giving it the ticket-system token, expose a trusted function such as “list issues for project X” through a broker. The broker authenticates to the service, validates project and query parameters, limits returned fields and records the request. The agent receives only the response needed for its task.
The same pattern works for cloud storage, deployment systems and email. Scope the broker credential to read-only operations where possible, restrict tenant and resource identifiers, cap response size and make destructive methods approval-only. Never echo the underlying token in an error, trace or model-visible response.
Handle suspected exposure as an incident
Rotate or revoke a credential as soon as it may have entered the agent environment, logs or artifacts. Review provider audit logs, invalidate sessions, inspect exported files and determine whether the token had write or administrative privileges. Then change the setup so the replacement credential remains in the trusted side or is brokered with narrower scope. Deleting a prompt or workspace does not prove that a copied value was not transmitted.
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Command execution and local-shell considerations
Shell access is more powerful than a file-editing API. A command can install a package with a malicious post-install script, alter system configuration, enumerate devices or send data over a helper binary. For local shell execution, use a sandbox or a strict command allowlist and denylist before forwarding anything to the system shell, as described in OpenAI’s local shell guidance.
Prefer structured tools over arbitrary shell strings: provide “run the test suite in this directory” rather than an unrestricted command interpreter. Run as a non-administrator, remove unnecessary capabilities, use a clean working directory and pin dependencies when installation is unavoidable. Review package names, lockfiles and install scripts before approval.
The local-shell page notes that legacy local-shell support ended on February 12, 2026. Check the current API and SDK behavior before relying on an older integration; do not assume that a previously working command loop still receives security updates or has the same controls.
Container and hosted-sandbox hardening
A container can reduce the blast radius, but its safety comes from configuration. Use a non-root user, a minimal image, a read-only root filesystem where practical, a small writable temporary directory, dropped Linux capabilities, no host control sockets and only the mounts required for the task. Disable network access unless a documented allowlist is needed. Keep the host kernel, container runtime and image-build process patched and trusted.
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Hosted execution moves the worker away from the developer laptop but introduces provider-side responsibilities. Confirm what data is retained, who can administer workers, how images are built, how sessions are isolated and where network connections originate. Anthropic’s self-hosted security model emphasizes that the operator remains responsible for least privilege, tool isolation inside the sandbox, worker image and build integrity, and data retention after session content reaches the worker.
Do not claim that one container or hosted platform is categorically safer than another without comparing the exact version, host integration, mounts, egress rules and credential design. The boundary you configure—not the product name—determines the exposure.
Practical patterns for common workloads
Trusted code maintenance
Use a local client under a non-administrator account, mount one repository, keep the source read-only if the agent only analyzes it, and write proposed patches to a separate directory. Permit no network unless tests require a specific service. A human reviews the diff and applies it to the real checkout.
Untrusted repository or document
Build or use a configured container or hosted sandbox. Copy the input into a disposable workspace, run as an unprivileged user, deny network by default and expose no home-directory mounts or credentials. Allow package installation only after reviewing the dependency and approving the exact operation. Destroy the workspace after exporting inspected results.
Sensitive data analysis
Minimize the dataset first: remove fields the task does not require, use synthetic values for development and mount the remaining input read-only. Keep the network disabled, prevent access to local sockets and require review of every output file. If a remote model or tool is involved, verify the data path and retention policy before sending any record.
Agent that must call an external service
Keep the service credential in a trusted proxy. Give the agent a narrow function with validated parameters and an allowlisted destination. Return only the minimum response, redact secrets and require approval for writes, deletes or publication. Log the request, policy decision and result outside the agent workspace.
Comparison checklist before you start a session
| Control | Question to answer | Acceptable evidence |
|---|---|---|
| Process identity | Which user, groups and capabilities run the agent? | A non-administrator identity with only task-required permissions. |
| Filesystem | Which paths are mounted, and which are writable? | A small task workspace; sensitive inputs read-only or copied; no home-directory or credential-store mount. |
| Credentials | Can generated code read a long-lived token? | No. Control-plane keys remain in the harness; third-party access is brokered or short-lived. |
| Network | Where does each tool connect from? | No egress by default, or an explicit destination allowlist covering local and remote tools. |
| Commands | Can the agent execute arbitrary shell or install software? | Structured tools, a constrained shell and approval for installation or system changes. |
| State | What survives a resumed session? | A documented retention policy and a verified clean-workspace procedure. |
| Governance | Are risky requests reviewable and auditable? | Specific approvals and logs of requests, decisions, outputs and policy events. |
Troubleshoot failures without weakening the boundary
The agent cannot find a needed file
Do not mount the whole disk as a quick fix. Check the path inside the sandbox, then add only the required directory or stage a copy into the workspace. Keep the new mount read-only unless a write is essential, and rerun the denied-path probe.
A package install fails because the network is blocked
First ask whether installation is necessary. Prefer a prebuilt, reviewed image or a dependency cache prepared by the trusted side. If a network exception is unavoidable, allow the specific registry and package operation for a short window, record the approval and remove the exception afterward.
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Identify where the connection originates. The helper may be a remote MCP server or a host-side process rather than the sandbox. Move the call behind the trusted proxy, apply policy at that egress point and ensure the response does not expose credentials or unrelated data.
A resumed session contains unexpected files
Stop the task, inspect the live workspace and identify the session’s persistence behavior. Delete or rebuild the workspace before continuing, then update the resume procedure so each session starts from an explicitly verified state.
A credential appears in output or logs
Revoke or rotate it, preserve the relevant audit evidence securely, inspect whether it was transmitted and remove the injection path. Redact future tool outputs and keep secrets out of prompts, files and environment variables.
Version and responsibility caveats
Sandbox behavior can change with SDK releases, operating systems, agent settings and container configuration. Read the current documentation for the exact client you deploy, and repeat boundary tests after upgrades. The documented Linux and macOS behavior of UnixLocalSandboxClient is not a promise about every future release.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesSecurity controls are layered. Filesystem restrictions do not replace network policy; network policy does not replace credential separation; approvals do not replace least privilege. A trusted harness can reduce exposure, but it must itself be protected, patched and limited to the data and administrative functions it needs.
The bottom line
Run the agent in a deliberately constrained execution environment, not directly in your everyday account. Give it a small workspace, read-only inputs or disposable copies, no long-lived credentials, no network by default and only narrowly scoped tools. Put authentication and high-impact actions in a trusted harness or broker, require explicit approval for consequential operations and test denied paths before real work. “Local” is a location; the protection comes from the boundary you configure and continually verify.
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