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How do I give an AI desktop agent access to only the files it needs? Put the required documents in a dedicated workspace, expose only that folder (or specific files) to the agent, make inputs read-only whenever possible, and use an OS sandbox, container, microVM, or hosted runtime when the agent can execute commands. A workspace path selected in an app is not automatically a security boundary.
The safest design treats file access, writing, network access, credentials, and integrations as separate capabilities. Configure each one independently, then test the boundary with harmless files before sharing sensitive data.
A workspace folder is not the same as a security boundary
Many desktop agents ask you to choose a project directory or working directory. That setting tells the application where to start, but it may not prevent the agent or a subprocess from opening other paths.
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The OpenAI Agents SDK documents this distinction for its Unix-local client: commands run as host processes, and choosing a workspace, HOME, or current working directory does not restrict what those processes can access on Linux. If the agent can run shell commands, code it generates may use every file and credential available to that host process. See the SDK client guidance at the Agents SDK sandbox clients documentation.
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OpenAI’s Sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” That sentence is the practical threat model: reduce what the environment can see, rather than relying on an instruction such as “only use this folder.”
A least-access setup therefore has two layers:
- Scope: copy or mount only the files required for the task and tell the agent exactly where they are.
- Enforcement: use permissions and an execution boundary that can actually deny access outside those paths.
For a trusted agent that only reads files, careful scoping may be adequate. For untrusted prompts, generated code, plugins, or shell commands, use an enforced sandbox and treat the workspace as one input to that sandbox—not as the sandbox itself.
The least-access setup, step by step
1. Create a task-specific workspace
Make a new directory for one job, such as a contract-review folder or a source-code snapshot. Copy or export only the documents the agent needs. Do not point it at your entire home directory, a complete cloud-drive root, or a repository containing unrelated secrets when a narrower folder will work.
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Keep the original files elsewhere and preserve the smallest useful set of metadata. If the task needs a spreadsheet and two reference PDFs, provide those three files rather than the whole project archive. For recurring jobs, generate a fresh workspace from a known source so old outputs and unrelated conversations do not accumulate.
2. Separate inputs from outputs
Use one directory for source material and another for files the agent is allowed to create. Make the input directory read-only if the operating system, container, or agent framework supports it. Give write permission only to the dedicated output directory.
This arrangement limits accidental edits and makes review straightforward: everything the agent can change appears in one place. The OpenAI sandbox guide recommends mounting only the inputs an agent should use and reviewing artifacts before moving them outside the sandbox; see the sandbox guide.
3. Configure the agent’s paths explicitly
In the agent’s workspace or mount configuration, list the input and output paths rather than selecting a broad parent directory. In the prompt, identify the allowed paths and the expected output location. Path instructions improve behavior, but they are not a substitute for OS enforcement.
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4. Grant the smallest useful permissions
- For summarization, classification, or review, expose inputs as read-only.
- For transformations, allow writes only in a separate output directory.
- Do not mount an entire home directory merely to reach one subfolder.
- Do not mount a broad cloud-storage root when a project-specific folder is sufficient.
- Do not place API keys, SSH keys, browser profiles, password stores, or recovery codes in the workspace.
Read-only access protects against overwriting the originals, but it does not prevent copying sensitive content. If the agent can read a file, assume it can summarize, duplicate, or transmit that content through any other capability available to its environment.
5. Choose an enforced execution boundary
When an agent can execute commands, process untrusted files, install packages, or run generated code, use a backend with a real filesystem boundary: an appropriately configured container, microVM, or hosted sandbox. Verify its mounts, user identity, permissions, network policy, and credential handling.
A local working directory alone is insufficient for the Linux Unix-local backend described by the Agents SDK. Conversely, a container is not automatically safe if it mounts your home directory, exposes the host socket, or runs helper processes on the host.
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6. Restrict network access independently
File confinement does not stop data leaving through the network. Set outbound access to no network, an allowlist of required endpoints, or a controlled proxy whenever the task permits. If the agent only needs local documents, disabling external network access is the clearest policy.
When network access is necessary, document why each destination is allowed. Consider whether DNS, package managers, browser automation, webhooks, or telemetry create additional paths. The security page at OpenAI’s Agents API environments documentation treats network availability as part of the agent’s effective environment.
7. Keep credentials outside the workspace
Long-lived credentials should not be stored in source files, environment files, shell history, or mounted project folders. Prefer short-lived, narrowly scoped tokens supplied through a trusted proxy or secret-management mechanism. Grant only the operations the task requires.
Any secret injected into the execution environment may be readable by agent-generated code. A credential that can read an entire database or cloud account defeats a carefully narrowed file mount. Separate the identity used for the agent from your personal identity, and revoke it after a temporary job when practical.
8. Audit every integration
Inspect shell helpers, browser tools, file-sync services, desktop automation, plugins, and Model Context Protocol (MCP) servers. Determine where each process runs, which operating-system user it uses, which directories it can read, and whether it has independent network access.
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Docker’s security documentation warns that local MCP processes run with host permissions and host isolation. A main agent inside a VM can therefore regain broad host access through an integration running outside that VM. Read Docker’s AI Sandboxes security model for the documented mount and integration behavior.
9. Test before exposing sensitive files
Create two harmless test files: one inside the permitted workspace and one outside it. Ask the agent to open the allowed file, then attempt to access the outside file. Confirm that the first succeeds and the second is denied by the enforcement layer, not merely refused in the agent’s response.
Repeat the test through each integration and with a subprocess if commands are enabled. Also test writes: the agent should be able to create an output where intended, unable to modify read-only inputs, and unable to write elsewhere. This is a verification practice; results depend on the product, operating system, and configuration.
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| Approach | What enforces it | Visible paths | Write scope | Network and credentials | Main limitation |
|---|---|---|---|---|---|
| Prompt or UI workspace | Agent instructions or application convention | Often all paths available to the host process | Host process permissions | Usually whatever the host process can use | Not a security boundary; malicious or buggy code can ignore it |
| Application-level permission | Agent application checks | Paths the application chooses to expose | Depends on the application | Depends on the application and host | Subprocesses or integrations may bypass the check |
| macOS filesystem sandbox | Operating-system sandbox profile, where the product uses it | App container and user-selected locations, subject to permissions | Profile- and permission-dependent | No automatic network isolation in the SDK backend | Do not assume every desktop agent uses Apple’s App Sandbox |
| Linux local process | Normal host permissions only | Any path allowed to the host process | Host permissions | Host network and credentials unless separately restricted | The Agents SDK Unix-local client adds no OS-level confinement |
| Container or microVM | Container or virtual-machine boundary plus explicit mounts | Mounted paths and files created inside the runtime | Mounts can be read-only or writable | Can be blocked or allowlisted; credentials must still be controlled | Broad mounts and host-running integrations can reintroduce access |
| Hosted sandbox | Provider-managed isolated runtime | Files uploaded or mounted into that runtime | Runtime policy | Provider and configuration policy | Verify retention, egress, identity, and integration behavior |
Use the weakest mechanism that meets the task’s risk, but do not call a prompt convention “sandboxing.” For sensitive data or untrusted code, require an enforceable boundary and inspect what crosses it.
Linux: a working directory does not confine a local agent
The Agents SDK’s Unix-local client executes commands as host processes on Linux. Setting cwd, HOME, or a workspace path changes process context but does not restrict filesystem access. A process can still reach any path allowed by its Unix user and other host policies.
Linux offers additional kernel mechanisms, but they must be configured by an application or sandbox runner. The Landlock interface can restrict filesystem actions such as reading, writing, executing, and listing directories. Its documentation explains: “A sandboxed thread can only access a file path if all its enforced policy layers grant the access as well as all the other system access controls (e.g. filesystem DAC, other LSM policies, etc.).” See the Linux kernel Landlock documentation.
Landlock is not a one-click desktop setting. It is a kernel API that software must invoke, and it works alongside ordinary discretionary access controls and other security layers. Files opened before restrictions are enforced are not subject to those later restrictions, so a runner must apply policy before opening sensitive resources. If you do not control the runner, use a container, microVM, or hosted sandbox instead of assuming Landlock is active.
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The SDK documentation describes filesystem restrictions through sandbox-exec for its Unix-local backend on macOS, but it does not provide container-equivalent isolation or network isolation. A file restriction therefore does not automatically prevent the process from contacting arbitrary destinations.
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Apple’s App Sandbox model provides an app container and access to user-selected documents. POSIX permissions and ACLs can still deny operations; Apple documents the model at Accessing files from the macOS App Sandbox. Do not infer that every agent is App-Sandboxed, or that selecting one document grants a complete boundary around every helper process. Check the specific product’s permission and process documentation, then perform the harmless-file test.
Windows: verify the product instead of assuming a recipe
The official sources covered here do not establish a general Windows desktop-agent configuration that applies across products. Windows access controls, AppContainer, WSL, containers, and vendor-specific sandboxes behave differently, so a universal recipe would be misleading.
For a Windows agent, read its current sandbox and filesystem-permission documentation. Identify the Windows account, inherited ACLs, mounted drives, network policy, and integration processes. Test an allowed file, a denied file, writes, and each helper tool before exposing confidential material.
Docker and hosted sandboxes: inspect mounts and escape routes
Docker’s AI Sandboxes documentation describes microVMs in which an explicit workspace mount connects the agent to project files on the host. That mount is the intended file-sharing route: mount only the narrow folder required for the task, and choose read-only mode where possible.
The same security model says the agent has broad privileges inside the VM and warns that local MCP processes may execute outside sandbox isolation with host permissions. A microVM therefore reduces exposure only when its mounts, host interfaces, and integrations are configured deliberately. Avoid mounting a home directory, Docker socket, SSH directory, or other control plane unless the task absolutely requires it.
For a hosted environment, establish what is uploaded, how long it is retained, who can access it, whether outbound traffic is logged or filtered, and how credentials are supplied. A hosted label alone does not answer those questions.
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Network egress
An agent that can read a sensitive file and reach arbitrary web endpoints can potentially transmit the file, even if the filesystem mount is narrow. Disable egress for offline work. Otherwise use an allowlist or proxy and account for package repositories, DNS, browser sessions, webhooks, and update services.
Secrets
Keep personal tokens and broad account credentials outside agent-readable files. Use a separate service identity with minimal scope, short expiration, and an auditable path to the required service. If a proxy performs an action on the agent’s behalf, ensure the proxy validates the operation instead of exposing the underlying master credential.
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MCP and helper processes
An MCP server or shell helper may run on the host even when the visible agent runs in a sandbox. Check its filesystem, network, and user permissions independently. Disable integrations that are not needed for the task, and treat desktop automation and file synchronization as privileged components.
Artifact export
Review generated files before copying them out of the sandbox. Remove embedded secrets, hidden files, metadata, and unintended source excerpts. Export only the final artifacts needed by the next system or person.
A practical decision checklist
- Only trusted, read-only summarization: use a task workspace, read-only inputs, a separate output folder, and no unnecessary integrations.
- Generated code or shell commands: use a container, microVM, or hosted sandbox with explicit mounts; do not rely on a working directory.
- Highly sensitive files: disable network access where possible, keep credentials outside the runtime, and require a tested process boundary.
- Cross-system workflows: review every MCP server, browser, sync agent, and export path as a separate principal.
- Unclear product behavior: stop, consult the current vendor documentation, and run the harmless-file boundary test before granting access.
Troubleshooting common failures
The agent can read files outside the workspace
That usually means the workspace is only a prompt or application setting. Move the task to an enforced sandbox, remove broad mounts, and verify the process identity and inherited permissions. On Linux, a Unix-local process is expected to retain host access unless another confinement layer is configured.
The agent cannot read an allowed file
Check the mount path inside the runtime, read permissions, ACLs, user and group IDs, symlinks, and whether the file was copied after the sandbox started. A parent directory may also need execute permission for traversal. Do not solve the problem by mounting a broader parent directory without reassessing exposure.
The agent can write the source files
Remount inputs read-only or change their permissions, and provide a separate writable output directory. Confirm that a helper process is not writing through a host-mounted path with broader permissions.
A supposedly isolated agent reaches the internet
Inspect the runtime’s egress policy, proxy variables, DNS, browser integration, and host-side helpers. Filesystem isolation does not imply network isolation; configure both.
A local integration bypasses the sandbox
Identify where the integration process runs and which account it uses. Disable it, move it into the isolated runtime, or apply an equivalent boundary to that process. Docker specifically documents the risk of local MCP processes retaining host permissions.
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Final operating procedure
- Create a fresh task folder containing only required inputs.
- Mount inputs read-only and expose a separate output directory.
- Use an enforced sandbox for command execution or untrusted content.
- Disable or allowlist network egress.
- Keep credentials outside files the agent can read; provide narrowly scoped access through a proxy or secret mechanism.
- Audit MCP servers, shells, browsers, sync tools, and export paths.
- Test allowed and denied files, writes, network behavior, and integrations with harmless data.
- Review artifacts, then remove the workspace and revoke temporary access.
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