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Yes, you can run an AI agent on your computer without giving it your personal files—but choosing “local” or starting it in a project folder is not enough. On Linux, a locally started process normally has the same filesystem and network permissions as your user account. Real protection requires an operating-system or virtual-machine boundary, narrowly chosen workspace sharing, separate network controls, and a review step before you execute anything the agent writes.
This guide explains the practical setup, the limits of SDK permissions, Docker sandbox workspace modes, network and credential decisions, host-side integrations, and a safe workflow for reviewing agent changes.
What “local” does—and does not—protect
“Local” describes where some component runs. It does not describe what that component is allowed to read, modify, or contact. An agent process running on your machine can often use every file, credential, device, and network connection available to the operating-system account that launched it.
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Starting the agent with a project directory as its current working directory (cwd) does not turn that directory into a security boundary. The same is true of setting HOME, naming a folder “workspace,” or instructing the model in a prompt to “only use this directory.” Those choices provide context and convenience, not enforceable containment. A program can change directories, open an absolute path, inspect parent directories, read environment variables, or invoke another tool unless the operating system or a sandbox prevents it.
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The first question is therefore not “Is the model local?” It is: What can the agent process access, what is shared with its runtime, and which tools run outside that runtime?
Why a project directory is not isolation on Linux
A Unix-local process normally receives the host permissions of the user who starts it. If that user can read a personal document, SSH configuration, browser profile, cloud-sync folder, or another repository, the process may be able to read it too. Network sockets are governed separately from the current directory, so a process can also send data to destinations permitted by the host firewall and its account.
The OpenAI Agents SDK documentation describes Unix-local execution on Linux as having no OS-level confinement. That makes local execution convenient for trusted development, but it should not be presented as filesystem isolation for commands influenced by untrusted files, prompts, or generated code.
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Some agent SDKs offer filesystem grants such as an extra_path_grants list. These grants can control access through the SDK’s own file APIs, which is useful for expressing the paths an agent is expected to use. They are not automatically a kernel-enforced jail for every process the agent can launch.
In particular, the SDK guidance warns that Linux shell commands are not confined by those grants. Treat grant lists as trusted operator configuration: define them yourself, keep them narrow, and never let an agent expand them at runtime. If shell access is available, use a stronger execution boundary rather than assuming an API-level allow-list covers arbitrary commands.
The security model you should build
A safe local-agent setup has several independent controls. Each answers a different failure mode; none substitutes for the others.
- Execution boundary: run untrusted or uncertain commands in a VM, container sandbox with an appropriate isolation mechanism, or hosted sandbox rather than directly on the host.
- Workspace sharing: expose only the files required for the task, and choose whether they are absent, read-only, privately cloned, or live read-write.
- Network policy: disable network access when it is unnecessary, or restrict and inspect the destinations when it is required.
- Credentials: keep tokens, SSH keys, browser cookies, and cloud credentials out of the agent environment unless the task genuinely requires them.
- Host integrations: audit MCP servers and other adapters; an integration that runs on the host is not contained merely because the agent itself is in a VM.
- Change review: inspect generated files and hidden execution points before running them on the host.
Choose an execution boundary that matches the risk
Trusted local development
Direct local execution is reasonable when you trust the agent, the prompt and repository contents, and every command it may run. It is usually the fastest option and avoids synchronization overhead. It should still be treated as host execution: the process has the permissions of your account, and a prompt instruction is not a security control.
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Do not use this mode for commands influenced by untrusted pull requests, downloaded archives, unknown plugins, or files you have not inspected. A coding agent that can execute shell commands can also run build scripts, package hooks, and arbitrary binaries present in the workspace.
Docker or another VM-backed sandbox
For untrusted commands, use a runtime that provides an actual isolation boundary. Docker describes its Sandboxes as microVMs with their own Linux kernel. The agent has broad control inside that VM, including the ability to use sudo; containment comes from the VM boundary and from the resources you do not share with it, not from the agent being harmless inside the VM.
Sandbox state persists across restarts until the sandbox is removed. Persistence is useful for a long-running project, but it means old files, packages, and configuration remain available to later sessions. Remove the sandbox when you no longer need its contents, especially after processing sensitive material.
Hosted or externally isolated execution
A hosted sandbox or another external isolation service can separate execution from your personal computer. Evaluate it using the same questions: which files are uploaded or mounted, how credentials are handled, whether outbound network access is allowed, who can access the workspace, and how long state is retained. The product-specific behaviors described below apply to the documented Docker and OpenAI Agents SDK paths; other frameworks may use different controls.
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Understand Docker workspace modes before sharing a repository
Docker documents three workspace patterns. The choice determines whether the agent can see host files and whether its edits become live immediately.
| Mode | Host filesystem reach | How edits behave | Best use |
|---|---|---|---|
| Mountless | No host workspace is shared; the agent uses the sandbox’s own filesystem. | Changes stay in the sandbox. | Experiments or tasks that do not need an existing checkout. |
| Clone | The repository is mounted read-only for transfer into the sandbox. | The agent edits a private in-VM clone. Changes stay private until you deliberately fetch them. | Coding tasks where you want a review gate before host changes. |
| Direct | The host working tree is shared read-write. | Edits appear on the host immediately. | Trusted work where instant synchronization is worth the increased exposure. |
For a first run, choose mountless mode when no source tree is needed, or clone mode when the agent must work on a repository. Direct mode turns the shared working tree into part of the agent’s effective write access. If you must use it, create a dedicated working copy, keep personal files outside the shared path, and inspect every change before executing anything.
Configure a least-privilege workspace
1. Inventory the task’s real inputs
Write down the files and tools the agent actually needs. A documentation edit may need only a copied set of text files. A test run may need source code and a test database, but not your home directory, cloud credentials, or browser profile. If a copy is sufficient, stage a copy into the sandbox instead of mounting the original path.
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2. Share the narrowest path
When a path genuinely must be shared, expose that path rather than its parent. Prefer read-only access for inputs and a separate output directory for generated files. Do not mount an entire home directory to provide one configuration file. Check whether the runtime resolves symlinks, follows bind mounts, or exposes parent directories, and account for those behaviors in your design.
3. Keep grants under operator control
Define SDK grants or mount settings before starting the agent. Treat them as trusted configuration, not model-editable data. An agent should be able to request access for you to approve, but it should not be able to rewrite its own allow-list or mount additional host paths.
4. Separate sensitive material
Keep personal documents, password stores, SSH keys, API tokens, browser data, and unrelated repositories outside the workspace. Environment variables are also inputs: remove secrets from the environment unless the task needs them. Remember that a process can often inspect its environment even when a particular file path is not mounted.
Control network access independently
A filesystem boundary does not stop network exfiltration. An agent that cannot read a host file may still send the data it can read to an external service, download additional code, or open a listening port if networking is enabled.
Disable networking when the task does not need it
The OpenAI Agents SDK Docker client documents network_mode="none" for disabling networking and preventing exposed ports. Use the equivalent setting in your runtime, and verify that it applies to the actual sandbox in which commands execute. A disabled network is especially appropriate for local refactoring, offline tests, and work on sensitive source code.
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Some tasks require package downloads, an internal API, or a test service. Enable only what the task needs and inspect the policy rather than relying on a default. Docker Sandboxes describe outbound TCP traffic passing through a host proxy and being allowed according to network policy. Review that allow-list and narrow it where possible; “network enabled” should not mean unrestricted internet access.
Network policy should cover both outbound connections and inbound exposure. A tool that starts a development server may bind a port inside the sandbox, but that does not make it safe to publish the port to your host or local network without checking the runtime’s forwarding rules.
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Audit credentials and host-side integrations
Credentials inside the sandbox
The safest credential is one the agent never receives. Use short-lived, task-specific tokens when authentication is unavoidable, give them the minimum scope, and revoke them after the task. Do not copy your primary SSH key, cloud credential file, password manager database, or browser session into a workspace.
Docker documents a proxy that can inject credentials into outbound HTTP request headers so raw credential values do not enter the VM. That behavior is specific to the described Docker Sandbox path; do not assume every local container, SDK, or proxy provides it. Confirm exactly which requests are covered and whether logs, error messages, or subprocesses can still reveal the token.
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Local stdio MCP servers
Docker states that local stdio MCP servers run outside the sandbox VM and use host permissions and isolation. This is a critical boundary crossing: the agent may be sandboxed while the MCP server it invokes is a normal host process.
Before enabling an MCP server, inspect its executable, configuration, filesystem access, network behavior, and credentials. Remove servers that are not needed for the task. If an integration must handle sensitive data, place it behind an explicitly designed service boundary instead of assuming the agent VM contains it.
A safe end-to-end procedure
- Classify the input. Mark the prompt, repository, dependencies, and generated artifacts as trusted, uncertain, or untrusted. Treat downloaded code and unreviewed pull requests as untrusted.
- Pick the boundary. Use host execution only for trusted work. For uncertain or untrusted commands, choose a VM-backed or hosted sandbox and verify its isolation documentation.
- Create a clean workspace. Use mountless mode when possible. Otherwise use clone mode or a disposable dedicated checkout. Keep personal files outside the shared path.
- Set sharing permissions. Mount only required paths, make inputs read-only where feasible, and provide a separate output location. Do not grant a parent directory merely to reach one file.
- Set network policy. For offline work, configure
network_mode="none"or the runtime’s equivalent. For online work, list required destinations and inspect the policy before starting. - Remove unnecessary secrets. Start with a minimal environment. Add a narrowly scoped, short-lived credential only when the task cannot proceed without it.
- Audit integrations. List every MCP server, plugin, or tool adapter. Confirm whether each runs inside the sandbox or on the host and what permissions it receives.
- Run the agent. Give it explicit task boundaries, but treat the prompt as guidance rather than a security mechanism. Log commands and outputs if your runtime supports it.
- Review the diff and filesystem. Inspect source changes, new files, permissions, symlinks, build scripts, package scripts, CI files, IDE tasks, and configuration changes. Check hidden directories and Git hooks separately because hooks are not shown by
git diff. - Test outside the sensitive environment. Run generated code first in the sandbox or another disposable environment. Only after review should you fetch clone-mode changes or copy selected artifacts to the host.
- Clean up. Revoke temporary credentials, remove unnecessary artifacts, and delete the sandbox when its persistent state is no longer needed.
Files that deserve special review
An agent does not need to modify a personal document directly to affect your host. It can change a file that will execute later, often without an obvious prompt.
- Git hooks: scripts under the repository’s hooks directory can run on commits, merges, or checkouts and are not included in ordinary
git diffoutput. - Build and task files: Makefiles, task-runner definitions, IDE launch settings, and CI workflows can execute commands automatically.
- Package metadata: install and post-install scripts may run when dependencies are installed or updated.
- Shell and environment files: startup scripts and local environment files can alter later commands or expose secrets.
- Symlinks and permissions: inspect links, executable bits, ownership, and files outside the expected project tree.
Review the actual filesystem, not only the visible source diff. In direct mode, the agent’s edits are already live on the host; in clone mode, review before fetching; in mountless mode, review before copying artifacts out.
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“I set the working directory, but the agent read my home folder.”
That is expected for an unconstrained local process. Replace directory conventions with an OS or VM boundary, and remove broad home-directory mounts.
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“The SDK grant list blocked its file API, but a shell command still read another path.”
SDK grants are not necessarily shell confinement on Linux. Disable shell access, run the command in a properly isolated sandbox, or use a runtime that enforces filesystem policy at the operating-system boundary.
“The sandbox is isolated, but the agent still reached the internet.”
Check network configuration separately from mounts. Set the documented no-network option when appropriate, or inspect and narrow the sandbox’s outbound policy and proxy behavior.
“A VM protected the files, but an MCP tool changed the host.”
Inspect the integration’s execution location. A local stdio MCP server may run outside the VM with host permissions. Disable it or replace it with an integration that has an independently reviewed boundary.
“I reviewed the diff and missed a script that later ran.”
Inspect Git hooks, hidden files, generated configuration, package scripts, CI definitions, and executable permissions separately. A normal diff is not a complete execution-surface review.
“I deleted and recreated the sandbox, but old files were still there.”
Sandbox contents persist across restarts. Remove the sandbox itself when you need a clean state, and verify that no host-mounted path or external volume is preserving the data.
Decision table: which setup fits your task?
| Scenario | Recommended workspace | Network | Review requirement |
|---|---|---|---|
| Trusted edit in a private repository | Local execution or direct mode, if you accept host write access | Only what the tools require | Review generated scripts before running them |
| Untrusted repository or downloaded code | Clone mode in a VM-backed sandbox | Disabled unless a dependency requires it | Review before fetching changes to the host |
| Offline transformation of sensitive files | Mountless mode with staged copies | network_mode="none" or equivalent |
Review outputs before copying them out |
| Task needing package downloads | Clone or mountless sandbox | Enabled under a narrow, inspected policy | Check downloaded code and credentials handling |
| Agent calling a local MCP server | Any mode, but audit the host-side server separately | Apply policy to both agent and server | Review the server’s host permissions and logs |
How to verify the boundary before trusting it
Documentation tells you the intended behavior, but your configuration can still be wrong. Perform a harmless verification in a disposable workspace before processing personal data.
- Confirm the agent can see the intended project path and cannot list an unshared personal path.
- Check whether the runtime follows symlinks out of the workspace.
- Verify that a no-network setting prevents outbound connections and port exposure.
- List environment variables available to the agent and remove unexpected secrets.
- Identify every process started on the host, including MCP servers and proxy helpers.
- Restart the sandbox and confirm what persists; remove it and confirm what is deleted.
Do not treat a failed probe as proof of universal safety. Isolation depends on the operating system, runtime version, mount configuration, credentials, and integrations you actually use. Recheck the current documentation when any of those change.
The practical bottom line
To run an AI agent locally without exposing personal files, put the agent in a real VM-backed or equivalent sandbox, share only a dedicated project copy, disable networking unless required, keep credentials out of the environment, and audit host-side integrations. Use clone or mountless workspace modes for cautious work; reserve direct read-write mounts for trusted tasks. Finally, review files—including hidden hooks and scripts—before executing or importing them onto your host. “Local” is a location, not a security boundary.
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