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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Running an AI agent on your own computer does not automatically protect your personal files. On Linux, a process launched from a project directory can generally read any file and use any network connection that your user account is allowed to access. A folder name, current working directory, HOME variable, or workspace label is not an operating-system boundary.
To keep private data out of an agent’s reach, you need an enforceable execution boundary, deliberately limited workspace sharing, separate network controls, and a review step before running anything the agent changes. The safest practical pattern is to give the agent a mountless workspace or a private clone inside a sandbox, disable networking unless it is required, avoid unnecessary credentials, and inspect generated files before executing them.
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What “local” does—and does not—mean
“Local” describes where some part of the software runs. It does not describe what that software can access. A locally running agent may include a language model served on your machine, an orchestration process using your operating-system account, shell tools, file APIs, browser automation, or integrations that send data to external services. Each component can have a different boundary.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIf an agent process runs directly on Linux under your account, it normally inherits that account’s filesystem permissions. Starting it in /home/alex/projects/site does not prevent it from opening /home/alex/Documents, SSH keys, browser profiles, cloud-sync folders, or other readable paths. Setting HOME to a temporary directory changes an environment variable; it does not revoke permissions. A prompt such as “only use this project” is an instruction to the model, not a kernel-enforced restriction.
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The same distinction applies to network access. A model or tool can be local while its process still makes outbound requests. Conversely, a sandbox can isolate files while allowing selected network traffic through a proxy. Treat filesystem access, network egress, credentials, and host integrations as separate controls.
Start with a concrete threat model
Before choosing a runtime, write down what the agent must do and what it must never see or change. This prevents a broad “sandbox” label from hiding an unsafe mount or credential.
- Data: Which repository, test fixtures, documents, and generated artifacts are required? List sensitive paths that must remain outside the boundary.
- Actions: Does the agent need to edit files, run a compiler, install packages, start services, access Git, or invoke a browser?
- Trust: Are the instructions and files entirely yours, or can the agent process untrusted prompts, pull requests, packages, or downloaded content?
- Network: Is internet access necessary? If so, which destinations and protocols are actually needed?
- Secrets: Can the task work without API keys, cloud credentials, SSH keys, signing keys, or personal tokens?
- Integrations: Do MCP servers, editor plugins, Git helpers, or other tools execute inside the same boundary?
Use the narrowest capability that completes the task. If a copy of a file is sufficient, stage a copy rather than sharing the original path. If a path must be shared, prefer the smallest directory and read-only access that your selected runtime can enforce.
Compare execution choices before you start
| Execution choice | Host filesystem reach | Workspace behavior | Network and credentials | When it fits |
|---|---|---|---|---|
| Direct local process on Linux | Host permissions of the user account; no operating-system confinement supplied by the local SDK path | Reads and writes any permitted host path | Uses the host’s normal network and environment unless separately restricted | Trusted development where convenience matters more than isolation |
| Sandbox, mountless | Unshared host files remain outside the sandbox boundary | Agent works in the sandbox’s own filesystem; no host workspace is mounted | Configure network independently; do not assume it is disabled | Untrusted or exploratory work that does not need a host checkout |
| Sandbox, clone mode | Repository is exposed as a read-only source mount; working edits stay in a private in-VM clone | Changes do not reach the host until you deliberately fetch or copy them | Configure egress and credentials separately | Code tasks requiring a starting repository with delayed host writes |
| Sandbox, direct mode | Only explicitly shared paths are exposed, but the shared working tree is live | Read-write host mount; agent edits appear on the host immediately | Independent network and credential decisions still apply | Trusted workflows that need immediate edits and accept the review burden |
| Hosted or external isolation | Depends on the provider’s documented boundary and mounts | Usually a separate workspace; verify persistence and export paths | Provider policy determines egress and secret handling | Tasks where local host exposure is unacceptable |
The table describes documented behavior for the relevant local SDK and Docker Sandbox paths, not a guarantee that every agent framework or container setup behaves identically. Check the current documentation for the runtime you intend to use.
Why SDK file grants are not a Linux sandbox
Some agent SDKs let an operator grant specific paths to their file APIs. Those grants can be useful for deciding which paths an SDK-managed read or write call may use. On Linux, however, they do not necessarily confine arbitrary shell commands. A command such as cat /home/alex/Documents/tax.pdf or find /home/alex is executed by the operating system and may still use the host permissions of the process.
Treat path grants as trusted operator configuration. Do not let the agent rewrite its own grant list, and do not describe the list as a complete security boundary unless the runtime documents an operating-system or virtual-machine restriction that covers every tool the agent can invoke.
Build a safer workspace step by step
1. Inventory the task
Remove capabilities before you add them. Decide whether the agent really needs a full Git history, package caches, dotfiles, test data, or access to a local service. Many coding tasks need only source files and a language toolchain. A documentation task may need no shell access at all.
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- Write the required input directories and output directories.
- Mark every path containing personal data, credentials, or unrelated projects as forbidden.
- List commands the agent must run and commands it must not run.
- Decide whether generated changes can be reviewed before they touch the host.
2. Stage a copy when a copy is enough
Create a dedicated working copy containing only the files needed for the task. Exclude home-directory configuration, private keys, cloud-sync folders, browser data, and unrelated repositories. A copy also gives you a clean recovery point: you can delete the workspace without affecting the original.
If you must share a path, use the narrowest directory rather than an entire home directory or filesystem root. Prefer read-only exposure for inputs. A read-only mount protects the contents of that mount from writes, but it does not by itself prevent an agent from writing elsewhere inside its sandbox or from using network tools.
3. Choose an enforceable boundary
For trusted development, a direct local process may be acceptable, but call it convenience, not filesystem isolation. The OpenAI Agents SDK documentation states that Unix-local execution on Linux runs with host permissions and receives no OS-level confinement through that path.
For commands influenced by untrusted content, use a configured Docker Sandbox, another VM-backed environment, or an equivalent external isolation mechanism. Review its mounts, credentials, persistence, and network policy before handing it work.
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4. Select mountless, clone, or direct workspace sharing
Docker documents three practical patterns:
- Mountless: no host workspace is shared. The agent creates and edits files in the sandbox’s own filesystem. This gives the clearest separation when you can provide inputs another way.
- Clone mode: the repository is mounted read-only as a source, while the agent works on a private clone inside the virtual machine. Its edits remain private until you choose to fetch or copy them out.
- Direct mode: the host working tree is mounted read-write. The agent can change files that are immediately visible on the host.
For a first run involving untrusted instructions, mountless or clone mode is the safer default because the agent cannot silently modify your primary checkout through the workspace mount. If direct mode is necessary, use a disposable working copy and plan a file review before any build, test, hook, or deployment command.
5. Configure the network independently
Filesystem isolation does not stop data from leaving over the network. If the task does not require networking, disable it in the runtime. The OpenAI Agents SDK Docker client documents network_mode="none"; this disables networking and prevents ports from being exposed.
from agents import Agent, Runner
from agents.run import RunConfig
from agents.sandbox import DockerSandboxConfig
sandbox = DockerSandboxConfig(
network_mode="none",
)
agent = Agent(
name="local-coding-agent",
instructions="Work only on the files supplied in the sandbox.",
)
# Pass the sandbox configuration using the SDK's current runner API.
# Exact constructor names can change; verify them against your installed version.
result = Runner.run_sync(
agent,
"Inspect the supplied project and prepare a patch.",
run_config=RunConfig(sandbox=sandbox),
)
The snippet illustrates the documented network setting, not a universal API. Agent frameworks expose different configuration names, and you should confirm the syntax for the version you installed.
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When network access is required, make the policy explicit. Docker Sandboxes describe outbound TCP through a host-side network-policy proxy. Inspect which destinations are allowed instead of assuming the default is minimal. Restrict access to the domains, ports, and protocols needed for the task, and remember that a permitted destination can still receive sensitive data if a tool sends it.
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Do not place SSH private keys, cloud tokens, package-registry credentials, signing keys, or a broad environment dump in the sandbox unless the task genuinely requires them. Use short-lived, least-privileged credentials when a credential is unavoidable, and remove it after the run.
Docker documents a proxy path that injects credentials into outbound HTTP request headers so raw credential values do not enter the virtual machine. That behavior belongs to the described Docker Sandbox setup; it should not be generalized to every local runtime, container, or proxy. Verify how your chosen environment handles secrets before relying on it.
7. Audit host-side integrations
A virtual machine boundary covers only processes running inside that boundary. Docker states that local stdio MCP servers run outside the sandbox virtual machine and use host permissions and isolation. An agent in a VM can therefore invoke a host-side server whose capabilities are broader than the VM itself.
Inventory every integration the agent can call. Read its launch command and configuration, identify the files and services it can reach, and disable integrations that are not needed. If an MCP server can read a home directory, control a browser, or execute arbitrary host commands, treat that server as part of the host attack surface even when the agent’s main process is sandboxed.
Understand what a Docker Sandbox actually contains
Docker describes its Sandboxes as microVMs with their own Linux kernel. Inside the VM, the agent has broad control, including the ability to use sudo. That is not a claim that the agent is harmless; it means the containment comes from the VM boundary and from the host resources you did not share with it.
Sandbox contents persist across restarts until the sandbox is removed. Persistence is useful for keeping dependencies and intermediate files, but it also means a malicious or accidental change can remain for a later run. Delete disposable sandboxes when the work is complete, or periodically recreate them from a known-clean definition.
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Check these settings before starting:
- Which host paths are mounted, and with what read/write mode?
- Is the source repository a read-only mount or a live working-tree mount?
- Are ports exposed to the host?
- Is outbound traffic disabled or filtered by a documented policy?
- Which environment variables and credentials are injected?
- Which tools execute outside the VM?
- How is state removed, exported, or reviewed?
Review agent changes before running them
Code review is necessary even when the agent appears to have worked only on an ordinary source file. Agents can modify files that execute implicitly later, including Git hooks, CI configuration, editor task settings, Makefiles, package scripts, shell profiles, and build definitions.
Direct workspace mode makes this risk immediate because the host working tree changes as the agent works. Clone mode provides a safer handoff: inspect the private clone, then fetch only the changes you approve.
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- List all changed and untracked files, including hidden files.
- Inspect Git hooks separately. Docker notes that hooks are not shown by
git diff. - Read build scripts, package scripts, CI files, task definitions, and generated shell commands line by line.
- Search for new network calls, credential access, encoded payloads, shell execution, and changes to startup files.
- Run tests in the sandbox first, with networking disabled where possible.
- Copy or fetch only approved files into the host checkout.
Do not execute an agent-written installer, hook, migration, or deployment script merely because it is present in the expected directory. The review boundary is part of the security design, not an optional final polish step.
Common mistakes and how to correct them
“I set the working directory, so the agent cannot see anything else.”
A working directory controls relative paths, not absolute-path permissions. Run the task in a VM-backed sandbox or another OS-enforced boundary, and share only the required paths.
“The SDK granted one directory, so shell commands are confined there.”
SDK file grants may govern SDK APIs while Linux shell commands retain host permissions. Remove shell access, use a runtime that confines the shell at the OS or VM level, or treat the process as fully trusted.
“A container means my host files are safe.”
Safety depends on mounts, privileges, integrations, and the container technology. A read-write workspace mount deliberately gives the agent host-file write access. Inspect the actual configuration rather than relying on the word “container.”
“The VM protects me even though I shared my home directory.”
Anything explicitly shared is inside the agent’s effective reach. Replace a home-directory mount with a staging directory containing only the needed files, preferably read-only.
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“Network access is unrelated to file privacy.”
An agent can read a permitted file and transmit its contents without modifying it. Disable networking for offline tasks; otherwise use a narrow, reviewed egress policy.
“The agent is sandboxed, so its MCP tools are sandboxed too.”
Host-side stdio MCP servers can run outside the VM with host permissions. Review and isolate each server independently.
“Git diff is a complete review.”
Git hooks and some untracked or hidden files may not appear in a normal diff. Inspect hooks and the complete working tree before running project commands.
A repeatable checklist
Before launch
- Use a disposable project copy or a private clone.
- Remove personal files, dotfiles, credentials, and unrelated repositories.
- Choose mountless, clone, or direct mode deliberately.
- Disable network access unless a documented task requirement exists.
- Remove unnecessary environment variables and host integrations.
- Confirm which paths are read-only and which are writable.
During the run
- Watch for requests to expand mounts, read secrets, or enable broad network access.
- Keep the agent inside the intended workspace and tool set.
- Stop the run if it attempts unrelated file or credential access.
- Remember that sandbox state can persist between restarts.
After the run
- Review all modified, untracked, hidden, and hook files.
- Run tests inside the sandbox before importing changes.
- Fetch or copy only approved files from a clone workspace.
- Delete disposable sandboxes and revoke temporary credentials.
- Recheck network and integration settings before the next task.
Choosing the right level of isolation
Use direct local execution when the agent, its prompts, its dependencies, and its inputs are trusted and the convenience of immediate host access is intentional. It is not suitable when merely reading a file could expose personal data or when untrusted content can influence shell commands.
Use mountless mode when the agent can work from generated inputs or a separately staged archive. Use clone mode when it needs a repository but you want changes isolated until review. Use direct mode only when immediate host edits are worth the increased exposure and you have a dedicated checkout plus a disciplined review process.
For every option, verify the same six boundaries: host filesystem reach, workspace write behavior, network egress, credential exposure, host-side tools, and persistence or review workflow. A setup is only as strong as its broadest unreviewed capability.
Bottom line
To run an AI agent locally without exposing personal files, do not rely on “local,” a prompt, cwd, HOME, or an SDK path grant. Give the agent a minimal staged workspace, place untrusted work in a VM-backed sandbox, prefer mountless or clone mode, disable networking when possible, keep credentials out, audit host-side integrations, and review every changed executable file before it runs. Those controls turn a convenient local process into a deliberately bounded workflow.
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