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Use layered controls rather than trusting the model. A safe setup gives an AI coding agent an isolated execution environment, exposes only the project files it needs, blocks network traffic by default, keeps credentials in a separate trusted harness, and requires approval for actions that cross the boundary. For a solo developer, the agent’s operating-system sandbox can be sufficient for low-impact work. Untrusted code, shared services, production data, and unattended jobs warrant a fresh per-task container or dedicated virtual machine with explicit mounts, egress rules, and audit logs.
The key distinction is that a sandbox is an enforced execution boundary, not a promise that an agent will behave. Agent-generated code can use every file, credential, command, and network route available inside that boundary, so the boundary must be designed before the agent runs.
What does a sandbox protect when an AI coding agent runs commands?
A sandbox limits the consequences of model-directed commands. It can constrain which files are visible, where writes are allowed, which processes and system calls are available, whether outbound connections work, and which credentials can be presented. The harness outside the sandbox should own authentication, approvals, audit records, snapshots, and recovery; the sandbox should execute the commands and code the model requests.
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OpenAI describes a sandbox session as an isolated, Unix-like environment with a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled access to external systems. Microsoft describes two separate isolation dimensions for coding agents: filesystem isolation for configured paths and network isolation for outbound connections or approved domains. Treat those as independent controls: a read-only filesystem does not stop data exfiltration, and a blocked network does not stop an agent from reading a mounted secret.
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- Confidentiality: keep SSH keys, cloud tokens, production configuration, personal files, and other repositories outside the visible mount.
- Integrity: limit writes to a disposable checkout or explicitly approved project paths.
- Availability: prevent runaway processes, disk consumption, fork bombs, and destructive commands from affecting the host.
- Accountability: record commands, approvals, network decisions, and the final state so a job can be investigated or rolled back.
Approval prompts complement technical isolation; they do not replace it. If an agent can already read a secret or reach production, waiting for a prompt after the fact is not a reliable containment strategy.
Which execution boundary should you choose?
Choose the smallest boundary that matches the data and impact of the task. A local OS sandbox is convenient for a trusted developer working on a non-sensitive repository. A per-task container adds reproducibility and a clean reset. A hosted sandbox or dedicated virtual machine is preferable when jobs are untrusted, multi-user, internet-facing, or able to affect valuable systems.
| Option | Isolation strength | Filesystem and mounts | Network control | Credentials | Reset and reproducibility | Multi-tenant separation | Audit and approvals | Typical cost and startup |
|---|---|---|---|---|---|---|---|---|
| Local OS sandbox | Bound by the operating system and agent runtime; suitable for lower-impact work when configured correctly | Workspace paths can be read/write; home directories and unrelated paths should remain unavailable | Often configurable as blocked, prompted, or allowed; verify the runtime’s exact behavior | Keep secrets outside the workspace; do not assume the OS sandbox hides every credential source | Fast startup; rollback depends on version control or local snapshots | Weak unless each user has a separate OS account and workspace | Agent UI can surface approvals; host-level logging may be limited | Lowest operational overhead and near-zero extra startup cost |
| Per-task container | Process and filesystem isolation with a smaller blast radius than the host, but the container runtime and kernel remain part of the trust boundary | Mount only the checkout and required data; use read-only mounts wherever possible | Can start with no network and add an explicit hostname allowlist through a proxy or controlled gateway | Inject short-lived, destination-limited credentials through a broker, never into the image or source tree | Disposable images, pinned dependencies, and snapshots make reruns consistent | Use a separate container, volume, and identity per user or workload | Harness can capture command, network, and approval events centrally | Moderate setup and seconds-to-minutes startup, depending on image size |
| Hosted sandbox or dedicated VM | Strongest practical separation when it is freshly provisioned and patched; a VM adds a hypervisor boundary | Attach only task data, with explicit read/write permissions and automatic teardown | Worker or proxy can enforce a hostname allowlist independent of the task code | Secrets stay in a vault or trusted control plane and are released per request | Images, snapshots, and per-task lifetimes support clean recovery | Separate VM or strongly isolated runner for tenants that must not share data | Centralized logs, approval workflows, and operator recovery are easier to enforce | Highest cost and latency; requires service operations and patching |
The container row is not automatically safe. Mounts, Linux capabilities, privileges, egress, identity, and credential handling determine the actual blast radius. A privileged container with the host filesystem mounted can be more dangerous than a carefully configured local workspace sandbox.
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How do you scope the filesystem?
Start by making a list of the files the task truly needs, then mount only those paths. The repository, a generated-artifact directory, and a small cache are common examples. Keep the host home directory, SSH configuration, cloud credential directories, production configuration, browser profiles, and unrelated repositories outside the boundary.
Use a disposable checkout
Clone or copy the project into a directory created for the job. If the agent only needs to inspect code, mount the checkout read-only. If it must edit files, allow writes to that checkout and nowhere else. A separate output directory makes it easy to review generated artifacts before copying them into a trusted project.
Separate data by sensitivity
- Put test fixtures containing real personal or customer data in a different, minimized mount; prefer synthetic data.
- Do not mount
~/.ssh, cloud-provider credential folders, password stores, private package-manager configuration, or CI secrets. - Keep production configuration and deployment keys in the harness or deployment system, not in the agent’s working directory.
- Use read-only mounts for reference documentation, dependency caches, and datasets unless the agent must modify them.
Example container boundary
The following is a starting pattern, not a complete security policy. It mounts one project, makes the root filesystem read-only, drops Linux capabilities, blocks privilege escalation, and disables networking. Adapt the image, user ID, and paths to your platform and test every assumption.
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docker run --rm
--read-only
--network none
--cap-drop ALL
--security-opt no-new-privileges:true
--pids-limit 512
--mount type=bind,src=/srv/agent-work/project,dst=/workspace,rw
--workdir /workspace
agent-image:tagDo not add the host root directory, the Docker socket, device files, or broad writable volumes merely to make a tool work. If a build needs a compiler cache or temporary directory, create a narrowly scoped volume and decide whether it should be retained after the task.
How do you control network access?
Begin with no network. Then add only the package registries, source hosts, model gateways, or APIs required by the task. An explicit hostname allowlist is safer than allowing arbitrary IP addresses or unrestricted HTTPS, because malware can exfiltrate through any reachable service.
Design the egress path
- List every external dependency the build or test actually uses, including package registries, source-control hosts, license servers, and the model endpoint.
- Resolve whether each dependency can be mirrored internally. A private registry or cache reduces both exposure and accidental data transfer.
- Route allowed traffic through a proxy or gateway that can authenticate the task, enforce hostnames, limit methods, and record requests.
- Keep DNS under the same policy. Blocking a domain at an HTTP layer is ineffective if the task can resolve and connect directly to arbitrary addresses.
- Set timeouts, connection limits, and bandwidth limits so a buggy process cannot consume the host or turn the runner into a scanner.
A hosted runner pattern can let the trusted controller decide which hostnames the task container may reach. If the task needs to download a package, prefer a one-time approval or a pre-populated cache over permanently opening the network.
What network access should trigger approval?
Require an approval for a new destination, a request to contact an internal service, an upload, a port exposure, or a change from read-only package retrieval to arbitrary API calls. The approval should identify the destination, purpose, duration, and credential scope. Do not approve a vague request such as “enable internet”; approve a specific route for a specific task.
How should secrets and credentials be handled?
Keep long-lived application keys in the trusted harness, a vault, or a broker. Never bake them into an image, commit them to source code, place them in prompts, or write them to logs. Injecting a secret into the sandbox makes it available to agent-generated code, including code that the model did not intend to run.
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A broker can exchange a task identity for a short-lived token limited to one API, one hostname, one operation, or one environment. The sandbox calls the broker through an approved route; the broker checks the task’s policy and returns only the minimum credential. Revoke the token when the task ends or when an approval is withdrawn.
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Prevent accidental leakage
- Redact secrets from command output, exception traces, debug archives, and audit events.
- Use separate identities for development, testing, staging, and production; an agent that needs to run tests should not receive a production deployment key.
- Disable ambient credentials inherited from the host, including metadata-service access and default cloud profiles.
- Scan generated files and patches for tokens before they leave the sandbox.
- Assume that any secret visible to the process can be copied into a file, encoded, or sent over an allowed connection.
How do approvals fit with sandboxing?
Set the sandbox mode first, then define when the agent must stop and ask. Use on-request approvals for writes outside the project, network access, privileged operations, deployments, repository pushes, and destructive commands. The approval policy is a human decision point; the sandbox remains the technical backstop if a command is misclassified or an approval is granted too broadly.
Make prompts specific
A useful prompt states the command, affected paths, destination host, requested credential, expected side effect, and whether the permission is one-time or persistent. “Run the test suite with the package cache mounted read-only” is reviewable; “allow everything needed” is not.
Separate ordinary and high-impact actions
- Usually pre-approved: reading the mounted repository, compiling, formatting, and running tests that do not require external access.
- Review individually: installing a new dependency, opening a network route, writing outside the checkout, or changing a build script.
- Require an explicit operator: production access, credential issuance, deployment, force-push, data deletion, host administration, and disabling security controls.
How do you drop privileges and isolate users?
Run the executor as an unprivileged user with a dedicated user and group ID. Remove unnecessary Linux capabilities, deny privilege escalation, apply process and resource limits, and keep the host kernel and container runtime patched. Permission profiles can constrain commands, but they do not replace process isolation or a sound recovery plan.
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For multiple users or workloads that must not share data, give each task a separate workspace, volume, identity, cache policy, and audit stream. Do not reuse a writable container or its filesystem between tenants. Destroy the environment after the task unless a reviewed snapshot is required for debugging.
What is a practical setup sequence?
Use this sequence for a new project, then record the decisions in the repository or runner configuration so a later operator can reproduce them.
- Choose the boundary. Use the agent’s OS-enforced read-only or workspace-write mode for a single developer and low-impact code. Use a fresh container or VM for untrusted code, shared services, sensitive data, or unattended execution.
- Create a clean workspace. Prepare a disposable checkout and identify the exact directories that need read and write access.
- Define mounts. Mount only the checkout and required test data. Keep home directories, keys, cloud credentials, production configuration, and unrelated repositories out.
- Set the default network to deny. Add only the package registries, source hosts, model gateways, or APIs that the task requires, preferably through an allowlisting proxy.
- Configure the identity. Run as an unprivileged user, remove unnecessary capabilities, set process and disk limits, and use separate identities for separate environments.
- Attach the credential broker. Keep application keys in the trusted harness and issue short-lived, narrowly scoped tokens only after policy checks.
- Set approvals. Require an operator for boundary-crossing writes, new network destinations, privileged operations, pushes, deployments, and destructive commands.
- Enable logging and recovery. Record command, approval, filesystem, network, and credential events. Keep a snapshot or clean checkout so the task can be discarded.
- Run the validation checklist. Test both allowed and blocked actions before allowing unattended work.
How do you validate that the sandbox actually works?
Do not infer safety from configuration files alone. Run deliberate tests inside the exact image, operating-system profile, and approval mode that production jobs will use.
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- Secret read test: attempt to read a known test secret placed outside the workspace. The operation should fail, and the failure should appear in the audit record without revealing the secret.
- Path write test: attempt to create a file outside the permitted project directory and verify that the write is denied.
- Network deny test: attempt a request to an unapproved host and confirm that DNS or connection policy blocks it.
- Allowlist test: contact one approved package or source host and verify that the request succeeds only through the intended route.
- Credential test: request a brokered token for an approved operation, then try to use it against a different destination or after expiry.
- Resource test: start a process that consumes CPU, memory, processes, or disk and confirm that configured limits terminate or contain it.
- Approval test: attempt a boundary-crossing command and verify that execution pauses until an operator approves the exact request.
- Recovery test: destroy the environment, restore the clean checkout or snapshot, and confirm that no writable state or credential survives unintentionally.
Repeat these checks after changing the base image, runtime, mount list, proxy, operating-system policy, or approval configuration. A passing test is evidence for that configuration, not a permanent guarantee.
What should you monitor while an agent is running?
Capture enough context to reconstruct the task without storing secrets. Useful events include task and user identity, image or VM version, mounted paths, command start and end, exit status, approval decisions, destination host, bytes transferred, credential issuance, and resource-limit violations. Hash or otherwise identify artifacts so a reviewer can connect a patch to the exact execution environment.
Alert on repeated denied accesses, attempts to enumerate sensitive paths, unexpected destinations, privilege-escalation attempts, large uploads, unusual child-process trees, and changes to security configuration. Monitoring is most valuable when the harness can pause or terminate the task and revoke brokered credentials immediately.
What are the common sandbox failures and fixes?
The agent cannot install a dependency
First confirm whether the package registry is intentionally blocked. If the dependency is approved, add only its registry hostname through the proxy or provide a vetted internal cache. Do not solve the problem by enabling unrestricted internet access.
Tests need a service that is not mounted
Provide a disposable test service on an isolated network, with a dedicated account and synthetic data. Expose only the required port and deny routes to host or production networks.
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Inspect what it actually reads. Copy the minimum configuration into a task-specific directory, remove tokens and private keys, and mount that directory read-only. If the tool truly requires host-level state, move the operation into the trusted harness instead of widening the agent’s filesystem.
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A container escape or privileged operation is suspected
Stop the task, revoke any issued credentials, preserve logs, and discard the container or VM. Investigate the runtime, kernel, image provenance, capabilities, mounts, and host exposure before rerunning; never continue by granting more privileges to “see if it fixes the error.”
The agent keeps requesting approvals
Group repeatable, low-risk actions into a narrowly scoped policy such as read-only access to one registry or one project directory. Keep destructive, external, and credential-bearing actions on individual approval rather than creating a broad permanent exception.
How can you decide whether the setup is safe enough?
Classify the task by data sensitivity, code trust, external connectivity, and impact of a mistake. A local OS sandbox is reasonable when the repository is non-sensitive, the agent is supervised, the network can remain disabled, and recovery is easy. Move to a disposable container when you need a clean toolchain, deterministic mounts, repeatable tests, or a reset after every task. Use a VM or hosted runner when tenants are untrusted, production-adjacent systems are involved, stronger auditability is required, or the host cannot tolerate a compromised process.
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Frequently Asked Questions
Can an AI coding agent read files that are not in its workspace?
Only if the operating-system policy, container mounts, inherited credentials, or an exposed service gives it a path to those files. Verify this with a deliberate read test against a harmless marker outside the workspace; do not rely on the agent interface merely hiding a directory.
Is a read-only sandbox enough to prevent data exfiltration?
No. Read-only access still lets a process collect every visible file and send it over an allowed network connection. Pair filesystem restrictions with default-deny egress, hostname allowlisting, and secret isolation.
Should package installation be allowed in an unattended task?
Prefer pinned dependencies and a vetted internal cache or prebuilt image. If a live registry is unavoidable, allow only the required registry through a proxy, log the request, and keep the task’s credentials and network route narrowly scoped.
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Treat the credential as compromised: revoke or rotate it immediately, stop the task, preserve relevant audit records, and inspect the allowed network destinations and generated artifacts. Recreate the environment from a clean image and issue a replacement token only after the policy and egress path are corrected.
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