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An AI agent sandbox is an isolated, Unix-like workspace where an agent can run shell commands, install packages, edit files, build software and expose previews without receiving unrestricted access to your computer. The sandbox is the compute plane; a separate harness remains responsible for model calls, tools, approvals, tracing and recovery. Giving an agent this “little Linux box” matters when a task needs a repository, documents, package installation or a resumable development session. For a short question that needs only text, creating a sandbox adds needless operational complexity.
What is an AI agent sandbox?
An agent sandbox is a bounded execution environment with its own filesystem, shell, installed packages, mounted data, optional ports, saved state and controlled connections to outside systems. It is closer to a temporary development machine than to extra prompt context: the agent’s commands execute there, and the resulting files and processes live there until you deliberately export, mount or preserve them.
OpenAI’s sandbox model separates two responsibilities. The harness controls model requests, tool permissions, approvals, run state, tracing and recovery. The sandbox supplies the operating environment in which model-directed code actually runs. Keeping those planes distinct makes it possible to replace a local runtime with Docker or a hosted provider without rewriting the agent definition and its file manifest.
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An isolated workspace is useful when an agent must do real computer work rather than return a paragraph. It can inspect a repository, run tests, install dependencies, transform a directory of documents, generate artifacts, start a preview server or pause while a person reviews changes.
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- Repositories and build tools: The agent can clone or receive a project, install its toolchain and run tests without polluting your everyday machine.
- Documents and data: A manifest can provide selected local files, directories, Git repositories or object-storage mounts instead of exposing an entire home directory.
- Long-running work: A live session, serialized session or snapshot lets a job resume after a review or interruption.
- Reproducibility: A declared base environment, packages and mounts make a run easier to repeat than an agent operating in an unknown desktop setup.
For a simple factual answer, these benefits do not justify the extra filesystem, networking and lifecycle controls. Use a sandbox when execution, modification or persistence is part of the task.
Are containers safe for coding agents?
A container limits processes and files, but a container by itself is not automatically a complete security boundary. The strength of the boundary depends on the runtime, kernel sharing, mounted paths, privileges, network rules and credentials. Docker’s documented Sandboxes place each agent in a lightweight microVM with its own Linux kernel, adding a boundary below the container.
Docker describes five defensive layers for that arrangement:
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- Network isolation: Traffic passes through a policy-enforcing path rather than inheriting unrestricted host networking.
- Docker Engine isolation: The agent can run a private Docker Engine inside the VM, so it does not need the host’s Docker socket.
- Workspace isolation: Files are mounted or cloned according to an explicit mode instead of silently exposing the host checkout.
- Credential isolation: Secrets can be mediated by a host-side or provider-controlled proxy rather than copied into the VM.
The primary trust boundary in Docker’s security guidance is the microVM. That does not make every workload harmless: an agent can still damage everything it can see inside the sandbox, abuse an allowed network destination or leak data through an approved channel. OpenAI’s security guidance therefore recommends defense in depth: isolate workloads, restrict outbound access to approved endpoints and keep credentials separate.
What is the difference between a container and a microVM?
A conventional container shares the host kernel while isolating processes, namespaces and filesystems. A microVM adds a virtualized machine boundary and its own guest kernel, generally providing stronger separation at the cost of additional startup and resource overhead.
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| Decision axis | Container-only runtime | MicroVM-backed sandbox | Full virtual machine |
|---|---|---|---|
| Kernel boundary | Shares the host kernel | Uses a separate guest Linux kernel | Uses a separate guest operating system and kernel |
| Typical startup and density | Usually the lightest and fastest | Lightweight, with virtualization overhead | Generally the heaviest option |
| Host filesystem exposure | Determined by mounts; a broad read-write mount is high risk | Determined by mounts, with the microVM as the primary trust boundary | Determined by virtual disks, shared folders and agents |
| Nested containers | Needs careful daemon and privilege configuration | Can run a private Docker Engine inside the VM | Can run its own container runtime |
| Network policy | Runtime and host policy dependent | Can enforce traffic through a sandbox proxy and resolver | Usually configured at the VM and cloud-network layers |
| Operational burden | Low to moderate | Moderate; the provider manages much of the VM plumbing | Highest; you manage more of the machine lifecycle |
Choose the boundary according to the consequence of compromise. A trusted, single-purpose build step may fit a container. Untrusted generated code, multi-tenant workloads or agents that need Docker commonly justify a microVM or VM. No boundary replaces least-privilege mounts, egress controls and review of generated scripts.
How should I expose a workspace to an agent?
The workspace mode determines whether the agent can write directly to your host files. Docker documents three useful patterns; selecting the right one is often more important than the choice of programming language or model.
| Mode | What the agent sees | Benefit | Risk and review point |
|---|---|---|---|
| Mountless | Only the sandbox filesystem | Clearest host boundary | You must copy or mount every required file through an explicit manifest |
| Direct mount | A host workspace mounted read-write | Immediate, convenient editing of the real checkout | Changes are instantly visible on the host; inspect Git hooks, CI files, build scripts and agent or IDE configuration before running anything |
| Clone mode | Host repository mounted read-only, with a private clone inside the VM | Normal agent editing while preserving a clean host checkout | Changes must be exported or reviewed from the private clone before they reach the primary workspace |
Use mountless mode for sensitive tasks or when the input can be represented as a small manifest. Use clone mode as the default for coding agents that need a complete repository but should not be able to rewrite the host checkout. Reserve direct mounts for trusted workflows where immediate host-side edits are an intentional part of the process.
How do I stop an agent from touching my files or secrets?
Start with an allowlist rather than trying to block every possible filename after the fact. Give the agent only the directories, repositories, users, groups and environment variables required for the task, and choose read-only mounts wherever writes are unnecessary.
- Define the input manifest. List the exact files, directories, Git repositories or object-storage mounts the run needs. Exclude home-directory-wide mounts, SSH directories, browser profiles and unrelated repositories.
- Pick a write boundary. Prefer mountless or clone mode. If a direct mount is unavoidable, make a clean commit first and inspect executable files that the agent could alter.
- Separate credentials. Do not place API keys in a checked-in file or ordinary environment variable that the agent can print. Inject access through a host-side or provider-controlled proxy that can authorize a request without revealing the secret value inside the VM.
- Constrain egress. Allow only the package registries, source hosts, APIs or preview endpoints the job requires. Deny all other outbound destinations and log permitted requests.
- Review exports. Treat generated patches, archives, logs and build artifacts as untrusted output. Scan them for secrets and unexpected binaries before copying them to a host or publishing them.
A sandbox prevents access only to resources outside its configured boundary. If you mount a secret, grant a broad token or expose the host Docker socket, you have intentionally moved that resource inside the boundary. The private Docker Engine pattern avoids the especially dangerous shortcut of giving an agent control of the host daemon.
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What network controls should an agent sandbox have?
Network access is a policy decision, not a default feature to leave unchecked. OpenAI recommends permitting outbound traffic only to approved endpoints. This limits data exfiltration and reduces the damage from a dependency, script or prompt-injected instruction that attempts to contact an arbitrary service.
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In Docker’s documented local model, sandbox traffic is routed through a policy-enforcing proxy, outbound UDP is disabled by default and DNS is handled by the sandbox resolver. Those defaults still need to be checked against the runtime version and changed deliberately when a workload requires a particular protocol. Package installation, Git access, cloud APIs and browser previews should each have a stated destination and purpose.
- Build jobs: allow only the package and source hosts needed to resolve dependencies.
- Data-processing jobs: keep outbound access disabled unless a specific API is part of the task.
- Preview applications: expose the required port to an authenticated reviewer, not to the public internet by default.
- Auditing: retain proxy and approval logs so an unexpected connection can be investigated after the run.
How do persistence, snapshots and recovery work?
A sandbox does not have to disappear when a command exits. Providers can preserve a live session, serialize a session, create a snapshot or attach a storage mount. OpenAI’s manifest model can describe files, directories, repositories, object-storage mounts, environment variables, users and groups, allowing a resumed run to reconstruct the intended workspace.
Use a live session while an agent is actively developing, a snapshot when you need a restorable point before a risky change, and an explicit export when a human has approved artifacts for the host. Snapshots preserve state; they do not automatically make state safe. A snapshot may contain credentials, caches, private source or malicious binaries, so apply the same retention and access rules as the original workspace.
Should I run an agent sandbox locally or use a hosted provider?
Local execution keeps data and latency under your control, while hosted execution removes much of the machine and scaling work. The right choice depends on governance, workload burstiness and the features you need around storage, previews and credentials.
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| Option | Best fit | Advantages | Trade-offs to verify |
|---|---|---|---|
| Unix-local | Individual development on a Unix-like host | Fast feedback, local data and no service dependency | You manage isolation, updates, networking and cleanup |
| Local Docker or microVM runtime | Developers who want a repeatable local boundary | Container tooling, selectable mounts and a private engine pattern | Desktop resource use and runtime-specific security defaults |
| Hosted sandbox | Teams needing managed execution, scaling, previews, storage mounts or provider-managed credentials | Less infrastructure maintenance and easier parallel runs | Provider availability, cost, data residency, retention and lock-in |
OpenAI recommends Unix-local for local development, Docker when local container isolation is desired and hosted providers when managed execution is more valuable than owning the infrastructure. A provider can be selected at run time while the agent definition and manifest remain stable. OpenAI-hosted sandboxes provide a Linux workspace with Python, Node.js and command-line tools; the application chooses the environment type, compute size and network settings.
How do I design a safe sandbox workflow?
Build the workflow around explicit boundaries and a human review point, not around the assumption that the model will behave. The following sequence works for a new coding or data-processing agent.
- Classify the task. Decide whether it needs execution at all, and identify the files, services and runtime tools it truly requires.
- Select the isolation boundary. Use a container for low-risk, single-tenant work; choose a microVM or VM when separate kernels, untrusted code or nested containers matter.
- Write the manifest. Declare mounts, users, groups, packages, environment variables, ports and persistence explicitly.
- Choose mountless or clone mode first. Move to a direct read-write mount only when the workflow has a specific reason and a clean rollback path.
- Set network and credential policy. Allowlist destinations, mediate secrets through a proxy and disable unused protocols.
- Run with approvals. Require review before exporting files, publishing a preview, changing a deployment configuration or using a new endpoint.
- Inspect and recover. Check diffs, logs and generated artifacts; restore a snapshot or discard the sandbox when the run diverges.
What commonly goes wrong in agent sandboxes?
The agent changed files I did not expect
Check whether the repository was directly mounted read-write and whether hooks, build scripts or configuration files execute automatically. Recreate the run in clone mode or mountless mode, then export only the reviewed patch.
The agent cannot install a package or clone a repository
Inspect the egress allowlist, DNS behavior and proxy logs before granting broader access. Permit the specific registry or source endpoint, and keep unrelated destinations blocked.
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A tool needs Docker but the host daemon is exposed
Do not mount the host Docker socket as a shortcut. Use a runtime that provides a private Docker Engine inside a microVM or use a separate VM with its own daemon.
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A resumed session contains stale or sensitive state
Review what the snapshot or storage mount retained, rotate any credential that may have entered the workspace and create a fresh manifest. Persistence should be intentional and time-limited.
A hosted sandbox is unsuitable for the data
Confirm the provider’s region, retention, access controls and deletion behavior. Move the workload to a local or privately managed boundary when organizational policy does not permit the data to leave your environment.
Which sandbox setup should you choose?
For most coding agents, a microVM-backed clone workspace with an allowlisted network and proxy-mediated credentials is the practical middle ground: it preserves a clean host checkout while supporting ordinary development tools. Use mountless mode for the strongest separation, direct mounts only for trusted and reversible workflows, and a hosted sandbox when managed scaling or previews outweigh provider and governance concerns. The Linux box is useful because it makes execution controllable; its safety comes from the boundaries you configure around files, kernels, networks, credentials and human approvals.
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Frequently Asked Questions
Can a sandboxed agent access the internet?
Yes, if the runtime policy permits it. Treat access as an allowlist of destinations and protocols, and test package installation or API calls against that policy before giving the agent a broader route.
Does a microVM guarantee that an agent is safe?
No. A microVM strengthens the kernel boundary, but a mounted secret, permissive token or approved endpoint can still expose sensitive data. Security remains a layered design involving mounts, credentials, egress, logging and review.
Do multiple agents need separate sandboxes?
Use separate environments when their data, users or trust levels must not mix. Sharing one sandbox can be reasonable for cooperating steps in a single workflow, but shared writable state makes attribution, cleanup and incident response harder.
What should I keep from a sandbox after a run?
Normally keep the reviewed patch, selected artifacts and relevant logs rather than the entire filesystem. Preserve a snapshot only when you have a defined recovery or audit need, and apply an expiration and access policy to it.
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