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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match“Sandbox” describes the goal—constraining what an AI agent can do—not one specific technology. A process restriction, container, or virtual machine can each be part of a sandbox, but the real boundary depends on what enforces it and which files, credentials, tools, and network paths the agent can reach. For trusted local work, process restrictions may be sufficient if their limits are understood; configured containers offer a practical shared-kernel boundary; and VMs or microVMs are appropriate when stronger separation from the host is required.
What “sandbox” means for an AI agent
An AI agent can turn model output into actions: reading files, running commands, installing packages, opening services, or calling tools. A sandbox is an environment configured to limit those actions and their consequences. The word alone does not tell you whether the limit is a permissions setting, an operating-system boundary, a container, or a guest machine under a hypervisor.
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OpenAI’s Agents SDK describes sandbox compute as an execution plane separate from the trusted harness that manages model calls, agent loops, tool routing, approvals, traces, recovery, and run state. Its documentation says: “A sandbox gives an agent an isolated, Unix-like execution environment with a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled access to external systems.” This model is useful when an agent needs to work with files or commands, produce artifacts, expose services, or resume state. The key architectural idea is to keep the trusted orchestration and the model-directed work apart where feasible.
Isolation is only one part of safety. A boundary can limit reach and blast radius, but it does not decide whether an agent should be authorized to perform a particular action. Tool permissions and approval policies still matter.
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How the main execution boundaries differ
| Option | What enforces the boundary | Typical fit | Important limitation |
|---|---|---|---|
| Local process restrictions | Operating-system permissions or other restrictions applied to host processes | Trusted local developer work when the limits are understood | A working directory, workspace path, or changed HOME alone is not OS-level confinement. |
| Container | Configured container isolation around processes, filesystem, and other resources | Reproducible execution with a useful boundary and controlled workspace | Ordinary containers share the host kernel; configuration and exposed capabilities determine what remains reachable. |
| VM or microVM | A hypervisor-backed guest environment that can run its own kernel | Work that needs stronger separation from host processes and resources | The actual assurance depends on the implementation and its network, mount, credential, and lifecycle configuration. |
These are not three interchangeable product labels. A sandbox is the constrained execution goal; a container or VM is a possible way to implement part of that goal. Neither the word “sandbox” nor choosing a particular isolation type guarantees a safe deployment.
When process restrictions are enough—and when they are not
A local process backend can be convenient for commands you trust. But OpenAI’s Python SDK client guide says that Unix-local commands run as host processes. On Linux, that backend adds no OS-level confinement: choosing a workspace directory, setting HOME, or setting the current working directory does not prevent a process from accessing other host-permitted paths.
The same guide distinguishes macOS filesystem restrictions from a container boundary: those restrictions do not provide network isolation or the same boundary as a container. For untrusted commands, it advises using Docker or hosted isolation that has been appropriately configured. Treat a local workspace path as a location, not as a security control, unless an actual enforcement mechanism restricts access.
When to use a container or a VM
Choose a configured container for a practical execution boundary
A container can give an agent a reproducible runtime and a useful way to control its filesystem and process environment. It is often a sensible choice when the job needs shell commands, packages, or repository edits, while sharing the host kernel is acceptable for the threat model. The word “configured” matters: mounted paths, network access, credentials, and other granted capabilities shape the effective boundary.
Docker’s documentation for its local AI sandbox describes a different product architecture: each agent runs in its own microVM with its own Linux kernel. The document contrasts that design with ordinary containers, which share the host kernel, and describes additional isolation layers involving the hypervisor, network, Docker Engine, workspace, and credential proxy. That is a description of Docker’s implementation, not a universal definition of every container or VM offering.
Choose a VM or microVM when host separation is a stronger requirement
A VM or microVM can provide a guest kernel distinct from the host kernel, which makes it a stronger candidate when model-generated commands, untrusted repository contents, hostile users, or mutually distrustful jobs raise the cost of host access. This does not make every VM deployment secure by default: the assurance still depends on the hypervisor and configuration, including exposed services, network routes, mounts, and credentials.
There is no established universal latency, cost, or escape-probability ranking for process restrictions, containers, and VMs in the cited documentation. Select against the specific threat model and operational needs rather than assuming one category is categorically best.
Workspace mounts are capabilities
The files an agent can see are part of its authority. Docker’s documentation describes materially different workspace choices: a mountless workspace stays inside the sandbox; a direct mount exposes host files and makes them writable to the agent; and clone mode lets the agent work in a private copy. A direct mount may be convenient, but it also gives model-directed code the ability to change the mounted files. Choose the narrowest workspace exposure that lets the job succeed.
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- Mountless workspace: Use when the task does not need direct access to host files.
- Private clone: Use when the agent needs a working copy without writing directly to the original workspace.
- Direct read/write mount: Use only when those host files genuinely need to be visible and writable.
- Any mount: Limit it to the data required for the task; do not treat a mount as passive merely because it is called a workspace.
Docker also warns that mounting the host Docker socket can grant an agent broad host access. A container boundary can be undermined by a capability that lets the agent control host Docker, so avoid exposing that socket unless the task explicitly requires it and the consequences are acceptable.
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Network access and credentials need separate controls
OpenAI’s Sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” That is the practical rule for threat modeling: assume code the agent runs can use every resource the environment makes available, even if the model was not meant to use it.
- Restrict outbound traffic. Prefer an explicit allowlist of approved destinations or enforced proxy rather than unrestricted egress. Check whether the environment can reach internal services as well as public endpoints.
- Keep application secrets outside execution compute. Do not place the application API key in the sandbox. For third-party credentials, use a proxy or vault-backed flow where possible.
- Scope and limit credential lifetime. Prefer narrowly scoped, short-lived access over ambient cloud credentials or long-lived secrets in environment variables or mounts.
- Assume injected secrets are readable by code. A secret made available inside the environment can be read by agent-generated code; injection alone is not a protection boundary.
Network isolation is not automatically supplied by filesystem restrictions, and neither containers nor VMs should be assumed to have safe egress by default. Configure and verify network controls as their own layer.
Keep trusted orchestration outside model-directed compute
The orchestration harness may hold authority that the agent’s execution environment does not need: model credentials, billing access, tool routing, approval decisions, audit records, recovery logic, or run state. Keeping this control plane in trusted infrastructure and sending only the required task and scoped data to sandbox compute reduces the number of sensitive capabilities exposed to agent-run code.
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Anthropic’s self-hosted Managed Agents security model is a useful reminder that a managed control plane does not secure customer-operated compute automatically. It assigns customers responsibility for image quality and runtime hardening, network egress, service-key storage and rotation, tool-to-tool isolation, and data retention after content reaches a customer’s worker. Establish those responsibilities explicitly when a provider operates some layers and you operate others.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use the threat model to choose the boundary
| Workload or concern | Decision direction | Controls to examine |
|---|---|---|
| Trusted commands on a developer machine | Process restrictions may be workable when their limits are clear. | Host permissions, actual filesystem restrictions, network reach, and which credentials are ambient. |
| Model-generated commands or untrusted repository content | Prefer configured isolated compute over a host process with only a chosen working directory. | Kernel boundary, mounts, egress, package installation, exposed ports, and cleanup. |
| Mutually distrustful users or jobs | Evaluate VM/microVM or hosted isolated execution when stronger separation is required. | Per-job identity and workspace, cross-job access, persistence, snapshots, logs, and provider/customer responsibilities. |
| Jobs that need host files or internal services | Grant only the specific data or destination the task needs; reconsider whether direct host access is necessary. | Read/write scope, private clone options, egress allowlists, proxy enforcement, and secret brokering. |
| Jobs that need nested containers or service ports | Decide whether the additional capability justifies its effect on the boundary. | Docker socket exposure, port visibility, network reach, and who can access the service. |
Anthropic groups agent risks into user misuse, model misbehavior, and external attacks through tools, files, or networks. Its described defense layers distinguish environment controls, model safeguards, and permissions on external content and tools. Environment controls constrain what an agent can reach; least-privilege tools reduce blast radius; model safeguards can shape behavior but do not create a hard capability boundary.
Anthropic’s engineering article says: “Claude Code’s reference devcontainer exists precisely so that the agent can run unattended, without per-action approvals.” That explains the rationale for Anthropic’s reference environment; it is not a guarantee that devcontainers make arbitrary agents safe.
Operational details affect the real boundary
Isolation is also a lifecycle and ownership question. Decide how an environment starts, what state persists, how it is cleaned up, what is logged, and who hardens the image and rotates credentials. A snapshot or resumable workspace can be useful, but persistence also determines what data and capabilities survive between runs.
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For one provider-specific example, Google Cloud’s Gemini Enterprise Agent Platform documentation, last updated October 1, 2026, states a seven-day TTL for a custom container image and a 14-day TTL for a code execution sandbox. The same page says cold provisioning can take up to two minutes, while later sandbox starts usually take seconds. These are that platform’s stated lifecycle and startup details, not general sandbox standards or guarantees for other providers.
Anthropic reports roughly 0.1% attack success on single attempts and about 5–6% after 100 adaptive attempts for Claude Opus 4.7 on Gray Swan’s Agent Red Teaming benchmark. Those are model- and benchmark-specific vendor-reported figures; repeated adaptive attempts are not equivalent to a single attempt, and the figures should not be generalized to other models or deployment settings. Anthropic also reports that Claude Code auto mode catches roughly 83% of overeager behaviors before execution. That is a product-specific vendor-reported figure, not an independent or universal safety rate. Neither measure substitutes for restricting actual capabilities in the execution environment.
Quick Recap
A practical selection and configuration sequence
- Classify who and what you trust. Decide whether the workload is trusted developer work, model-generated code, untrusted repository content, work from hostile users, or jobs that must not access one another.
- List required capabilities. Identify whether the agent must inspect or edit files, install packages, run nested containers, open ports, use a browser, persist state, or access specific data.
- Choose the enforcement boundary. Use local process restrictions only for trusted work with known limits; use a configured container where a shared-kernel boundary fits; consider a VM, microVM, or hosted isolated compute when stronger host separation is required.
- Minimize exposed workspace and identity. Remove unnecessary mounts and ambient credentials. Prefer a private clone or narrowly scoped data access, and grant short-lived access only when needed.
- Constrain the network and broker secrets. Block arbitrary access by default, allow only required destinations, and keep application keys outside execution compute. Use a proxy or vault-backed flow for third-party secrets where available.
- Separate the control plane. Keep model calls, authentication, billing, approvals, traces, and recovery in trusted infrastructure where feasible; give execution compute only the data and permissions needed for the task.
- Review the lifecycle and responsibility split. Check cleanup, persistence, snapshots, logs, image hardening, tool boundaries, and which party operates each security layer before calling the deployment isolated.
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