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Use a container when you are testing software behavior and accept a shared host kernel. Use a microVM when the agent itself is the untrusted subject and needs a separate kernel. Use Docker Sandbox when you want that microVM boundary plus a private Docker daemon so the agent can build and run containers without touching the host daemon. The right choice depends on what the agent may execute, which files and networks it can reach, and whether the evaluation must allow nested Docker workflows.
Docker describes containers as processes isolated with namespaces and cgroups inside a shared kernel. Its Sandbox product runs each local Sandbox in a microVM with its own kernel and a Docker Engine inside that VM. That is a meaningful architectural difference, but it is not an absolute security guarantee: workspace mounts, network policy, credentials, MCP integrations and persisted state still create paths across the boundary.
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A container isolates processes while using the host machine’s kernel. A microVM is a lightweight virtual machine that boots its own kernel. Docker Sandbox is a product built around the second model: Docker says every local Sandbox runs in a microVM, with a separate Docker Engine available inside it.
| Option | Kernel boundary | Docker build/run behavior | Typical evaluation role |
|---|---|---|---|
| Container | Shares the kernel of the machine where it runs; isolation uses namespaces and cgroups. | Uses whichever Docker daemon or container runtime you configure. A mounted host socket reaches the host daemon. | Fast, repeatable tests of an agent’s generated application when host-level isolation is not the primary threat. |
| MicroVM | Has a guest kernel behind a virtual-machine monitor and hypervisor boundary. | Needs a container engine inside the VM if the agent must build or run containers. | Evaluations in which a separate kernel materially reduces the consequence of a compromised workload. |
| Docker Sandbox | Docker’s local implementation of a microVM; the Sandbox has its own kernel. | Docker documents a private Docker Engine in each Sandbox, so agent-created containers do not use the host Docker daemon. | Autonomous coding agents that install packages, execute generated code and need Docker, Compose or nested builds. |
Docker’s comparison page poses the question “Is a Docker Sandbox just a container?” Its answer is no: the container shares the machine’s kernel, while the Sandbox is a microVM with its own kernel. Treat that as an architectural distinction, not as a promise that every surrounding integration is isolated.
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Why does the kernel boundary matter for an AI-agent evaluation?
An evaluation agent may run arbitrary generated code, install a malicious or vulnerable package, inspect environment variables, or attempt to control a container runtime. With an ordinary container, a kernel vulnerability or a dangerous runtime configuration can turn a container compromise into a host compromise. A guest kernel in a microVM adds a separate layer between the workload and the host.
The stronger boundary changes the threat model, not the need for one. A microVM still has a virtual device model, a hypervisor, a guest operating system, services running inside the VM and any deliberately exposed host integrations. Keep the host patched, minimize privileges, and treat an escape as possible even when the design makes it harder.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsContainers remain appropriate when the test is about application behavior rather than adversarial isolation. They are usually simpler to start, easier to inspect and efficient for large test matrices. If the agent is trusted, the code is reviewed before execution, and the runner is disposable, a container may provide the best throughput. If the agent is intentionally given autonomy over untrusted code, a separate kernel is often the more defensible default.
Can an AI agent build Docker containers safely in a Docker Sandbox?
It can build and run containers without using the host Docker daemon when the agent is operating in a Docker Sandbox configured with Docker’s in-VM engine. The agent’s Docker commands terminate at the Docker Engine inside that Sandbox, so a host Docker socket does not need to be mounted.
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“Safely” is conditional. A direct workspace mount is read-write, permitted network destinations remain reachable, local stdio MCP servers run on the host, and supported configurations can share a skills store. Those are part of the attack surface and must be included in the evaluation design.
What the private daemon changes
- The agent can execute ordinary Docker commands against the engine inside its Sandbox.
- Images, containers and build layers belong to that Sandbox’s engine rather than the host engine.
- A compromised build cannot automatically issue commands to the host daemon through a mounted socket, because the host socket is not the intended control path.
- Host administrators still control the Sandbox lifecycle, hypervisor, networking policy and any mounts or integrations they enable.
What it does not change
- Code can still modify any read-write workspace exposed to it.
- Network access can still allow data exfiltration or attacks against approved destinations.
- Credentials may be usable through an outbound proxy even when their raw values stay outside the VM.
- A host-side MCP server is a trusted component outside the VM and can expose whatever capabilities it implements.
Which workspace mode should an evaluation use?
Workspace mode is an experimental variable, not a cosmetic setting. Choose it according to whether the agent is being tested on a real checkout, on an isolated copy, or without source files at all.
| Mode | What the agent sees | Host-side effect | Best fit |
|---|---|---|---|
| Mountless | No host workspace. | No host working tree is exposed through the workspace. | Capability and coding tests that provide files another way, or tests focused on package and runtime behavior. |
| Direct mount | The host working tree is available read-write. | Edits are immediately visible on the host. | Interactive development workflows where host-visible changes are the subject of the test; not a clean isolation test. |
| Clone mode | The source is mounted read-only and a private clone is provided inside the VM. | Agent edits stay in the private clone until you deliberately export results. | Most reproducible coding-agent evals that need source context without allowing direct writes to the host checkout. |
Record the mode in every result. A patch produced in a private clone is not directly comparable with a patch made in a host-mounted tree: file timestamps, untracked files, available hooks and the consequences of destructive commands differ.
How should network access, credentials and MCP integrations be controlled?
Define network policy before the run and save the effective rules with its results. Docker documents outbound TCP through a host proxy that enforces policy. UDP is disabled by default; experimental UDP egress can be enabled under policy, and ICMP is blocked. These defaults are not a substitute for an allowlist.
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Build an explicit network matrix
- List every destination the workload needs, such as a package registry, source-control mirror, model endpoint or internal test service.
- Allow only those destinations and ports. Distinguish production services from disposable evaluation endpoints.
- State whether DNS is available and which resolver answers requests; DNS can itself disclose names or enable unexpected access.
- Record whether TCP proxying, UDP exceptions or other experimental options are active.
- Run a connectivity check before the agent starts and preserve the result with the evaluation record.
Docker says its host-side proxy can inject credentials into outbound HTTP requests so credential values remain outside the VM. That reduces secret exposure inside the guest, but it does not make an authorized request harmless. Scope tokens to the smallest repository, registry and time window, and revoke them after a failed or suspicious run.
Local stdio MCP servers are different: Docker documents them as running on the host outside the VM. Treat each server as trusted infrastructure. Review its executable, arguments, filesystem permissions and network access, and do not include a host MCP server in a claim that the agent had no host integration.
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Containers generally have less startup work because they do not boot a second kernel. Docker says a Sandbox takes more work to start because a kernel boots, and that its virtual machine monitor calls the platform hypervisor directly. Those are qualitative statements, not a controlled benchmark for AI-agent evaluations.
| Measure | What is established | What you must measure |
|---|---|---|
| Startup | Docker characterizes Sandbox startup as heavier than container startup because of kernel boot. | Time from request to usable shell, usable Docker daemon and first successful build on your host platform. |
| Memory and CPU | No cited material supplies a numerical overhead comparison. | Idle and peak memory, vCPU consumption, image-cache growth and contention while agents run in parallel. |
| Throughput | No controlled agent-eval throughput result is provided. | Completed tasks per hour, queue time, build cache hit rate and failure rate at your concurrency target. |
| Security outcome | The kernel boundary is qualitatively different; no escape-rate statistic is supplied. | Results from your own adversarial tests, including attempted host access, network exfiltration and credential misuse. |
Docker’s overview says local Sandbox compute and the CLI are free for commercial work, while cloud compute is pay-as-you-go and model-provider charges are separate. The comparison material labels its pricing estimates as rates current in September 2026. Confirm current terms before budgeting; those statements do not establish the total cost of an evaluation, which also includes hosts, storage, observability and engineering time.
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How do I choose the isolation boundary for a specific eval?
Start with the consequences of compromise, then check the workload’s technical requirements. Do not choose a boundary solely because an agent happens to invoke a Docker command.
Choose a container when
- The agent is trusted or the generated code is reviewed before execution.
- The runner is disposable and has no valuable host credentials or production network path.
- You need high fan-out and the test does not require a separate kernel.
- You are measuring application correctness, package compatibility or build reproducibility rather than hostile behavior.
Choose a standalone microVM when
- The code is intentionally untrusted and a host-kernel boundary is part of the safety case.
- You can install and manage a container engine inside the guest when nested Docker is needed.
- You want to control the guest image, kernel and services independently of a product-specific workflow.
Choose Docker Sandbox when
- The agent must autonomously install tools, execute arbitrary code and build or run containers.
- You want Docker’s documented in-VM Docker Engine rather than exposing the host socket.
- You can define acceptable workspace, network, credential and MCP connections explicitly.
- You prefer a managed local workflow over assembling a microVM, guest image and nested engine yourself.
Use a two-tier design when
Many teams can separate cheap functional runs from adversarial runs. Run ordinary regression tasks in containers, then send tasks that install unknown packages, process attacker-controlled repositories or attempt runtime manipulation to a microVM-based tier. Keep scoring and artifacts comparable, but label the boundary so results are not blended into one misleading number.
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What is a reproducible Docker Sandbox evaluation procedure?
- Write the threat model. State whether the agent may read secrets, alter the checkout, contact the internet, invoke Docker, call MCP tools or attack the runner. Define what counts as a failure.
- Pin the workload. Record agent version, model and prompt, repository revision, base image, package-lock files, Sandbox mode, network policy and host platform. Separate local from cloud runs.
- Select the workspace mode. Use mountless, direct or clone mode intentionally, and record the reason. For clean patch assessment, clone mode avoids immediate writes to the host tree while preserving source context.
- Configure least privilege. Expose only required directories, destinations and credentials. Avoid a host Docker socket. Review every host-side MCP server as an explicit trust boundary.
- Warm-up or cold-start deliberately. Decide whether images, package caches and installed tools should persist. Do not compare a warm reused Sandbox with a freshly created container without labeling the difference.
- Run benign checks first. Verify the agent can compile, test, build an image and reach only the services it needs. Capture daemon endpoint information so you can prove which engine handled Docker commands.
- Run adversarial cases. Include attempts to read parent files, inspect environment variables, contact blocked destinations, alter the host checkout, abuse Docker, and invoke unapproved MCP capabilities.
- Collect evidence. Save logs, filesystem diffs, image lists, network decisions, resource measurements, exit codes and any host alerts. Preserve failed runs rather than deleting them before analysis.
- Destroy or retain state according to policy. Docker documents that local Sandbox state, including installed packages and images, persists through stops and restarts until the Sandbox is removed. Recreate for clean trials; reuse only when persistence is the condition being tested.
- Report results by boundary. Publish startup, resource, success, failure and security outcomes separately for containers, microVMs and Sandboxes. Include uncertainty where you have no benchmark.
What common mistakes invalidate an agent-eval comparison?
- Calling a mounted host socket “nested Docker.” A container with the host Docker socket is controlling the host daemon, not an isolated inner engine.
- Changing workspace semantics between runs. Direct mounts expose writes that clone mode hides; scores are not equivalent.
- Ignoring network exceptions. A nominally offline run with an approved package registry or MCP endpoint still has an information path.
- Reusing dirty state accidentally. Persisted images, credentials, caches and installed packages can make later agents faster or give them capabilities earlier agents did not have.
- Reporting vendor architecture as measured security. A separate kernel is a design property. It is not an independently measured escape probability or a guarantee against every hypervisor, guest or integration flaw.
- Comparing cold containers with warm Sandboxes. Measure equivalent lifecycle conditions and report both cold-start and steady-state behavior.
- Forgetting the host. Keep the orchestrator, logs, artifact store and credentials outside the agent’s writable paths, and monitor them as part of the system under test.
What should an evaluation report contain?
A useful report lets another engineer reproduce the boundary and interpret the result without guessing. Include:
- Boundary type, runtime and guest or host kernel details.
- Host platform and hypervisor configuration for microVM-based runs.
- Workspace mode and exact mounts, including read-only versus read-write status.
- Docker endpoint used by the agent and whether a host socket was present.
- Network protocol defaults, allowlisted destinations, proxy behavior and DNS arrangement.
- Credentials, token scope, expiration and host integrations such as MCP servers.
- Cold or warm lifecycle, persistence policy and image-cache state.
- Startup latency, peak resources, task result, artifact checksums and security-test outcomes measured by your team.
- Costs separated into local infrastructure, cloud compute, storage, model-provider charges and operator time.
This level of detail prevents a “Sandbox passed” result from hiding that the agent had a read-write checkout, broad egress or a powerful host-side integration.
Frequently Asked Questions
Does a Docker Sandbox protect files that are outside the mounted workspace?
Only if those files are not exposed through another path. A mountless or clone configuration limits the workspace view, but credentials, host services, network destinations and MCP integrations can still provide indirect access, so review the complete configuration rather than relying on the workspace setting alone.
Should I delete a Sandbox after every AI-agent task?
Delete and recreate it when you need clean-state measurements or want to remove packages, images and other accumulated artifacts. Reuse is reasonable for a persistence test or a development workflow, but label warm-state results because they are not comparable with a fresh environment.
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Docker documents UDP as disabled by default and says experimental UDP egress can be enabled under policy. If your workload needs UDP, record that exception and test the resulting exposure separately from the default TCP-only configuration.
What evidence shows that an agent used the inner Docker daemon?
Capture the daemon endpoint and engine metadata from inside the evaluation, then correlate image, container and build records with the Sandbox’s engine. A successful Docker command alone is insufficient if a host socket or another external runtime was also available.
Are cloud Sandboxes configured exactly like local Sandboxes?
Not necessarily. The cited Docker pages focus on local Sandboxes and direct readers to separate cloud documentation for cloud behavior and limitations. Treat locality, networking, persistence and pricing as explicit variables when comparing environments.
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