Secure Docker by controlling who can reach the daemon, limiting each container’s privileges and writable surfaces, checking images and host configuration, and tracing what crosses any AI sandbox boundary. Work through the labs below in that order: each one examines a different part of the trust boundary, and none alone proves a workload is secure.
What Docker security depends on
A container image is only one part of the security picture. The host kernel provides isolation; the Docker daemon can control containers and expose host resources; container settings determine what a workload can do; and host hardening affects the environment around it. A secure image cannot compensate for an exposed daemon or an over-privileged container.
The same principle applies when an AI agent runs in a sandbox: examine not only the agent process, but also shared files, network access, credentials, tools, and processes that run outside the sandbox.
Lab 1: Map and constrain Docker daemon access
Identify who can control the daemon
Start by listing the people, services, automation jobs, and remote clients that can issue Docker commands. On a rootful Docker installation, control of the daemon has host-level implications. Docker documents that the daemon can provide a container with unrestricted access to a host directory. Do not treat container isolation as protection from an untrusted user who can control that daemon.
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On a Linux host, inspect the daemon’s socket and how access is granted. Check group membership and any service accounts or CI runners that can access it. A user who can control a rootful daemon may be able to create a container with powerful access to the host; membership in a Docker-control group should therefore be treated as privileged access, not ordinary container access.
Review remote access and mounts
- Determine whether the Docker API is reachable over a network. An API endpoint exposed to an untrusted network gives its callers powerful control; restrict access to trusted networks and use a protected remote-administration path such as SSH when remote management is required.
- For each host mount, record the host path, container path, whether it is read-only, and why the workload needs it. Avoid exposing broad host directories when a narrower path will do.
- Remove unused daemon access and credentials, then repeat the review for deployment tools and automated jobs that can create containers.
Lab 2: Review container runtime privileges
Check the workload’s identity and access
For each running container, identify its configured user, Linux capabilities, privileged setting, host mounts, and access to host resources. The Docker CLI can help you inventory what is running and inspect individual container configuration:
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docker psdocker inspect <container-name-or-id>Review the relevant configuration fields rather than assuming the image’s defaults are appropriate. If the application can run as a non-root user inside the container, configure and test that choice. A non-root container user reduces the process’s privileges within the container, but does not make risky daemon access, host mounts, or other excessive permissions safe.
Remove unnecessary capabilities and privileged access
Docker’s Engine guidance recommends removing capabilities that a process does not explicitly need. Use an allowlist mindset: begin with the workload’s actual requirements, retain only the capabilities it needs, and avoid privileged operation unless a documented requirement justifies it. Also review devices, namespaces, and other host resources exposed to the container.
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Test changes against the real workload. A service may fail if it depends on a capability or resource you remove; investigate that dependency instead of restoring broad privileges by default. Record the smallest configuration that works and recheck it when the application changes.
Lab 3: Assess images and reduce their attack surface
Compare candidate images against the needs of the application, not just their size or name. Review which components they include, the default user, writable locations, compatibility with the workload, and how updates and vulnerabilities are handled.
- Included components: Identify shells, compilers, package managers, and other tools that the application does not need at runtime. A more limited image can reduce available attack surface, although it does not guarantee application security.
- Default user: Confirm whether the image runs as a non-root user by default and whether the workload can keep that setting.
- Writable surfaces: Find which paths the application must write to and whether those paths can be narrowed. Avoid making unrelated parts of the container writable.
- Update workflow: Establish how the base image and application dependencies are rebuilt, checked, and redeployed when fixes become available.
- Compatibility: Verify that reducing image contents does not remove a runtime dependency the application actually needs.
Docker Hardened Images are one approach to reducing included components and using non-root defaults. Evaluate them using the same criteria as any base image; adopting a hardened base does not by itself secure the application or its deployment.
Lab 4: Run a Docker security baseline audit
Docker Bench for Security automates checks for common Docker deployment practices. Its project describes the checks as based on CIS Docker Benchmark v1.6.0. Treat its report as a self-assessment against that benchmark version, not as a certification or proof that an application is secure.
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- Choose the host to assess. Use the project’s official instructions to obtain and run Docker Bench for Security on the Docker host you want to review. Confirm that the way you run it is appropriate for that host and environment.
- Review the check descriptions and applicability. A finding needs context: determine what setting it refers to, whether the control applies to your deployment, and what changing it would affect.
- Record and prioritize findings. Note the affected host or workload, the current configuration, the risk, and the owner of the fix. Investigate before changing production settings.
- Remediate appropriate findings and rerun the checks. Confirm that the configuration changed as intended, that workloads still function, and whether the finding is resolved.
The benchmark basis identifies what the checks map to; it does not establish that every check fits every host or that the tool supports every platform. Check the project’s instructions for your target environment.
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Draw the boundary and list what crosses it
Docker describes AI Sandboxes as running agents in microVMs, with the VM as the primary trust boundary. Map the agent’s actual access rather than assuming that the word “sandbox” settles the question. For each shared input or capability, ask what the agent can read, change, execute, or reach.
- Workspace: Which host files or directories are shared, and can the agent modify them?
- Network: Which destinations can the agent reach, and what services or data are available there?
- Credentials: What tokens, keys, or other secrets are accessible to the agent or to its tools?
- Tools: What can each tool do, and which permissions does it use?
- MCP servers and host processes: Does a local MCP server start a host process or Docker container? Docker cautions that such a process uses host permissions and host isolation; it does not inherit the sandbox’s boundary.
Compare isolation choices by exposure
When comparing ways to run an agent, assess the boundary’s strength alongside workspace sharing, network access, credential exposure, and the authority of tools or MCP servers started outside the sandbox. A microVM boundary is one important control, but shared access and host-side processes still need their own limits and review.
Keep versions and advisories current
Docker security announcements are version-specific and can change. When an advisory may affect your environment, identify the component involved—such as Engine, BuildKit, runtime, or Docker Desktop—then compare the installed version with the advisory’s affected range and patched release. Check the current announcement before deciding whether a system is affected; do not infer the status of one component from another.
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