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What “safe” testing actually means
AI-generated code must be treated as untrusted input. It can contain ordinary bugs, destructive operations, accidental data leaks, or deliberately harmful instructions. A sandbox reduces the blast radius; it does not make execution risk-free.
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The practical question is not whether a tool carries the word sandbox. It is which boundary executes the code, what files and devices cross that boundary, which network destinations are reachable, and what happens when the code finishes. Docker’s official security documentation puts the relationship plainly: “The sandbox isolates the agent from your host system, but the agent’s actions can still affect you through explicitly shared resources and allowed network channels.”
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For a defensible test, establish four controls before execution:
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- A real isolation boundary: preferably a separate-kernel microVM or an application-kernel runtime, not an ordinary container presented as a virtual machine.
- Minimal visibility: no host mount unless the task truly needs one, and no sensitive files in any shared repository.
- Constrained connectivity: start with no external access or a short, reviewed allow-list.
- Separated secrets: keep API tokens, cloud credentials, private keys, and signing authority outside the execution environment whenever possible.
After the run, inspect source changes, generated scripts, hooks, task definitions, and external-service activity before using the result on your workstation or in a shared system.
Start with a threat model
Write down what you are protecting before selecting a runtime. The controls appropriate for a throwaway toy project are not enough for a development repository, production credentials, customer records, or a multi-tenant execution service.
Identify the asset at risk
- Personal workstation: browser sessions, SSH keys, password stores, local documents, clipboard contents, and other user files.
- Development repository: source code, untracked files, ignored files, build credentials, Git hooks, and CI configuration.
- Production or cloud access: environment variables, cloud metadata, deployment keys, signing keys, and API tokens.
- Customer or regulated data: anything that must not be copied to an untrusted process or external service.
- Multi-tenant execution: other users’ workloads, shared storage, orchestration control planes, and the host or hypervisor itself.
Define what a successful test looks like
Specify the input, expected output, allowed files, required packages, and permitted network destinations before starting. If the code only needs a compiler and a small fixture, it should not receive an entire home directory, a writable checkout, or unrestricted Internet access. A precise test boundary makes unexpected behavior easier to detect.
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Escalate high-impact cases
The Docker and gVisor documentation describes runtime mechanisms, not a complete threat model for every deployment. For especially sensitive or multi-tenant workloads, have a security engineer assess the host, hypervisor or runtime configuration, network path, storage, logging, patching, and operational response. Do not infer that a documented feature is a complete security review.
Choose an isolation boundary that matches the risk
Docker Sandboxes: a documented microVM boundary
Docker’s local Sandboxes documentation says each agent runs inside a microVM with its own Linux kernel. That separate guest kernel is the trust boundary; it is materially different from an ordinary container that shares the host kernel. The agent can still have substantial authority inside its VM, including sudo access, so the protection comes from the VM boundary and the resources you share with it—not from assuming the in-VM process is harmless.
Docker describes a five-layer isolation design in its product documentation. Treat that count as a description of the product’s current architecture, not as an independently measured safety score or proof that every attack is prevented.
gVisor with Docker or Kubernetes
gVisor takes a different architectural route. Its runsc OCI runtime supplies an application kernel, often called the Sentry, that intercepts workload system calls. The project’s official overview says, “gVisor provides a virtualized environment in order to sandbox containers.” The workload does not pass every system call directly to the host kernel; a limited set of host calls supports the runtime.
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gVisor’s documentation contrasts this design with seccomp, AppArmor, and namespaces, whose enforcement still relies on the host kernel. It also distinguishes gVisor from a conventional hardware-virtualized VM. That is an architectural comparison, not a universal performance or attack-resistance ranking. Compatibility and runtime configuration must be evaluated for the workload you intend to run.
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Why an ordinary container is not the same thing
Standard containers use namespaces, cgroups, capabilities, and related controls while sharing the host kernel. They can be useful for packaging and reducing accidental damage, but do not describe them as equivalent to a separate-kernel VM. A container with a broad host mount, powerful capabilities, or an exposed Docker control socket can still provide a path to serious host impact.
Python exec(), an unrestricted subprocess, or a wrapper that merely changes the working directory is not a sandbox for arbitrary generated code. Those approaches leave the process inside the host’s operating-system security boundary.
Compare the main choices
| Axis | Docker Sandboxes | gVisor with Docker or Kubernetes | Ordinary container |
|---|---|---|---|
| Isolation architecture | Each agent runs in a microVM with its own Linux kernel. | runsc provides a per-sandbox application kernel and intercepts workload system calls. |
Workloads share the host kernel. |
| Workspace and network controls | Documented mountless, clone, and direct workspace choices, plus policy-controlled outbound networking. | Runtime architecture is documented; workspace and network policy depend on the surrounding container and orchestration setup. | Controls depend on the container runtime and deployment; no separate-kernel boundary is implied. |
| Operational fit | Product-specific local workflow for supported AI agents; the agent has broad permissions inside its VM. | OCI-compatible runtime choice for Docker or Kubernetes workloads; compatibility requires testing. | General packaging and process isolation, not a claim of VM-equivalent protection. |
| Evidence available | Official Docker security model and product controls. | Official gVisor architecture and runtime documentation. | Varies by runtime, configuration, and host hardening. |
The available documentation does not provide a comparable benchmark, a universal ranking, or a numeric measure of escape resistance. Choose based on your threat model and verify the exact deployment configuration.
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Mountless mode: the safest default when files are unnecessary
If the test can run from generated input, a fixture archive, or a newly created project, use a mountless environment. With no host project directory exposed, code cannot directly read or modify that directory through the workspace interface. Supply only the files required for the test.
Clone mode: inspect a private working copy
Docker documents clone mode as a read-only host repository mount combined with a private clone inside the VM. The agent works on the clone, so edits do not immediately alter the host working tree. This is useful when the code needs a repository but you want review before applying changes.
A read-only source mount is not a secrecy boundary. Docker notes that files under the Git root—including untracked and ignored files—are readable in clone mode. Before sharing a repository, check for .env files, credentials, local configuration, test exports, and other material that should not enter the sandbox.
Direct mode: use only for deliberate, observable edits
Direct mode mounts the working tree read-write into the VM. Code running inside the boundary can affect those shared files, and the changes become visible on the host as they happen. Choose this only when live host-visible edits are required and when the repository contains no secrets that the generated code should not see.
Prefer a disposable branch or copy, record the initial state, and keep the test’s output separate from the source tree. A direct mount does not protect you from destructive file operations within the mounted path.
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Check the repository before sharing it
- Search for environment files, private keys, cloud configuration, database dumps, and credentials embedded in ignored files.
- Review Git submodules and generated directories that may contain additional code or secrets.
- Decide whether build caches, package-manager credentials, and local tool configuration are needed.
- Remove or replace sensitive fixtures with synthetic data.
- Record which paths are shared and whether each is read-only or read-write.
Restrict network access before installing anything
Begin with no external access
Many tests do not need the network. Start with outbound access disabled when the task permits, then add only the destinations required for a package download or API call. This limits data exfiltration and makes unexpected connections visible.
Understand Docker’s documented network behavior
Docker describes outbound TCP as routed through a policy-enforcement proxy. UDP is disabled by default; experimental UDP egress can be controlled by policy. The default posture blocks outbound TCP destinations outside the allow-list, but policy rules can add destinations, and default allowed domains may be broad. Inspect the active policy rather than assuming that an allow-list is narrow.
Allow-list by function, not convenience
- Permit the package registry required by the test, not every domain used by a package manager.
- Permit a specific API endpoint only when the test genuinely needs it.
- Review wildcard domains carefully; one broad pattern can cover many unrelated services.
- Block uploads, telemetry, and update channels that are not part of the test.
- Log destination, protocol, and time so an unexpected connection can be investigated.
A network policy reduces reachable services but does not make the code trustworthy. An allowed endpoint can still receive sensitive test data, and a compromised dependency can use an apparently legitimate destination.
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Prefer a host-side credential proxy
Docker documents a host-side proxy that injects credentials into outbound HTTP headers without storing the raw values in the VM. This design keeps the secret value outside the execution environment, but it does not remove the need to review which requests the code can make or what data those requests contain.
Do not treat every shared channel as a secret-free channel
Credential handling depends on what you explicitly supply. Workspace files, mounted configuration, environment variables, forwarded sockets, and local services are separate sharing decisions. A token kept out of the VM can still be exposed if a file containing it is mounted or if a process can query a host service that returns it.
Review SSH-agent forwarding
Docker’s security documentation notes that an SSH-agent socket may be forwarded and sandbox processes may request signatures. The private key material may remain on the host, but the sandbox could still ask the agent to sign data if forwarding is enabled. Disable forwarding unless the test requires it, and use a narrowly scoped key or confirmation workflow when it does.
Use disposable identities
- Use short-lived, least-privileged tokens limited to the test’s required API.
- Never expose production credentials, cloud-admin roles, signing keys, or long-lived personal tokens.
- Revoke test credentials after the run, especially if network policy was broader than intended.
- Check shell history, logs, crash dumps, and generated artifacts for accidental secret disclosure.
A safe test procedure
1. Freeze the input and expected result
Save the exact AI output, prompt context that affects the code, dependency manifest, fixture data, and expected behavior. Do not let the test silently fetch a newer prompt, package, or remote script while you are evaluating it.
2. Inspect before execution
Read the source and look for file deletion, recursive traversal, shell invocation, dynamic code loading, network clients, credential discovery, archive extraction, process spawning, and attempts to modify Git or CI configuration. Generated build scripts and package lifecycle scripts deserve the same scrutiny as application code.
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3. Select the boundary and workspace
Use a Docker microVM or a gVisor-backed runtime appropriate to the deployment. Choose mountless mode if possible, clone mode for an isolated repository copy, or direct mode only when live edits are intentional. Write down every shared path and its access mode.
4. Configure network and identity
Disable external access unless required. Add the smallest destination allow-list, confirm whether UDP is disabled, and remove environment variables, SSH-agent forwarding, and mounted credential files that the test does not need.
5. Run a harmless probe first
Start with a small fixture that verifies the expected interpreter, package set, working directory, and file permissions. Confirm that an attempted write outside the intended workspace fails and that a blocked network request is actually blocked. This validates the boundary before the full generated program runs.
6. Execute with observation enabled
Capture stdout, stderr, exit status, file changes, package installation, process creation, and network destinations. Keep the run isolated from personal applications. Do not paste unreviewed output into a privileged terminal, editor, browser, or chat session.
7. Stop and preserve evidence
When the test ends, save logs and the resulting workspace as evidence. Do not immediately promote generated files into the host project. If the process hangs or behaves unexpectedly, terminate the sandbox and revoke any temporary credentials before investigating.
8. Review before applying changes
Compare the resulting files with the initial state. Inspect build scripts, package scripts, IDE tasks, Git hooks, CI configuration, generated deployment files, and dependency changes. A normal git diff may not show Git hooks, so check hook directories and other hidden execution paths explicitly.
Inspect escape routes and shared resources
Direct mounts can become execution paths
Files changed through a direct mount may execute implicitly during normal development. A modified build script can run when an editor saves, a package manager installs dependencies, or a CI job starts. Review the entire shared tree before returning to ordinary tooling.
Local stdio MCP servers are outside the VM
Docker documents that local stdio MCP servers run on the host outside the sandbox VM. If the agent can call one, its operations do not automatically inherit the VM boundary. Treat each server as a separate privileged integration: inspect its executable, arguments, filesystem access, network access, and returned data.
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Clipboard access is a host-side channel
Docker also notes that local sandbox processes can write text to the host clipboard. Check clipboard contents before pasting into a host application, password field, terminal, or support ticket. A clipboard payload can contain commands or sensitive data even when the workspace itself is isolated.
Review implicit execution hooks
- Git hooks and repository-level tool configuration.
- Package-manager lifecycle scripts.
- Editor tasks, language-server actions, and build presets.
- CI workflow files and deployment manifests.
- Shell startup files and generated command aliases.
- Archive extraction and post-install hooks.
Common failure modes and corrections
The code cannot install a package
First verify whether the test actually needs installation. If it does, add only the required registry or artifact host to the network policy, rerun the harmless probe, and record the new rule. Do not solve a blocked package download by granting unrestricted Internet access.
The program changed files on the host
You are probably using a read-write direct mount or another shared path. Stop the run, preserve the diff, and restore from a known-good copy. Use mountless or clone mode for the next attempt, and audit ignored files that may have been readable.
A secret appeared in logs or artifacts
Assume it is compromised. Revoke or rotate the credential, remove it from logs and shared storage, identify how it entered the environment, and repeat the test with a host-side proxy or disposable identity. Do not rely on deleting the visible copy if the process could have sent it over the network.
The network appears restricted but data still left
Inspect the active policy, wildcard domains, proxy logs, DNS behavior, and any host-side integration. Check whether a local MCP server, forwarded socket, clipboard, or mounted file provided another path. Narrow the policy and repeat with synthetic data.
The diff looks clean but behavior changed
Check Git hooks, ignored files, generated scripts, dependency lockfiles, package lifecycle actions, and CI configuration. A clean application diff does not prove that no executable behavior changed.
SSH operations succeed unexpectedly
Inspect whether an SSH-agent socket was forwarded. Disable it unless required, use a separate key with limited authorization, and review agent confirmation and signing logs.
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| Scenario | Recommended starting configuration | Reason |
|---|---|---|
| Pure algorithm test with synthetic input | Separate-kernel microVM or gVisor runtime, mountless workspace, no network, no credentials. | Nothing outside the test needs to be visible or reachable. |
| Repository analysis without host edits | MicroVM clone mode or an equivalent isolated copy, read-only host source, narrow package-registry access if required. | The agent can inspect a project while edits remain in a private workspace. |
| Build requiring live source edits | Disposable checkout, direct mount only for the required path, no sensitive files, tightly restricted network, temporary identity. | Live edits are visible, so the shared tree must be treated as an execution surface. |
| Code that calls an external API | Separate-kernel boundary, synthetic payloads, host-side credential proxy where available, destination allow-list, short-lived token. | The test needs a service but should not receive reusable secrets or unrestricted egress. |
| Multi-tenant or production-adjacent execution | Security review of host, runtime, orchestration, storage, network, logging, and incident response before launch. | Product documentation alone does not establish complete protection for this threat model. |
Pre-run and post-run checklists
Before starting
- Define the asset, trust boundary, expected output, and stop conditions.
- Choose a separate-kernel microVM or application-kernel runtime appropriate to the risk.
- Use mountless mode unless a repository is genuinely required.
- If sharing a repository, inspect untracked, ignored, hidden, and generated files.
- Use clone mode for isolated edits; reserve direct mode for deliberate live changes.
- Disable network access or configure a narrow, reviewed allow-list.
- Confirm UDP behavior and inspect wildcard domain rules.
- Remove credentials, SSH-agent forwarding, and host services that are not required.
- Use synthetic data and disposable identities.
- Record the initial workspace and active policy.
After finishing
- Save logs, exit status, and network observations.
- Review every changed file, including ignored files and hooks.
- Inspect package scripts, build tasks, CI files, and deployment manifests.
- Check clipboard contents and any host-side MCP activity.
- Revoke temporary credentials and remove unnecessary network rules.
- Apply changes to the host only after a human review and a clean, reproducible test.
What the available documentation does not prove
Docker’s and gVisor’s official pages explain architecture and configuration mechanisms. They do not supply a universal attack-resistance ranking, comparable performance figures, exhaustive coverage of every escape technique, or a guarantee that a particular deployment is secure. Defaults, supported integrations, and policy behavior can change, so verify the settings in the version and environment you operate.
The most defensible claim is narrower: a separate-kernel or application-kernel boundary, minimal sharing, restricted egress, and externalized credentials can substantially reduce exposure compared with running arbitrary generated code with unrestricted host permissions. The remaining risk is determined by the exact runtime, host, integrations, policies, and operational review around that boundary.
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