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The safest way to run model-generated code is to combine independent controls rather than rely on one container setting. Deny network egress by default, expose only explicitly mounted project and model directories, remove credentials and host sockets, filter system calls, drop Linux capabilities, and run as an unprivileged identity. Treat GPU access as a separate device permission: a model can receive CUDA access without gaining broader network or filesystem rights when the runtime keeps those policies independent.
This guide explains how to design that isolation, how OpenShell, gVisor, and Docker Sandboxes differ, how to test the boundaries, and how to avoid common configuration mistakes.
Why sandboxing needs several independent controls
Model-generated programs must be treated as untrusted software. A coding agent may accidentally execute a destructive command, intentionally search for credentials, probe internal services, or exfiltrate files. A single boundary rarely covers all of those paths. For example, a container can restrict ordinary file paths while still exposing a Docker socket, or block outbound TCP while allowing DNS resolution to an internal service.
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- Network: default-deny egress, with destination-specific exceptions.
- Filesystem: explicit mounts, preferably read-only, with a narrowly scoped writable workspace.
- Kernel interface: seccomp filtering, dropped capabilities, and no unnecessary host namespaces.
- Identity: an unprivileged user and group with no host credential access.
- Devices: only the GPU devices required by the workload.
- Isolation boundary: a process/container boundary, userspace kernel, or microVM chosen according to the threat model.
These layers should fail independently. If a model process escapes an application-level path check, the mount policy should still limit what it can see; if it obtains a file, the network policy should still prevent exfiltration.
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Start with a threat model and a resource inventory
Before selecting a runtime, write down what the model needs and what it must never reach. The answer differs for a batch inference job, a coding agent, and a training workload.
List required resources
- Model weights and tokenizer files, usually mounted read-only.
- Input datasets and source code, with read-only access unless edits are required.
- A specific writable workspace for generated files, build artifacts, and checkpoints.
- An ephemeral temporary directory for caches and compiler output.
- Package mirrors, model registries, or internal APIs only when the task genuinely needs them.
- Specific GPU devices, runtime libraries, and shared-memory capacity needed by the framework.
List prohibited resources
- Host home directories and credential files.
- SSH agent sockets, cloud credential stores, and instance metadata endpoints.
- Docker or other container-engine sockets.
- Host process, IPC, and network namespaces unless there is a documented requirement.
- Unrelated source repositories, customer data, and backup locations.
- All unapproved Internet destinations and internal network ranges.
Assume that generated code will attempt credential discovery, directory traversal, symlink tricks, device enumeration, and network exfiltration. Design the policy before launching the workload, then test those exact behaviors.
Build a default-deny network policy
Network isolation is strongest when the sandbox starts with no outbound destinations and receives a short allowlist. An allowlist should name the required service or destination, port, and protocol rather than broadly enabling “Internet access.”
Allow only task-specific destinations
Typical exceptions include an internal package mirror, a model endpoint, or one API used by the agent. Keep each exception separate so it can be removed and audited independently. If a package can be vendored into the image, do that instead of granting a live package-registry connection.
- Deny all outbound traffic by default.
- Permit only the required destination and port for each service.
- Decide explicitly whether DNS is needed; DNS access can reveal internal names even when HTTP is blocked.
- Block link-local and cloud metadata addresses unless the workload is specifically designed to use them.
- Log denied and allowed connection attempts, including destination and process identity.
Control ingress and lateral movement
Most model jobs need no inbound connections. Do not expose a listening service merely because a development tool expects one. If an internal callback is required, bind it to a dedicated interface and allow only the known caller. Prevent access to host and neighboring workloads by avoiding host networking and by filtering private address ranges that are outside the task’s allowlist.
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Do not confuse package installation with unrestricted access
An agent that can install arbitrary packages can fetch executable code from an untrusted registry. Prefer a curated mirror, prebuilt images, or an approval step that resolves dependencies outside the sandbox. If installation must occur inside the sandbox, keep the network exception narrow and remove it for later offline execution.
Define the filesystem with explicit mounts
Filesystem policy should describe what is visible, not merely what is writable. Start with a minimal read-only root image where practical. Add only the model, dataset, and project paths needed for the task.
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- Mount model weights and reference datasets read-only.
- Expose one dedicated writable workspace for generated code and outputs.
- Use an ephemeral temporary directory so caches disappear with the sandbox.
- Keep build caches separate from source and secrets.
- Do not mount the host home directory, broad parent directories, or “just in case” paths.
Read-only mounts prevent modification, but they do not automatically protect data from being copied. A process that can read a file may still send it over an allowed network connection. Pair read permissions with the network allowlist and with an explicit data-classification decision.
Exclude credentials and control sockets
Never expose SSH keys, cloud credential files, browser profiles, token caches, or environment files containing secrets. Keep SSH agents and cloud metadata inaccessible. Container-engine sockets are especially dangerous: access to one can amount to control over workloads and host-mounted files outside the sandbox.
Consider mediated filesystem access
Some runtimes mediate every filesystem operation through a separate component. Google gVisor documents a stricter mode in which directfs is disabled. In that mode, the sandbox uses fewer capabilities and stricter seccomp filters, while the Gofer process handles filesystem operations. This increases mediation and isolation, but filesystem operations can cost more than direct access. Directfs is faster, yet it exposes a larger performance-oriented trust surface and still limits access to trees presented by the Gofer.
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Defend against path and link attacks
- Test attempts to traverse with
..and to follow symlinks into unmounted locations. - Keep writable directories separate from sensitive read-only mounts.
- Use runtime-supported mount options that prevent device files and set-user-ID behavior where available.
- Ensure temporary files cannot be used to replace a privileged path or configuration.
Reduce the kernel attack surface
Filesystem and network rules do not replace kernel hardening. Apply a seccomp profile that permits only the system calls required by the language runtime, GPU libraries, and workload. Drop Linux capabilities that are not needed; most model jobs do not need administrative capabilities, raw networking, or the ability to load kernel modules.
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Use a dedicated non-root user and group inside the sandbox. An unprivileged identity limits the damage from application vulnerabilities and makes file ownership predictable. Avoid set-user-ID binaries and unnecessary privilege transitions. Keep host user namespaces and namespace-sharing options disabled unless the workload requires them and the security consequences are understood.
Avoid broad host namespace sharing
Host PID, IPC, and network namespaces expose information or communication channels that are normally outside the sandbox. Leave them isolated by default. If a profiler or accelerator requires one, document the reason, restrict the duration, and test what becomes visible.
Give GPU access without widening other permissions
GPU passthrough is a device allocation decision, not a reason to relax network or filesystem policy. Expose only the GPU devices and runtime components required by the job. Keep the same deny-by-default network rules, mount list, seccomp profile, capabilities, and unprivileged identity used for a CPU-only run.
Verify that GPU enablement is independent
NVIDIA OpenShell documents this separation explicitly: its --gpu option exposes the GPU device while Landlock filesystem restrictions, default-deny network enforcement, seccomp syscall filters, and the unprivileged process identity remain active and unchanged. Its documentation summarizes the design as “GPU passthrough is independent of security policy.” The same enforcement model applies to remote GPU access in the documented design.
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Do not infer that every runtime behaves this way. Compare the policy before and after enabling CUDA, then inspect device nodes, mounts, capabilities, and network interfaces. A GPU-enabled profile should not silently add host paths, privileged mode, host networking, or broad device access.
Check driver and CUDA compatibility
GPU isolation depends on the runtime, host driver, and CUDA versions working together. Validate the exact driver and CUDA combinations supported by the chosen sandbox before production use. A failed compatibility check can tempt operators to enable broader privileges as a workaround.
Compare the main isolation approaches
No runtime is universally safest. Evaluate the boundary, policy expressiveness, performance cost, and operational fit against your workload.
| Runtime | Isolation boundary | Filesystem approach | Network and identity controls | GPU approach | Main trade-off |
|---|---|---|---|---|---|
| NVIDIA OpenShell | Agent sandbox with layered policy controls | Landlock restrictions and explicit sandbox policy | Default-deny network, seccomp filters, and unprivileged identity remain active when GPU is enabled | Local and documented remote GPU allocation through --gpu |
Adoption and operational maturity must be evaluated for your organization; no comparable published overhead figure is stated in the official material. |
| Google gVisor | Userspace kernel boundary | Gofer-mediated access when directfs is disabled; directfs is faster but has a larger performance-oriented trust surface |
Sandboxed system calls are constrained before reaching the host kernel; apply network policy separately | nvproxy proxies application interactions with the NVIDIA driver for supported CUDA workloads |
Stronger filesystem mediation can add workload-specific filesystem overhead, and supported driver/CUDA versions must be verified. |
| Docker Sandboxes | MicroVM environment | Only resources explicitly shared with the sandbox are available | Governance controls cover network, filesystem, and MCP access; configure each shared resource deliberately | Verify the supported device-sharing path for the selected workload and image | MicroVM startup and resource overhead, plus the policy complexity of deciding exactly what to share. |
Official material reviewed for these runtimes does not provide a comparable owner-and-date benchmark for latency, throughput, or overhead. Measure your own workload instead of treating an unverified performance claim as a security property.
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Implementation sequence for a new sandbox
- Document the threat model. Assume generated code is malicious, identify sensitive data, and list the exact model, dataset, API, package, and GPU requirements.
- Build a minimal image. Remove compilers, shells, package managers, and debugging tools that the task does not need. If a compiler is required, keep its output in the dedicated workspace.
- Set the network to deny by default. Add only destination-specific exceptions for required mirrors, model services, or internal APIs. Log every exception and denial.
- Create explicit mounts. Add read-only model and dataset mounts, one narrowly scoped writable project directory, and an ephemeral temporary directory. Do not mount host credentials or engine sockets.
- Apply kernel and identity restrictions. Use seccomp, drop unnecessary capabilities, run as a non-root user, and leave host PID, IPC, and network namespaces disabled.
- Allocate the GPU last. Add only the required device capability, then compare the resulting mounts, capabilities, interfaces, and syscall policy with the CPU profile.
- Exercise adversarial cases. Test unauthorized DNS and HTTP destinations, private-address access, directory traversal, symlink escapes, credential reads, device enumeration, and container-engine socket access.
- Record expected failures. A blocked connection, denied file open, or inaccessible device should produce an observable error without crashing the supervisor or revealing secrets.
- Patch continuously. Keep the host kernel, GPU driver, runtime, sandbox components, base image, and model dependencies current. No cited runtime promises to be escape-proof.
How to test the policy before trusting it
Network tests
- Attempt an HTTPS request to an unapproved public destination and confirm it is denied and logged.
- Attempt DNS queries for internal names and verify that DNS behavior matches your policy.
- Try link-local and cloud metadata addresses.
- Confirm that an allowed endpoint cannot be used as an unrestricted proxy to other destinations.
Filesystem tests
- Read every parent directory above the workspace and confirm that unmounted paths are inaccessible.
- Attempt to open common credential locations, SSH agent sockets, and environment files.
- Create symlinks and use traversal sequences to target read-only or unmounted trees.
- Try writing to model and dataset mounts and verify that writes fail.
Kernel and device tests
- Enumerate devices and confirm that only the assigned GPU capability appears.
- Attempt privileged system calls, capability changes, module loading, and raw packet operations.
- Check visibility of host processes, IPC objects, and network interfaces.
- With CUDA enabled, repeat all network, mount, credential, and syscall tests; GPU access must not change their results.
Observability and incident response
Isolation is easier to trust when policy decisions are visible. Collect sandbox identity, image digest, policy version, mount list, GPU allocation, allowed destinations, denied connections, denied file operations, and termination reason. Send logs outside the sandbox so a compromised process cannot erase them.
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Give each run a short-lived identity and separate its writable output from other jobs. If a policy violation occurs, preserve logs and the workspace for investigation, revoke temporary network exceptions, rotate any credential that may have been exposed, and rebuild from a known-good image rather than continuing the same sandbox.
Choosing the right boundary
Use a layered process or container policy when
The workload is short-lived, the host is tightly patched, the required mounts are simple, and the runtime provides enforceable network, filesystem, syscall, capability, and identity controls. This approach can be operationally efficient, but it depends heavily on correct configuration and host hardening.
Evaluate gVisor when filesystem mediation is the priority
Disabling directfs moves filesystem operations through the Gofer and applies stricter seccomp and capability settings. Measure the actual application because I/O-heavy builds and data pipelines may pay a noticeable workload-specific cost. Confirm the GPU driver and CUDA versions supported by nvproxy before committing to production.
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Docker describes its AI Sandboxes as microVMs that let agents execute code, install packages, and use tools without accessing host resources beyond those explicitly shared. A microVM is not a substitute for least-privilege mounts or network policy: every shared resource still expands the attack surface, and startup and resource overhead must be measured.
Quick Recap
Common mistakes and their fixes
| Mistake | Why it is dangerous | Fix |
|---|---|---|
| Allowing all outbound Internet traffic for convenience | Generated code can exfiltrate files, download payloads, or scan internal services. | Start with deny-all egress and add logged, destination-specific exceptions. |
| Mounting the whole home directory | Tokens, SSH keys, browser data, and unrelated projects become readable. | Mount only named project, model, dataset, and temporary paths. |
| Exposing a container-engine socket | The process may control other containers or obtain host files. | Keep sockets out; use a narrowly scoped external build or approval service if needed. |
| Running as root or retaining broad capabilities | A process compromise has a larger kernel and filesystem impact. | Use an unprivileged identity, seccomp, and capability dropping. |
| Enabling GPU by switching to privileged mode | GPU access becomes coupled to unrestricted devices, mounts, or namespaces. | Use a runtime’s device allocation feature and verify that other policies remain unchanged. |
| Assuming a userspace kernel or microVM is escape-proof | Host, runtime, driver, and configuration vulnerabilities still matter. | Patch continuously, minimize sharing, and maintain adversarial tests. |
Practical decision checklist
- Is every outbound destination named, justified, and logged?
- Are model and dataset mounts read-only?
- Is there exactly one intentionally writable workspace?
- Are host credentials, metadata services, and engine sockets inaccessible?
- Does the process run unprivileged with unnecessary capabilities removed?
- Are host PID, IPC, and network namespaces isolated?
- Does GPU enablement leave network, filesystem, seccomp, and identity policies unchanged?
- Have traversal, symlink, credential, DNS, HTTP, device, and socket attacks been tested?
- Are runtime, kernel, driver, and sandbox updates tracked and applied?
- Can operators reconstruct what policy and resources a run used?
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