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No single sandbox boundary is sufficient for every threat model. Choose the runtime and policy strength according to what the model can access, what it must do, and the performance cost you can measure and accept.
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What a secure AI sandbox must prevent
Assume code produced by an AI agent can be intentionally malicious, accidentally dangerous, or capable of discovering credentials and exfiltrating data. A useful sandbox therefore has to constrain three independent resource classes:
- Network: unauthorized DNS lookups, HTTP or other outbound connections, access to cloud metadata services, and data exfiltration.
- Filesystem: reads outside the project and model data, writes to host directories, traversal through symlinks, and access to credential files or container-engine sockets.
- Devices and kernel interfaces: GPUs, system calls, process namespaces, capabilities, and other paths that can turn a process compromise into a host compromise.
These controls should be enforced independently. If a workload needs CUDA, that should add only the required GPU device capability; it should not change the network allowlist, mount set, syscall filter, or process identity.
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The layered control model
1. Default-deny network policy
Start with no network egress. Add destination-specific exceptions only for services the workload genuinely needs, such as an approved package mirror, model endpoint, or internal API. A hostname-level allowlist is easier to review than unrestricted Internet access, but the enforcement point must also account for DNS and direct-address connections so that an agent cannot bypass a name-based rule.
Log every denied and permitted connection. Review exceptions as policy changes, not as ad hoc fixes made after a job fails. If a build needs a package, prefer a controlled mirror over allowing arbitrary package registries. If inference uses a remote endpoint, permit that endpoint and its required DNS path only.
2. Explicit filesystem exposure
Make the root image as small and read-only as practical. Mount only the project files, model weights, datasets, configuration, and temporary workspace required by the job. A typical arrangement has read-only mounts for source and model data, one narrowly scoped writable project directory, and an ephemeral temporary directory that is deleted with the sandbox.
Do not expose host credential files, SSH agents, cloud credential directories, unrelated home directories, cloud metadata endpoints, or Docker and other container-engine sockets. A socket is not just a file: it can be an administrative API that lets a process create containers or otherwise control the host. Keep it outside the sandbox unless the workload has a separately designed, least-privileged broker.
3. System-call and privilege reduction
Use seccomp or an equivalent syscall policy to remove kernel interfaces the workload does not need. Drop Linux capabilities rather than running with a broad default set. Run the model process as an unprivileged identity and avoid host PID, IPC, or network namespaces unless a specific requirement has been documented.
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These measures are complementary. A process that escapes a language-level restriction still faces a reduced syscall surface, fewer capabilities, and a non-root identity. They do not make a sandbox escape impossible, so the host kernel, runtime, and drivers still need continuous patching.
GPU access is a separate capability
GPU passthrough can be granted without relaxing other controls when the runtime treats it as an independent device allocation. NVIDIA OpenShell documents this separation: its --gpu option exposes GPU hardware while Landlock filesystem restrictions, default-deny network enforcement, seccomp syscall filters, and an unprivileged process identity remain active and unchanged. The documentation summarizes the design as “GPU passthrough is independent of security policy.”
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That separation is the property to verify in any runtime. Compare the policy before and after enabling CUDA, then test both configurations. Confirm that the GPU flag changes only device visibility and the libraries needed by the workload, not mounts, network routes, capabilities, or namespace settings. Remote GPU access in OpenShell follows the same enforcement model according to its GPU documentation.
GPU access still increases the attack surface. Drivers and device interfaces are complex, and a model can use the GPU to consume resources or trigger denial of service even when it cannot read host files. Allocate only the devices and capacity required, isolate jobs that do not need a GPU, and monitor device use.
Choosing a sandbox runtime
The three runtime families described here provide different isolation boundaries. None is universally safest; the right choice depends on the threat model, workload compatibility, startup and I/O requirements, and the operational controls your team can maintain.
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| Runtime | Isolation boundary | Filesystem mediation | Network and identity controls | GPU approach | Main trade-offs |
|---|---|---|---|---|---|
| NVIDIA OpenShell | Agent sandbox with policy controls around the process | Landlock-based restrictions and explicit policy | Default-deny network enforcement, seccomp filters, and unprivileged process identity | GPU allocation is independent of the YAML security policy; remote GPU access uses the same enforcement model | Broad policy coverage; adoption depends on whether your organization can operate this runtime |
| Google gVisor | Userspace-kernel boundary | With directfs disabled, filesystem operations are handled by the Gofer process; the sandbox uses stricter seccomp filters and fewer capabilities |
Syscalls reaching the host kernel are constrained by the sandbox model | nvproxy proxies application interactions with the NVIDIA driver for supported CUDA workloads |
Stronger filesystem mediation with additional filesystem-operation overhead; driver and CUDA compatibility must be verified |
| Docker Sandboxes | MicroVM boundary | Only resources explicitly shared with the sandbox are available | Documentation describes governance controls for network, filesystem, and MCP access | Availability and behavior depend on the resources and devices shared by the policy | Distinct kernel boundary with microVM startup and resource overhead; sharing policy must be precise |
There is no published, directly comparable owner-and-date benchmark in the available official material for latency, throughput, or overhead across these three runtimes. Measure your own model, filesystem, and GPU workload instead of treating a generic performance claim as a guarantee.
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When OpenShell fits
OpenShell is suitable when you want one policy layer covering filesystem, network, syscalls, and process identity, while retaining an explicit GPU switch. Treat the YAML security policy as the source of truth and verify that every mount, destination, and device is intentional. The documented independence of --gpu is particularly useful for teams that need CUDA without widening access.
When gVisor fits
gVisor inserts a userspace kernel boundary between the application and the host kernel. Its stricter filesystem mode disables directfs; the sandbox then runs with fewer capabilities and stricter seccomp filters, while filesystem operations are performed through the Gofer process. This increases mediation but can add filesystem-operation overhead. The faster directfs mode has a larger performance-oriented trust surface and is limited to filesystem trees exposed by the Gofer.
For CUDA, gVisor uses nvproxy to proxy application interactions with the NVIDIA driver. Supported CUDA workloads can run while seccomp constrains syscalls reaching the host kernel, but you must verify the driver and CUDA versions supported by your chosen gVisor release before production deployment.
When a microVM sandbox fits
Docker describes its AI Sandboxes as microVM environments in which agents can execute code, install packages, and use tools without accessing host resources beyond those explicitly shared. A microVM supplies a distinct kernel boundary, which is attractive for high-risk, arbitrary code. It also introduces startup and resource overhead and does not remove the need for careful network, filesystem, MCP, and device-sharing policy.
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Implementation procedure
Step 1: Write the threat model first
- List the data the model may read: source, datasets, model weights, build artifacts, and approved service responses.
- List the data it must never read: host credentials, SSH keys or agents, cloud credentials, unrelated user directories, container-engine sockets, and cloud metadata.
- List required destinations and protocols, including package mirrors, model APIs, internal services, and DNS.
- Decide whether the workload needs a GPU, which devices or partitions it needs, and whether multiple jobs may share that capacity.
- Define the consequences of compromise. A code-generation task handling proprietary source needs a tighter egress policy than a disposable benchmark using public data.
Step 2: Build the network policy
- Set egress to deny by default.
- Allow only named services required for the task. Keep package installation tied to an approved mirror where possible.
- Control DNS as well as HTTP, HTTPS, and other outbound protocols. Test direct IP connections so an agent cannot evade a hostname rule.
- Block cloud metadata endpoints and any management network that is not part of the workload.
- Record the policy version and log accepted and denied attempts for later review.
Step 3: Define mounts and write paths
- Use a minimal, preferably read-only root image.
- Expose project source, model files, and datasets through explicit read-only mounts unless the job must modify them.
- Create one narrowly scoped writable workspace for generated files and build output.
- Provide an ephemeral temporary directory; do not make a host temporary directory a hidden back door.
- Check every parent directory and symlink reachable from a mount. A permitted path should not lead into an unintended host tree.
- Keep credential files, SSH agents, cloud metadata, home directories, and container-engine sockets outside the mount set.
Step 4: Reduce kernel privileges
- Run the process as an unprivileged identity.
- Apply a seccomp profile that permits only the syscalls required by the runtime and workload.
- Drop capabilities that are not explicitly needed; do not rely on a container’s default capability set.
- Avoid host PID, IPC, and network namespaces unless the threat model and application architecture require them.
- Keep the host kernel, runtime, and security components patched. Isolation is a moving dependency, not a one-time configuration.
Step 5: Add the GPU deliberately
- Run the workload without a GPU first and record the effective mounts, network policy, identity, capabilities, and syscall profile.
- Enable only the required GPU device or allocation. In OpenShell this is represented by
--gpu. - Compare the effective policy after GPU enablement. The network and filesystem policy should be unchanged.
- Verify driver, CUDA, and runtime compatibility, especially when using gVisor’s
nvproxy. - Monitor GPU memory, utilization, and job lifetime so a model cannot monopolize the device indefinitely.
Step 6: Test before allowing untrusted code
- Attempt DNS and HTTP connections to unauthorized destinations and confirm denial and logging.
- Attempt traversal above each mounted directory, including through symlinks, and verify that host paths remain inaccessible.
- Try to read common credential locations, SSH agents, cloud metadata, and environment-provided secrets.
- Probe for Docker or other container-engine sockets and confirm that they are absent, not merely unreadable.
- Enumerate devices and inspect process, PID, IPC, and network namespaces to ensure the exposure matches policy.
- Run the same adversarial tests with GPU access enabled. Device visibility must not alter the other results.
- Repeat tests after runtime, kernel, driver, or policy updates.
Filesystem mediation choices in detail
Explicit mounts versus mediated operations
Explicit mounts make the data boundary easy to reason about: if a directory is not shared, the model should not see it. A userspace-kernel design such as gVisor with directfs disabled adds mediation for filesystem operations, reducing the set of operations performed directly by the sandbox process. The stronger mode can cost I/O performance, so measure operations that matter to your model pipeline rather than relying on a generic overhead estimate.
Read-only data and ephemeral state
Model weights, reference datasets, and source snapshots should be read-only unless mutation is part of the task. Generated output belongs in a separate writable workspace that can be inspected, copied out, or destroyed. Temporary files should be ephemeral and scoped to the job; retaining them on the host can turn a transient secret into a persistent one.
Symlinks, archives, and extraction
Path checks must account for symlink resolution and archive extraction. An agent can create a link inside a writable directory that points toward a mounted parent, or extract an archive containing paths that attempt to escape the workspace. Test these cases explicitly and make the runtime’s path mediation enforce the final resolved location, not just the text of the requested path.
Network policy failure modes
Package installation breaks
Do not respond by opening the entire Internet. Identify the package mirror and DNS dependencies, allow those destinations, and log the exception. For reproducible builds, pre-stage packages or use an internal mirror so the sandbox does not need arbitrary registry access.
The model uses an unapproved endpoint
Keep the request denied, inspect the destination and task requirement, and decide whether a narrowly scoped exception is justified. If the destination is not essential, change the agent or tool configuration rather than weakening egress policy.
DNS is allowed but direct connections are not
This is expected in a destination-specific policy. A hostname resolving to an address does not automatically authorize every address or service. Define how the runtime maps names to permitted destinations and test both name-based and direct-address attempts.
Observability and operations
Record the effective policy alongside each job: runtime version, kernel and driver versions, image digest, mounts, network destinations, identity, capabilities, syscall profile, and GPU allocation. This makes an incident or unexpected denial diagnosable without guessing which layer changed.
Alert on repeated denied egress, attempts to read credential paths, traversal failures, device enumeration, and access attempts to container-engine sockets. These events may indicate either a malicious model or a legitimate tool that has outgrown its policy; in both cases they deserve review before an exception is granted.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minutePatch the host kernel, GPU driver, sandbox runtime, and policy components continuously. A sandbox is not an escape-proof guarantee, and no cited source promises that it is. Keep high-value credentials outside the sandbox even when a runtime appears strongly isolated, and use separate hosts or accounts for especially sensitive workloads.
Quick Recap
A practical decision framework
| Requirement | Policy emphasis | Runtime question |
|---|---|---|
| Untrusted code with sensitive source | Strict egress deny, read-only mounts, no credentials or sockets, strong syscall and capability reduction | Can the runtime provide the required filesystem mediation and auditability without sharing host resources? |
| Heavy filesystem I/O | Explicit mounts and carefully measured writable paths | Is the performance cost of stronger mediation acceptable for this workload? |
| CUDA inference or training | Independent GPU allocation, unchanged network and filesystem policy, driver compatibility checks | Does enabling the GPU preserve the same effective policy? |
| Highest kernel-boundary requirement | Separate kernel boundary and minimal shared resources | Can a microVM’s startup and resource overhead fit the service-level target? |
| Existing policy-centric agent workflow | One auditable policy for network, filesystem, syscalls, and identity | Can the organization operate and update a runtime such as OpenShell? |
What to verify in a production review
- Network egress is deny-by-default, destination-specific, logged, and tested against DNS bypasses.
- Only required project, model, dataset, and temporary paths are mounted; writable space is narrow and ephemeral.
- Host credentials, cloud metadata, SSH agents, unrelated home directories, and container-engine sockets are absent.
- Seccomp, capability dropping, and an unprivileged identity are active.
- Host PID, IPC, and network namespaces are not shared without a documented requirement.
- GPU allocation is explicit, limited, monitored, and proven not to relax other policy layers.
- Adversarial tests cover unauthorized network access, path traversal, symlinks, credential reads, device enumeration, and socket discovery.
- Kernel, driver, runtime, and sandbox updates trigger regression tests.
- There is a recorded rollback or isolation response when a policy exception proves unsafe.
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