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The right choice depends on what is untrusted, whether workloads must distrust one another, which data and credentials they can reach, hardware requirements, startup and operating cost, and who is responsible for maintaining the isolation stack.
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First define “untrusted”
An untrusted model is more than a file with an unfamiliar architecture. Its loader, tokenizer, custom code, dependencies, inference script, plugins, or input-processing path may execute instructions, open files, make network connections, consume excessive CPU or memory, or attempt to read credentials. A model can also be untrusted because its owner is unknown, because it comes from a user-uploaded repository, or because several customers’ workloads must not influence one another.
Write down the boundary you need before choosing a platform:
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- Application isolation: Can the model affect the web app that invokes it?
- Workload isolation: Can one model run affect another run?
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- Resource isolation: Can it exhaust CPU, memory, disk, GPU memory, file descriptors, or network capacity?
No deployment choice answers all of these automatically. Isolation is a system design, not a visibility label.
What Hugging Face Spaces actually provide
Spaces are application hosting
Spaces host an application from a repository. The current Spaces overview lists Gradio, Docker, and static HTML SDK options. A Space can expose a model demo, API-like interface, or static front end, but the unit being deployed is the application and its runtime—not a disposable VM created for every untrusted model call.
Visibility settings are useful for publication and access control:
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- Public: the repository and running Space are openly visible according to the Space’s access rules.
- Protected: the source can remain private while the running application is reachable through an embed URL or configured domain.
- Private: access is restricted to the people or organization permitted to use the Space.
These settings decide who can see or reach the app and source. They do not make each inference a separate trust domain. A private Space can still execute hostile code inside the same application runtime, and a public Space can be perfectly reasonable when the code and inputs are intentionally published as a demo.
Compute, plans, and hardware
Static Spaces are described as free in the overview. Gradio and Docker Spaces run on compute and generally require an eligible paid plan; the documentation also describes a ZeroGPU exception for some free personal accounts. Plan eligibility and limits change, so verify the current terms in the Spaces documentation before budgeting or promising availability.
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Docker Spaces allow hardware choices, including GPU options where the selected plan and hardware support them. GPU hardware is not available during image build, so dependencies that require a GPU must be handled at runtime rather than assumed to be present while the image is being built.
Secrets and the Docker runtime
Spaces provide configured runtime secrets as environment variables. Do not hard-code credentials in repository files, and do not pass secret-bearing environment variables, request headers, or mounted files to code that you do not trust. Model code can read its process environment and may print it to logs or return it through an application response.
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Docker Space containers run as UID 1000 according to the Docker Spaces documentation. A non-root UID is useful, but it is not a substitute for a VM boundary or a complete threat model. Review filesystem permissions, mounted paths, service tokens, and network reachability separately.
Persistence and restarts
Ordinary writable disk in a Docker Space is lost when the Space restarts. If a model cache, uploaded artifact, or result must survive restarts, use the documented Storage Buckets option or another external storage service, with credentials and access scopes limited to exactly what the application needs. Persistent storage increases the consequences of a compromised process: an attacker may be able to read or alter data that remains available to later runs unless the storage layout and permissions isolate tenants.
Dedicated Hugging Face sandbox VMs
The boundary
Hugging Face’s sandbox documentation describes a dedicated mode backed by Jobs: one sandbox gets one real VM. Its documentation calls this VM-level isolation the strongest option, says it supports hardware flavors including GPUs, and identifies it as the right choice for mutually untrusted code.
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The distinction matters when each workload must distrust every other workload. A fresh VM gives the job a separate guest operating system boundary rather than placing several runs in one long-lived application process or shared container.
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The untrusted command and the sandbox server still share the same VM. VM isolation separates that VM from the outside environment; it does not make the command safe to run alongside sensitive control-plane code inside the guest. Keep the server’s credentials and control interfaces out of the untrusted process, minimize mounted data, and treat anything reachable from the sandbox—including credentials included in a request—as potentially exposed.
Lifecycle discipline
Hugging Face explicitly recommends one dedicated sandbox per untrusted workload and calling kill() when it is finished instead of reusing one across trust boundaries. Reuse creates cross-run state: files, processes, caches, memory remnants, package installations, and credentials can survive longer than intended. A disposable lifecycle also makes incident response and accounting easier.
Pooled sandboxes are a different mode
The pooled mode is a lighter boundary inside a shared VM. It uses distinct UIDs and Landlock rules to restrict filesystem operations, but the documentation positions pools for workloads within one trust boundary and directs users to dedicated VMs for mutually distrusting code. Do not describe a pooled sandbox as equivalent to a fresh VM per job.
How a local gVisor design differs
Userspace system-call interception
Google’s gVisor documentation describes gVisor as an open-source workload-isolation solution for safely running untrusted code, containers, and applications. Instead of passing application system calls directly to the host kernel, gVisor handles them through a userspace application kernel. This reduces the amount of host-kernel surface directly exposed to the workload compared with an ordinary container.
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That is a different design from both a standard container and a hardware-virtualized VM. Compatibility can be lower for applications that depend on unusual kernel behavior, and the operator must select and maintain the runtime configuration. A gVisor deployment is only as strong as its surrounding controls: host hardening, runtime updates, filesystem mounts, device exposure, network policy, logging, and resource limits still matter.
What your team must operate
- Patch the host operating system, container runtime, and gVisor components.
- Run each mutually distrusting workload in a deliberately separated sandbox rather than assuming one shared pool is sufficient.
- Expose only required directories, devices, sockets, and environment variables.
- Apply CPU, memory, process-count, file-size, disk, and execution-time limits.
- Restrict egress and ingress with an explicit network policy; do not rely on a container’s default network.
- Collect logs and audit events without allowing untrusted code to overwrite or read sensitive logs.
- Plan for cleanup so crashed or malicious processes cannot retain state for the next run.
Side-by-side comparison
| Decision axis | Hugging Face Space | Dedicated managed sandbox VM | Local sandbox such as gVisor |
|---|---|---|---|
| Primary purpose | Host a shareable ML demo or application from a repository. | Execute a workload inside its own VM-backed Job. | Run workloads under an isolation layer operated by your team. |
| Isolation boundary | Space access, visibility, and app runtime; not a fresh VM for every invocation. | Separate VM per dedicated sandbox; the workload and sandbox server share that VM. | Userspace system-call boundary whose strength depends on deployment and host configuration. |
| Mutually distrusting jobs | Do not infer safety from public, protected, or private visibility. | Use one dedicated sandbox per untrusted workload and destroy it afterward. | Requires deliberate per-job separation and carefully configured host controls. |
| Secrets | Runtime secrets are available to application code; never expose them to untrusted model code or logs. | Treat credentials reachable from the sandbox, including request credentials, as potentially exposed. | Keep host credentials and sensitive mounts outside the sandbox; scope network access narrowly. |
| Operations | The platform hosts the app, but you manage visibility, settings, compute, storage, and application behavior. | The service manages VM lifecycle; you still account for per-sandbox startup and compute. | You manage host hardening, runtime updates, resource limits, network policy, and observability. |
| Hardware | Docker Spaces support selectable hardware where the plan provides it; GPU is unavailable during image build. | Dedicated Jobs support hardware flavors including GPUs, according to the sandbox documentation. | Determined by your local hardware and runtime configuration. |
| Persistence | Ordinary Docker Space disk is lost on restart; use Storage Buckets or external storage deliberately. | Design the sandbox as disposable and export only vetted results. | Choose and secure persistence yourself; shared caches can become a cross-job data leak. |
Choose by workload
Use a Space for an intentionally hosted demo
A Space fits when the repository, application behavior, and user interaction are the product. Examples include a public image-generation demo, an internal prototype for a trusted team, or a front end that calls a separately isolated inference service. Select visibility based on who should see the source and app, then configure secrets, storage, authentication, and rate limits as independent controls.
If users can upload arbitrary model files or trigger arbitrary custom code, the Space should be treated as an application front end—not as the sandbox itself. Put execution in a dedicated per-workload boundary and return only the result your app needs.
Use a dedicated managed VM for mutually untrusted execution
Choose the dedicated sandbox mode when workloads come from different users, repositories, or trust domains; when a model loader can execute custom code; or when the cost of a host compromise or cross-tenant data leak is high. Create a sandbox for the job, pass the minimum input, run the command, collect a validated result, and destroy the sandbox. Do not keep a VM alive merely to avoid startup work if the next workload has a different trust owner.
Use local gVisor when you need operational control
A local sandbox is appropriate when data cannot leave your environment, when you need a custom hardware or network topology, or when your team can own continuous security operations. It is not automatically safer than a hosted service. The team must prove that the chosen runtime, host configuration, credentials, mounts, and network policy satisfy its threat model.
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Deployment checklists
Before publishing a Space
- Decide whether the repository and running app should be public, protected, or private.
- Identify every model loader, plugin, dependency installation step, and subprocess the app can invoke.
- Place secrets in the platform’s runtime secret configuration, not in source files or model repositories.
- Remove secrets and sensitive prompts from logs, tracebacks, generated artifacts, and error responses.
- List required outbound hosts and block unnecessary network access.
- Set application-level limits for request size, execution time, concurrency, and model downloads.
- Choose persistence deliberately; assume ordinary writable disk disappears on restart.
- Keep untrusted execution outside the Space process when users can supply arbitrary code or model packages.
For each dedicated sandbox job
- Create a new dedicated sandbox for the single trust domain and workload.
- Choose the smallest hardware flavor that meets the model’s CPU, memory, or GPU needs.
- Pass only the input files and non-sensitive configuration required for that run.
- Keep control-plane credentials out of the command environment and filesystem.
- Apply time, process, memory, disk, and network limits before execution.
- Validate output as data rather than executing returned files or scripts.
- Export only approved results and logs.
- Call
kill()and discard the sandbox before processing a workload from another trust boundary.
For a local gVisor deployment
- Define the host, tenant, and workload boundaries in writing.
- Configure gVisor as the runtime for the untrusted workload, then test application compatibility with the exact model stack.
- Use read-only mounts wherever possible and create a separate scratch directory for each run.
- Do not mount the host container socket, cloud credential files, SSH agent, administrative sockets, or unrelated devices.
- Enforce cgroup or equivalent CPU, memory, process, and I/O limits at the host level.
- Apply default-deny network policy and permit only required destinations.
- Rotate and protect logs; prevent the workload from reading host logs or altering audit records.
- Destroy the sandbox and clear scratch data after completion, timeout, or failure.
Credentials, networking, persistence, and denial of service
Credentials
Assume a model can read anything placed in its process environment, current directory, mounted filesystem, request payload, or accessible metadata endpoint. Use short-lived, least-privilege credentials where possible. Separate credentials used to download a model from credentials used to serve an application, and never give model code a token that can administer the hosting account.
Network access
Network access is both a data-exfiltration path and a supply-chain path. An untrusted loader may download additional code, contact an attacker-controlled endpoint, or probe internal services. A private Space, container UID, or gVisor boundary does not by itself create a safe egress policy. Define allowed destinations, block internal address ranges unless required, and record connection attempts.
Persistent data
Separate immutable model artifacts from per-run scratch data. Give each workload a unique directory or storage namespace, and sanitize filenames and archives before extraction. When a run ends, delete temporary files and revoke access to exported artifacts that do not need to persist.
Resource exhaustion
Isolation must include availability. Limit request size, model size, sequence length, wall-clock time, concurrency, subprocess count, memory, disk growth, and GPU usage. Queue or reject work that exceeds policy rather than allowing one malformed model to consume the service for everyone.
Performance and cost expectations
Dedicated VMs add lifecycle and startup work, and each sandbox consumes compute for its lifetime. The Hugging Face documentation gives an approximate VM cold-start estimate, but it is a vendor operational estimate rather than a dated, independent benchmark or universal latency guarantee. Measure startup, model download, warm-up, inference, and teardown for your own hardware and model sizes.
A long-lived Space can reduce repeated startup for a trusted application, but keeping a process warm also preserves state and increases the impact of a compromise. A disposable VM or local sandbox may cost more per invocation while sharply reducing cross-run contamination. The correct comparison is total risk-adjusted cost, including engineering time for patching, monitoring, incident response, and storage cleanup—not only listed compute.
Common mistakes and safer corrections
- “Private means isolated.” Private visibility controls access to the Space; it does not create a per-run VM. Use a dedicated sandbox for mutually untrusted code.
- “A container is a VM.” Containers share more of the host environment than a VM-backed sandbox. Do not present a basic Docker Space as equivalent to a dedicated VM.
- “A non-root UID solves the problem.” UID 1000 reduces one class of permission risk, but credentials, network reachability, kernel exposure, and resource exhaustion remain.
- “gVisor removes host risk.” gVisor changes system-call handling; host configuration, updates, mounts, devices, and network policy still determine the overall boundary.
- “Reuse is cheaper and harmless.” Reusing a sandbox across trust boundaries leaves files, processes, caches, and credentials behind. Create one per untrusted workload and destroy it.
- “Secrets are safe because they are not in the repository.” Runtime environment variables and request headers are still visible to code running in that process. Scope and isolate them.
A practical decision rule
Ask one question first: Would I allow this model’s code to run beside the next user’s code, with access to the same credentials, filesystem, network, or process? If the answer is no, use a fresh dedicated VM-backed sandbox or an equivalently designed local boundary. If the answer is yes because the code, users, and data share one trusted domain, a pooled or application-level design may be sufficient after applying strict resource and network controls. Use Spaces for what they are best at—hosting an application or demo—and keep untrusted execution in a boundary designed for that threat model.
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