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Start with no network access and no host filesystem access, verify every artifact and revision before loading it, run as a non-root identity, enforce CPU, memory, process, disk, GPU, and request limits, and keep a disposable image or VM snapshot for rapid destruction and recreation. The steps below show how to choose the boundary and build it.
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What you are protecting when you run a local model
A model file is often described as data, but the complete local-AI stack is larger than the weights. The loader may deserialize complex objects; conversion utilities can execute scripts; an inference server may expose network listeners; plugins can add arbitrary capabilities; and user-supplied files can exploit parser bugs. A model that behaves normally during generation can still be accompanied by a compromised package, malicious extension, tampered download, or vulnerable runtime.
The assets at risk include your source code, documents, browser profiles, SSH keys, cloud credentials, environment variables, other containers, metadata services, GPUs, and the availability of the host. A useful sandbox therefore has to limit both confidentiality and blast radius. Preventing a model from reading your files is not enough if it can consume all host memory, reach an internal service, or invoke a privileged tool through the application that calls it.
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Write down the trust assumptions
- Model and runtime: Are the weights, tokenizer, quantizer, conversion program, inference engine, and plugins from publishers you trust?
- Users and tenants: Will different customers, teams, or unknown prompts share one process, GPU, VM, or host?
- Inputs: Can users upload documents, images, audio, archives, or serialized objects?
- Tools: Can generated text call a shell, browser, database, filesystem, code interpreter, or network client?
- Availability: What amount of CPU, RAM, VRAM, disk, processes, and request time may one job consume?
If any of these parties are mutually untrusted, put the workload in a dedicated VM or microVM rather than relying on a shared container kernel. Hugging Face describes a dedicated sandbox as a real VM suitable for mutually untrusted code, while Docker describes the microVM as its primary trust boundary. Shared sandboxes are intended for workloads inside one trust boundary and do not promise protection against every cross-sandbox attack.
Choose the isolation boundary
Containers, virtual machines, and microVMs can all be useful, but they solve different trust problems. The following comparison is a decision aid, not a claim that one technology is universally safest.
| Decision axis | Hardened container | Dedicated VM | MicroVM |
|---|---|---|---|
| Kernel and process boundary | Shares the host kernel; a kernel or runtime escape can affect the host. | Uses a separate guest kernel and a stronger boundary at the cost of a full guest OS. | Uses a lightweight virtual-machine boundary with a small guest environment; Docker identifies the microVM as its primary trust boundary. |
| Host filesystem exposure | Safe only when there are no broad bind mounts, host paths, or container-engine sockets. | Guest storage is separate by default; share only an explicit, preferably read-only channel. | Guest storage is separate by default; expose only the files required for the job. |
| Network policy | Can begin with no network and add a narrow allow-list or proxy. | Can use a separate virtual network, firewall, and egress gateway. | Can use a separate virtual network and policy while retaining fast disposable startup. |
| Credentials | Never pass broad host credentials or cloud metadata access; use short-lived, scoped tokens when unavoidable. | Keep credentials outside the guest where possible and inject only narrowly scoped, temporary values. | Apply the same credential discipline as a VM; a small guest does not make a credential broadening safe. |
| GPU use | Device pass-through can expose a powerful host resource and may create tenant-sharing risk. | Prefer a dedicated device or node for sensitive work; hardware-backed partitioning is needed for stronger sharing guarantees. | Use only when the accelerator and hypervisor provide the memory isolation you require; otherwise dedicate the device or node. |
| Startup and overhead | Usually the lightest option, but actual overhead depends on the runtime, model, host, and workload. | Typically has more guest-OS overhead and patching responsibility. | Designed to retain a VM boundary with lower startup and footprint than a traditional VM, but performance still depends on implementation and workload. |
| Patching and reproducibility | Pin the image digest, engine, libraries, and model revision; patch the host kernel and runtime. | Pin the guest image and model; patch both guest and host layers. | Pin the microVM image, guest components, runtime, and model; patch the host and microVM stack. |
| Best fit | One user or one trust domain, with a carefully constrained service and no hostile tenants. | Mutually untrusted code, high-sensitivity data, or a requirement for a separate kernel. | Short-lived or high-density jobs that still require a VM-level trust boundary. |
There is no universal sandbox performance percentage to quote. Primary guidance notes that performance varies with the runtime, model, hardware, and workload, so benchmark the exact model and policy you plan to deploy instead of assuming that a container or microVM has a fixed overhead.
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Acquire and verify the model before it enters the runtime
Record provenance
Download from a known publisher and record the exact repository or release, revision or commit, license, file names, conversion tools, and intended format. Do not treat a repository name as a permanent identity: tags and files can change. Keep a manifest beside the artifact that records when and where it was obtained.
Verify hashes
Calculate a cryptographic hash after downloading and compare it with a hash supplied through an independent, trusted channel. For a file on a Unix-like system, a basic check is:
sha256sum model-fileDo the same for tokenizer files, configuration, adapters, server binaries, container images, and conversion utilities. NIST SP 800-218A, published by NIST on July 26, 2024, calls for verifying the integrity, provenance, and security of acquired AI models and components. A successful hash check proves that the file matches the expected bytes; it does not prove that the publisher or the code is benevolent.
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Separate downloading, conversion, and inference
Use a disposable download or conversion environment with no access to production data. Inspect archives before extraction, reject unexpected executables or scripts, and keep the resulting model in a directory that the inference runtime can read but not modify. Conversion should happen before the model is admitted to the long-lived service. If a loader requires custom code, give that code the stronger VM or microVM boundary rather than weakening the production container.
Build a minimal, reproducible runtime
Pin every moving part
Pin the operating-system image, inference engine version, language libraries, tokenizer, model revision, adapter revisions, and container image digest. Capture the complete dependency lock file and the hardware or driver requirements. Rebuild from the pinned inputs instead of updating packages in a running sandbox.
Remove unnecessary software
A production image normally does not need a compiler, package manager, interactive shell, debugger, source tree, or build cache. Remove them unless the workload genuinely requires one. Fewer binaries mean fewer parser and privilege-escalation paths. Build the image in a separate environment, scan it, then run an immutable version for inference.
Use a non-root identity
Create a dedicated numeric UID and GID that owns only the temporary working directory. Run the server as that identity, drop all Linux capabilities, enable no-new-privileges, and apply a seccomp profile or equivalent syscall policy. A non-root process is not a substitute for a VM, but it limits damage when an application-level vulnerability is found.
Remove host access
Mount only what the model needs
Do not mount the host root directory, home directories, SSH directories, source trees, container-engine sockets, or cloud metadata endpoints. In particular, never expose /var/run/docker.sock to an untrusted model service: access to the engine socket can become control of the host’s containers and, depending on configuration, the host itself.
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Example container baseline
The following is a starting template for a single-user inference job. Replace the double-curly-brace values with your own pinned image digest, model path, and executable, and confirm that your runtime supports each option. The example intentionally has no network and no writable host mount.
docker run --rm
--name llm-job
--user 10000:10000
--read-only
--network none
--cap-drop=ALL
--security-opt=no-new-privileges:true
--pids-limit=256
--memory=16g
--cpus=8
--mount type=bind,src=/srv/models,dst=/models,ro
--tmpfs /tmp:rw,noexec,nosuid,size=1g
{{PINNED_IMAGE_DIGEST}}
{{INFERENCE_COMMAND}} --model /models/{{MODEL_FILE}}This is not a universal production command. The memory, CPU, process, and temporary-storage values must match your model and hardware, and a server that must accept requests needs a separately designed network path. If a flag is unsupported by your engine, implement the same control through the host, orchestrator, or VM policy rather than silently dropping it.
Start with no network, then add only a controlled path
Keep inference offline by default
Most local generation does not require Internet access. Start the inference process with egress disabled and no inbound listener. This blocks accidental telemetry, prompt exfiltration, malicious package downloads, and connections to internal services. It also makes the result reproducible.
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If the model or tokenizer must be downloaded, perform that operation in a dedicated acquisition job. Allow-list only the publisher endpoints needed for that job through a controlled proxy, record the response and hashes, then move the verified artifact into the offline inference environment. Do not give the long-running server the same network permission merely because the initial download needed it.
Expose an API through a narrow gateway
When remote clients need an API, put a gateway in a separate network segment. Permit only the gateway-to-inference connection, authenticate requests at the gateway, enforce request size and rate limits there, and encrypt data sent over the network. The llama.cpp security policy specifically warns against RPC backends and server exposure in untrusted networks and recommends encryption for data sent over a network.
Do not allow the model process to resolve arbitrary hostnames, scan private address ranges, call cloud metadata services, or open outbound connections. A proxy should enforce an explicit endpoint allow-list rather than merely logging traffic.
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Bound CPU, memory, storage, processes, and requests
Resource exhaustion is a security problem even when no code escapes. A prompt can trigger a very long generation, many parallel requests, oversized context, or repeated image and audio preprocessing. Set limits at more than one layer:
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- Memory: cap resident memory and reject requests whose context or batch size cannot fit.
- GPU and VRAM: reserve capacity per job and stop work that exceeds the allocation.
- Disk: limit model caches, temporary files, logs, and uploaded payloads.
- Processes and threads: apply a process limit and prevent unbounded worker creation.
- Network: cap request rates, concurrent connections, and response size.
- Lifecycle: use request timeouts, health checks, and automatic teardown for disposable jobs.
Choose values from measurements of the exact model and hardware. Keep enough headroom for the host and monitoring components; otherwise a limit intended to protect the host can still allow a noisy-neighbor failure inside the sandbox.
Handle GPUs as a separate isolation problem
Passing a GPU into a container or VM gives the workload access to a high-value shared resource. If mutually untrusted tenants share an accelerator, model inputs, outputs, caches, or memory remnants may become a cross-tenant leakage or side-channel risk. Do not assume that a process boundary alone clears accelerator memory or prevents observation of scheduling and memory behavior.
For sensitive workloads, prefer a dedicated GPU or node. If sharing is unavoidable, require hardware-backed partitioning with memory isolation that matches your threat model and verify what the driver and virtualization stack actually guarantee. After each job, clear model inputs, outputs, caches, temporary files, and accelerator memory where the platform supports reliable clearing. A VM boundary does not automatically solve a GPU boundary.
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Reject dangerous or wasteful inputs
- Allow only the file types and encodings your application actually parses.
- Reject malformed encodings, nested archives, unexpected compression, and oversized text, image, audio, or file payloads.
- Set independent limits for upload size, decoded size, image dimensions, audio duration, context length, and batch count.
- Scan files in a separate service before they reach the model runtime.
- Test prompt-injection behavior with attempts to reveal system instructions, exfiltrate secrets, or bypass tool policy.
Make tool use explicit
Generated text must not directly become a shell command, host path, database query, browser action, or network request. Put every tool behind an allow-listing broker that validates arguments, applies its own identity and resource limits, and returns only the minimum result. If the model does not need tools, remove the integration entirely. A sandboxed model can still cause harm through a trusted application that executes its output.
Protect secrets from prompts and logs
Do not place API keys, cookies, private documents, or host environment variables in the model prompt unless the task requires them and the sandbox can contain them. Redact secrets from request and response logs. Treat generated output as untrusted content when it is displayed in a web interface; escape it so that a model cannot inject active markup or scripts into the operator’s browser.
Use a VM or microVM for mutually untrusted workloads
A dedicated VM is the safer default when the model, loader, plugin, user, or input files may be hostile to one another. Give each trust domain its own guest, image, credentials, network policy, and storage. Keep the guest disposable: boot from a known image, attach only the verified model directory, run the job, collect approved results, and destroy or revert the guest.
A microVM follows the same trust model with a smaller guest footprint and faster lifecycle. Its value is the virtual-machine boundary, not the label. Confirm the implementation’s guest-kernel, device, filesystem, and network guarantees, and keep the host hypervisor and microVM manager patched. If a shared sandbox service states that it is intended only for workloads within one trust boundary, do not use it to combine unrelated customers or hostile code.
Observe, record, and recover
Log the evidence needed to investigate
For each job, record the model and runtime versions, image digest, model and component hashes, policy decisions, identity, resource consumption, request timing, and denied network or filesystem operations. Keep logs outside the sandbox so a compromised process cannot erase its own history, but avoid storing raw sensitive prompts unless retention is required.
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Detect abnormal behavior
Alert on unexpected child processes, attempted package installation, writes outside the temporary directory, network-denial bursts, sudden memory growth, repeated timeouts, unusual GPU use, and requests to forbidden tools. Monitoring should cover the host, guest or container, inference server, gateway, and accelerator rather than relying on application logs alone.
Destroy and recreate instead of cleaning in place
Keep an immutable image and, for VM or microVM workloads, a clean snapshot. When behavior is suspicious, stop the job, preserve the relevant logs, revoke any credentials that were present, and destroy the instance. Recreate from verified artifacts instead of trying to discover every modification made by the process. Rotate or invalidate temporary tokens after every disposable job.
Common unsafe configurations and their fixes
| Unsafe configuration | Why it fails | Safer correction |
|---|---|---|
| Container runs as root with all default capabilities. | An application flaw has a larger path to devices, kernel interfaces, and mounted files. | Use a dedicated non-root UID, drop capabilities, enable no-new-privileges, and apply seccomp or an equivalent policy. |
| Host home directory or root filesystem is mounted read-write. | The model can read secrets and alter files that were never part of the workload. | Mount only the model directory read-only and use isolated temporary storage. |
/var/run/docker.sock is mounted. |
Engine access can become control of other containers or the host. | Remove the socket; use an external, narrowly scoped control service if orchestration is required. |
| Inference server has unrestricted Internet egress. | It can exfiltrate prompts, reach internal services, or download unreviewed code. | Start with no egress and add only a proxy-enforced allow-list for a demonstrated need. |
| Model and conversion tool are downloaded and executed in one permanent environment. | A compromised artifact can persist and access production data during conversion. | Download and convert in a disposable, isolated job, verify hashes, then transfer only the approved artifact. |
| Container is treated as sufficient for hostile tenants. | Containers share a host kernel and may not provide the required cross-tenant boundary. | Use separate VMs or microVMs for mutually untrusted tenants or code. |
| GPU is shared without a memory-isolation guarantee. | Accelerator state and side channels may permit cross-tenant leakage. | Use a dedicated device or node, or hardware-backed partitioning with verified isolation. |
| No CPU, memory, disk, process, or request limits are set. | A single prompt or job can exhaust the host and deny service to other workloads. | Apply quotas, timeouts, concurrency limits, health monitoring, and automatic teardown. |
| Model output is passed directly to a shell or privileged tool. | Prompt injection can turn generated text into an unauthorized action. | Remove tools or place them behind typed, allow-listed, separately sandboxed brokers. |
| Operators rely on a clean shutdown without clearing temporary data. | Inputs, outputs, caches, logs, and accelerator memory may remain available to the next job. | Use disposable storage, clear supported caches and accelerator memory, and destroy or revert the job environment. |
A deployment checklist
- Classify the model, loader, plugins, users, inputs, tools, and data as trusted, untrusted, or mutually untrusted.
- Select a dedicated VM or microVM for mutually untrusted or high-sensitivity work; use a container only for a lower-risk, single-trust-domain workload.
- Record the exact publisher, repository, revision, license, files, and required runtime.
- Verify hashes and provenance for weights, tokenizers, adapters, conversion tools, binaries, and images.
- Build a minimal, pinned image or guest and remove compilers, shells, package managers, and debugging tools that are not required.
- Run as a non-root identity with no-new-privileges, dropped capabilities, a read-only root filesystem, and a syscall policy.
- Mount only a deliberate model directory and isolated temporary storage; never mount host roots, home directories, engine sockets, metadata endpoints, or broad devices.
- Start with no network access. If an API or download is essential, separate those phases and enforce a narrow proxy allow-list with encryption.
- Set CPU, memory, GPU, disk, process, concurrency, request-size, and timeout limits.
- Validate every input, test prompt-injection behavior, and prevent generated output from directly invoking privileged tools.
- Log versions, digests, hashes, policy decisions, resource use, and denied operations outside the sandbox.
- Keep a clean image or VM snapshot and destroy and recreate the environment after suspicious behavior.
These controls work together. A read-only container with unrestricted credentials is not safe, and a VM with a writable host share and an unbounded GPU is not a complete boundary. The practical goal is to make every required capability explicit, minimize the capabilities that remain, and make compromise disposable.
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