Choose a tool and workflow that let you verify what you are downloading and how it will be loaded—not one that simply labels a model “safe.” Prefer an established repository’s official interface or supported client, require safetensors where available, pin the revision you reviewed, check publisher provenance and scan findings, and inspect any repository code the runtime may execute. Each step addresses a different risk; none guarantees that a model or its surrounding software is safe.
What makes a model-download workflow safer?
A model repository is more than a weight file. It may also contain configuration, custom Python modeling code, and other files that a loader or runtime handles. Evaluate the repository, the serialization format, the loading behavior, and any code execution separately.
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- Use an established source. Start from the repository’s official page or a client supported by that service. Confirm the publisher and repository are the intended ones.
- Prefer
safetensors. It is designed to avoid the arbitrary-code-execution risk associated with loading pickle-based artifacts. This protects against a specific format risk; it does not certify the repository or runtime. - Make the loader require that format. If the loader supports it, configure it to fail when a safetensors file is unavailable instead of silently selecting another format.
- Review and pin a specific revision. A fixed commit or revision keeps a later change to the repository from silently changing the artifact or code you reviewed.
- Inspect scan findings and code. Scanners can help flag problems, but they are screening tools, not safety certifications. Review custom code before allowing it to run.
Choose the file format and fallback behavior
| Choice | What it means | Practical implication |
|---|---|---|
safetensors |
The format is designed to prevent arbitrary code execution during loading, unlike pickle-based serialization. The safetensors security policy recommends uploading and downloading models in this format. | Prefer it when the model supports it, while still reviewing repository code and provenance. |
| Pickle-based checkpoint | Deserializing a malicious pickle can execute code. Hugging Face explains this risk in its pickle scanning documentation. | Do not load it from an untrusted source. If no supported safetensors checkpoint exists, consider another model; any decision to proceed warrants independent review and an appropriately isolated environment, which is not a guarantee of safety. |
| Require safetensors | Transformers documents the use_safetensors parameter as a way to make loading fail if a safetensors file is unavailable. |
Use this where supported to avoid an automatic fallback to another format. See the Transformers security policy. |
| Allow fallback | The loader may select a different available format if the preferred one is missing. | Do not assume the chosen format from the model page alone; check the files and configure the loader explicitly. |
For example, Transformers documents a format requirement through use_safetensors. Confirm the parameter is supported by the loader and model path you use, then set it explicitly in the loading call. The expected protective behavior is failure when no safetensors file is available—not a silent switch to pickle.
Pin the revision you reviewed
A repository’s default branch can change after you inspect it. Record a specific commit or revision and use that same revision when downloading or loading the model. The safetensors project and Transformers both recommend revision pinning as part of reducing risk from later changes to weights or code (safetensors security policy; Transformers security policy).
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Pinning does not make a bad revision safe. It makes the reviewed version identifiable and prevents an unnoticed change from being treated as the same artifact. Recheck the repository if you choose a newer revision.
Verify publisher identity and commit provenance
Check that the account or organization publishing the model is the source you intended, and inspect the repository’s commit history and available signatures. A signed commit can establish where a commit came from; it does not establish that its contents are benign. Hugging Face states of signed commits: “This does not guarantee that your file is safe, but it does guarantee the origin of the file.” Read that assurance narrowly: origin, not safety (Hugging Face pickle scanning documentation).
For account and repository security features such as access tokens, multifactor authentication, commit signatures, and scanning, see Hugging Face Security. These controls inform how a service protects accounts and content; they do not remove the need to evaluate a particular model repository.
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Read repository scans as screening, not a clean bill of health
Hugging Face documents ClamAV and pickle-import checks, as well as Protect AI Guardian scans of public repository files. A scan result is useful evidence to consider, but an absence of findings is not proof that every file is safe. The pickle scanning documentation explicitly cautions that scanning is not foolproof (pickle scanning; Protect AI scanner).
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Look at the repository’s security or scan information alongside its file list and publisher. The Protect AI scanner documentation also notes risks beyond pickle, including exploitable Keras Lambda layers. A scan focused on one file type or threat cannot establish safety for all code, configurations, dependencies, or runtime behavior.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Handle trust_remote_code as a separate decision
Some models require a library to load custom repository code, commonly exposed in Transformers through trust_remote_code=True. That setting is about executing Python code, not just reading weights. A safetensors file does not make that code safe.
- Check whether the model can use built-in library code instead of custom repository code.
- If custom code is required, inspect the modeling files and understand what they do before enabling remote-code execution.
- Pin the reviewed repository revision in the loading workflow, so the code being executed matches the version you inspected.
Transformers’ security policy advises reviewing custom code and pinning a revision when using remote code. If you cannot review or trust the code, do not enable it casually.
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- Open the intended repository. Use the official service interface or its supported client, and verify the publisher identity.
- Review the model card and file list. Identify the available weight formats and whether custom modeling code or other executable content is present.
- Check scan findings. Treat flagged files as a reason to stop and investigate; treat no findings as limited screening, not proof of safety.
- Select and record a revision. Use the same specific commit or revision for review and loading.
- Require safetensors where supported. Configure the loader to fail if that format is missing rather than accepting an unexamined fallback.
- Review any remote code. If loading requires
trust_remote_code=True, inspect the code and pin the revision before enabling it. - Choose another checkpoint when risk remains unresolved. If only a pickle-based artifact is available and its publisher or contents cannot be independently reviewed, do not treat a clean scan as sufficient reassurance.
These controls are complementary: format choice addresses deserialization risk; revision pinning addresses later repository changes; provenance helps identify origin; scans provide limited screening; and code review addresses executable repository files.
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