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Choose a tool and workflow that let you verify the model’s source, inspect its files, require safetensors where available, and pin the exact repository revision you reviewed. No repository badge, signature, or clean scan makes every file safe: pickle-based checkpoints can execute code when loaded, and custom model code presents a separate risk.
What makes a model download risky?
A model repository may contain weights, configuration files, and Python code. Treat them as separate inputs rather than assuming that a safe weight format makes the entire repository safe.
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Pickle-based files can execute code when loaded
Python’s pickle format is not just passive tensor storage. Deserializing a malicious pickle file can execute code, so do not load pickle-based artifacts from an untrusted source. Hugging Face’s pickle-scanning documentation explains this risk and the limits of scanning.
Remote model code is a separate decision
Some repositories require a library to run custom Python modeling files. In Transformers, that can mean enabling trust_remote_code=True. A safetensors weight file does not make that Python code safe. Hugging Face recommends inspecting the modeling files and pinning the repository revision before trusting remote code; see its Transformers security policy.
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Compare the controls that matter
| Decision | Safer choice | What it does—and does not do |
|---|---|---|
| Weight format | Prefer safetensors when supported. |
The format is designed to avoid the arbitrary-code execution risk associated with pickle loading. It does not certify other repository files or the runtime. |
| Loader fallback | Require safetensors instead of allowing an unexamined fallback. |
Transformers documents use_safetensors to make loading fail if a safetensors file is not available. That avoids silently selecting an unsafe format; it does not assess custom code. |
| Repository version | Pin the specific revision you reviewed. | A fixed revision helps prevent later changes to weights or code from changing what you load. A moving branch can change. |
| Custom code | Use supported built-in model code where possible; inspect repository code before enabling trust_remote_code=True. |
Review and pin custom code before execution. A safe weight format cannot neutralize code the runtime is asked to run. |
| Security screening | Check the repository’s visible scan findings. | Scans are useful screening signals, not a safety certification or a guarantee that every risk was found. |
The safetensors security policy recommends using the format and pinning revisions. Transformers’ security policy documents the loader setting and guidance on remote code.
How to evaluate a model repository before downloading
- Start with the intended source. Use the repository’s official interface or a supported client. Check that the publisher and repository are the ones you intended, and read the model card and file list.
- Review provenance. Look at the publisher identity and commit history. A signed commit helps establish a file’s origin, not its safety: Hugging Face puts it plainly, “This does not guarantee that your file is safe, but it does guarantee the origin of the file.” The statement concerns signed commits; it is not a safety endorsement.
- Check the scan panel and interpret it narrowly. Hugging Face documents ClamAV and pickle-import checks, as well as Protect AI Guardian scans of public repository files. A clean result cannot prove the repository is harmless, and the pickle-scanning documentation warns that scanning is not foolproof. The Protect AI scanner page also describes threats beyond pickle, including exploitable Keras Lambda layers.
- Choose the format deliberately. Prefer a safetensors checkpoint and configure the loader to require it where supported. If the repository offers only a pickle-based checkpoint, choosing another supported checkpoint is safer. If you still need to consider it, do so only after independently reviewing the publisher and artifact, in an appropriately isolated environment; isolation reduces exposure but is not a guarantee.
- Record and load a fixed revision. Select the exact commit or revision you reviewed and pass it to the client or loader. Do not rely on a moving branch if you need the downloaded code and weights to remain the same as the reviewed version.
- Inspect code before granting execution. If loading requires
trust_remote_code=True, read the repository’s modeling files and pin the revision before enabling it. Do not treat a clean scan or safetensors weights as approval to run unreviewed Python code.
What a safer download setup looks like in Transformers
When using Transformers, set use_safetensors=True and provide the reviewed revision. In the documented behavior, requiring safetensors makes loading fail if a safetensors file is unavailable rather than silently selecting another format. Replace the example values with the intended repository and its exact reviewed revision:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"publisher/model-repository",
revision="REVIEWED_COMMIT_OR_REVISION",
use_safetensors=True,
)This is a format and version-control measure, not a blanket safety check. If the repository needs custom remote code, assess those files separately before enabling trust_remote_code=True. The Transformers security policy covers these controls.
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How to choose between browsing and download tools
Prefer the official repository interface or a supported client that makes it practical to inspect the publisher, revision, files, and scan findings before loading. A convenient browser or downloader is not safer merely because it has a familiar name or a badge. If it hides the source revision, obscures what files it retrieves, or makes the loader’s format behavior unclear, use a workflow that exposes those details instead.
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Hugging Face lists additional Hub security features, including access tokens, MFA, commit signatures, and scanning, in its security documentation. These controls can help with account and provenance checks; they do not replace inspecting the artifact and code you plan to run.
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