I recently ran a mass audit of Hugging Face models to see how many would pass a strict "Zero Trust" security policy. I used Veritensor, a CLI tool that performs static analysis and hash verification, to scan about 2,500 repositories.
The tool flagged 86 models as "FAIL".
I dug into the logs to understand why. Here is a breakdown of the errors, so you can avoid them in your pipelines.
Error Type 1: CRITICAL: Hash mismatch
Frequency: ~18% of failures.
Log: File differs from official repo + Metadata parse error: Header too large.
What happened:
The user (or their script) uploaded a Git LFS pointer file instead of the actual binary.
When you try to load this with torch.load(), PyTorch tries to unzip a text file. It fails.
The Fix: Always verify the SHA256 of your downloaded artifacts against the upstream API before passing them to your model loader. Don't assume the download succeeded just because the file exists.
Error Type 2: UNSAFE_IMPORT (Policy Violation)
Frequency: ~60% of failures.
Log: UNSAFE_IMPORT: ultralytics.nn.modules.block.C2f or xgboost.core.Booster.
What happened:
The scanner was running in "Strict Mode" (Allowlist only). It blocked these models because they tried to import libraries outside of the standard torch/numpy set.
The Fix: If you use specialized architectures (like YOLOv8 or XGBoost), you must explicitly whitelist these libraries in your security policies. Otherwise, a strict scanner should block them to prevent supply chain attacks via malicious PyPI packages.
Error Type 3: HIGH: Restricted license detected
Frequency: ~5% of failures.
Log: Restricted license detected: 'cc-by-nc-4.0'
What happened:
The scanner parsed the metadata header inside a .safetensors file and found a Non-Commercial tag.
The Fix: Never rely on the repository README alone. Metadata inside the file is the source of truth. Automated tooling is the only way to catch this at scale.
Error Type 4: via STACK_GLOBAL (Obfuscation)
Frequency: ~12% of failures.
Log: UNSAFE_IMPORT: dtype.dtype (via STACK_GLOBAL)
What happened:
The scanner detected a Pickle opcode sequence that constructs function names dynamically on the stack. This is how malware hides.
In this dataset, it was mostly legacy numpy serialization. But in a high-security environment, you cannot take that risk.
The Fix: Re-serialize your old models into safer formats like safetensors or ONNX. Stop using Pickle for long-term storage.
Summary
Out of 2,500 models, roughly 3.5% had issues that would break a strict production pipeline or cause legal headaches.
If you want to see the raw logs of what these errors look like, I've shared the dataset below.
📂 Get the Dataset (Excel/JSON)
(Analysis performed using Veritensor)
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