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Hugging Face Token Exposure: The Supply Chain Risk

More Than Just Download Access​

In the AI community, Hugging Face tokens (hf_...) are often treated casually. Developers share them in Colab notebooks or paste them into public spaces to allow others to download gated models like Llama-3 or StarCoder.

The danger lies in the Scope of the token.

When generating a token, Hugging Face offers two options: "Read" and "Write". Many developers default to "Write" to avoid permission errors later.

The "Write" Token Danger​

If an attacker finds a leaked Write token, they don't just steal your data. They can poison your supply chain.

  1. Model Poisoning: The attacker uploads a malicious pickle file or a backdoored safetensors model to your repository.
  2. Trojan Horse: Users (or your internal systems) pull the latest version of your model.
  3. Execution: When they load the model, the attacker's code executes on their machine (see Pickle RCE).

Detecting Leaked Tokens​

Hugging Face tokens have a consistent format: hf_ followed by 34 alphanumeric characters.

While GitHub Secret Scanning catches many of these, it often misses tokens hidden in:

  • Jupyter Notebook Outputs (logs from huggingface_hub.login()).
  • Config files inside zipped archives.
  • Comments in code.

Securing Your Hub​

  1. Fine-Grained Tokens: Use the new Fine-Grained Access Tokens to restrict scope to specific repositories.
  2. Scanner Integration:

Veritensor includes specific signatures for Hugging Face tokens. It scans your entire project directory—including artifacts and logs—to ensure no active tokens are accidentally committed.

# Signature
- "regex:hf_[a-zA-Z0-9]{34}"

If you find a leaked token, revoke it immediately and audit your repositories for unauthorized commits.