HuggingFace
Hugging Face hosts 1M+ open models, datasets, and Spaces apps, and offers Inference Endpoints and a managed Hub used by ML teams and employees running community demos and APIs.
Risk score
Assessment across data handling, compliance, security and transparency.
Overview
Hugging Face is the dominant hub for open-source machine learning: public and private model repositories, datasets, and Spaces (hosted Gradio/Streamlit apps). For most non-ML employees, the day-to-day touchpoints are Spaces demos (public web apps built by strangers), the Inference API/Endpoints (hosted model calls), and dataset downloads. Enterprise Hub adds SSO, audit logs, private storage regions, and a DPA. The risk surface is shaped by what users actually do: pasting real data into a random public Space, pulling a model that includes an unverified pickle or arbitrary-code loader, or calling Inference Endpoints without rotating tokens. Hugging Face itself is SOC 2 Type II and ISO 27001 certified and offers strong enterprise controls; the community content it hosts is not vetted by the platform and is the main source of real-world incidents (malicious models, leaked tokens, typosquatted repos).
Risk factors
3- Hosts a variety of models that may process user data.
- Data may be shared with third-party services.
- Requires user authentication for accessing certain features.
Recommendations
8- Require Enterprise Hub with SSO, SCIM, and audit logs for any team-owned org and disable public repo creation by default
- Block or wrap public Spaces in DLP so employees cannot paste PII/IP into unknown community apps
- Scan downloaded models for unsafe pickle/ExecuTorch payloads; prefer safetensors-only loaders
- Enforce token scoping (read vs write, repo-specific) and rotate quarterly; scan code for HF_TOKEN leaks
- Pin model and dataset revisions by commit hash; do not use floating main refs in production
- Verify publisher namespace (meta-llama, mistralai, etc.) and avoid typosquatted mirrors
- Run Inference Endpoints in a private VPC region matching your data-residency requirements
- License-check every dataset and model before commercial use; many are non-commercial
Data handling
- Storage
- Hub repos, datasets, and Spaces hosted on AWS (US-East default). Enterprise Hub offers regional storage (EU) and private Inference Endpoints in AWS, Azure, or GCP regions of choice.
- Retention
- Repos and Spaces retained while the account is active; deletion is user-controlled. Inference Endpoint logs follow configurable retention; Enterprise supports contractual deletion SLAs.
- Training on inputs
- Hugging Face does not train foundation models on customer Hub content. Public Spaces may, however, forward inputs to third-party model APIs chosen by the Space author.
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