Is Huggingface Hub Safe?
Huggingface Hub — Nerq Trust Score 71.5/100 (B grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-03-31.
Yes, Huggingface Hub is safe to use. Huggingface Hub is a Python package with a Nerq Trust Score of 71.5/100 (B), based on 3 independent data dimensions. It is recommended for production use. Security: 80/100. Popularity: 95/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-31. Machine-readable data (JSON).
Is Huggingface Hub safe?
YES — Huggingface Hub has a Nerq Trust Score of 71.5/100 (B). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
What is Huggingface Hub's trust score?
Huggingface Hub has a Nerq Trust Score of 71.5/100, earning a B grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Huggingface Hub?
Huggingface Hub's strongest signal is popularity at 95/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Huggingface Hub and who maintains it?
| Author | Hugging Face, Inc. |
| Category | pypi |
| Source | N/A |
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Safety Guide: Huggingface Hub
What is Huggingface Hub?
Huggingface Hub is a Python package — Client library to download and publish models, datasets and other repos on the huggingface.co hub.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=huggingface-hub
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Huggingface Hub has a Nerq Trust Score of 72/100 (B) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Huggingface Hub has a Trust Score of 72/100 (B).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 80/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 85/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Huggingface Hub collect?
Huggingface Hub is a Python package maintained by Hugging Face, Inc.. It receives approximately 47,675,484 weekly downloads. Licensed under Apache-2.0.
As a development package, Huggingface Hub does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Huggingface Hub Privacy Report · Privacy review
Is Huggingface Hub secure?
Security score: 80/100. Huggingface Hub has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Apache-2.0, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Huggingface Hub Security Report
How we calculated this score
Huggingface Hub's trust score of 71.5/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (80/100), privacy (80/100), reliability (90/100), transparency (85/100), maintenance (60/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on March 31, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.