Is Autograd Safe?
Autograd — Nerq Trust Score 74.5/100 (B grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-05-31.
Yes, Autograd is safe to use. Autograd is a Python package with a Nerq Trust Score of 74.5/100 (B), based on 3 independent data dimensions. Recommended for production use. Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Autograd safe?
YES — Autograd has a Nerq Trust Score of 74.5/100 (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 Autograd's trust score?
Autograd has a Nerq Trust Score of 74.5/100, 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 Autograd?
Autograd's strongest signal is security at 90/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Autograd and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
Autograd Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Autograd
What is Autograd?
Autograd is a Python package — Efficiently computes derivatives of NumPy code..
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=autograd
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Autograd has a Nerq Trust Score of 74/100 (B) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Autograd has a Trust Score of 74/100 (B).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 81/100 |
| Popularity | 90/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Autograd collect?
Privacy assessment for Autograd is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Autograd secure?
Security score: 90/100. Autograd has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under The MIT License (MIT) Copyright (c) 2025 by the President and Fellows of Harvard Un, 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: Autograd Security Report
How we calculated this score
Autograd's trust score of 74.5/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (81/100), popularity (90/100), quality (65/100), community (35/100). 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 May 31, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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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.