Is Torch Safe?
Torch — Nerq Trust Score 79.5/100 (B+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-11
Torch is a Python package with a Nerq Trust Score of 79.5/100 (B+), based on 3 independent data dimensions. Last analyzed: 2026-08-11 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-08-11. Machine-readable data (JSON).
Is Torch safe?
Trust Score Breakdown — Torch has a Nerq Trust Score of 79.5/100 (B+). Measured across 2 independent trust signals (as of 2026-08-11).
What is Torch's trust score?
Torch has a Nerq Trust Score of 79.5/100, 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 Torch?
Torch's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Torch and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
Torch Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Torch
What is Torch?
Torch is a Python package — Tensors and Dynamic neural networks in Python with strong GPU acceleration.
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=torch
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Torch has a Nerq Trust Score of 80/100 (B+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Torch has a Trust Score of 80/100 (B+).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 95/100 |
| Popularity | 100/100 |
| 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 Torch collect?
Torch is a Python package maintained by Unknown. It receives approximately 23,294,869 weekly downloads. Licensed under BSD-3-Clause.
As a development package, Torch 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: Torch Privacy Report · Privacy review
Is Torch secure?
Security score: 90/100. Torch has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under BSD-3-Clause, 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: Torch Security Report
How we calculated this score
Torch's trust score of 79.5/100 (B+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (95/100), popularity (100/100), 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.
Signals last measured on August 11, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.