Is Sqlalchemy Safe?
Sqlalchemy — Nerq Trust Score 87.0/100 (A grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-18
Sqlalchemy is a Python package with a Nerq Trust Score of 87.0/100 (A), based on 3 independent data dimensions. Last analyzed: 2026-08-18 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-08-18. Machine-readable data (JSON).
Is Sqlalchemy safe?
Trust Score Breakdown — Sqlalchemy has a Nerq Trust Score of 87.0/100 (A). Measured across 2 independent trust signals (as of 2026-08-18).
What is Sqlalchemy's trust score?
Sqlalchemy has a Nerq Trust Score of 87.0/100, earning a A grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Sqlalchemy?
Sqlalchemy's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Sqlalchemy and who maintains it?
| Author | Mike Bayer |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Sqlalchemy
What is Sqlalchemy?
Sqlalchemy is a Python package — Database Abstraction Library.
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=sqlalchemy
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Sqlalchemy has a Nerq Trust Score of 79/100 (B+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Sqlalchemy has a Trust Score of 79/100 (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 | 100/100 |
| Popularity | 100/100 |
| Quality | 55/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Sqlalchemy collect?
Sqlalchemy is a Python package maintained by Mike Bayer. It receives approximately 103,243,691 weekly downloads. Licensed under MIT.
As a development package, Sqlalchemy 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: Sqlalchemy Privacy Report · Privacy review
Is Sqlalchemy secure?
Security score: 90/100. Sqlalchemy has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under MIT, 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: Sqlalchemy Security Report
How we calculated this score
Sqlalchemy's trust score of 87.0/100 (A) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (100/100), quality (55/100), 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 18, 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.