Is Amber Safe?
Amber — Nerq Trust Score 50.5/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Amber is a Python package with a Nerq Trust Score of 50.5/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Amber safe?
Trust Score Breakdown — Amber has a Nerq Trust Score of 50.5/100 (C-). Measured across 2 independent trust signals (as of 2026-03-20).
What is Amber's trust score?
Amber has a Nerq Trust Score of 50.5/100, earning a C- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Amber?
Amber's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Amber and who maintains it?
| Author | Svyatoslav Bulbakha |
| Category | Python Packages |
| Source | N/A |
Amber Across Platforms
Same developer/company in other registries:
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Safety Guide: Amber
What is Amber?
Amber is a Python package — A library that makes using REST API easer..
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=amber
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Amber has a Nerq Trust Score of 50/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Amber has a Trust Score of 50/100 (C-).
- 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.
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.