Is Asammdf Safe?
Asammdf — Nerq Trust Score 74.0/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Asammdf is a Python package with a Nerq Trust Score of 74.0/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 75/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Asammdf safe?
Trust Score Breakdown — Asammdf has a Nerq Trust Score of 74.0/100 (B). Measured across 2 independent trust signals (as of 2026-03-20).
What is Asammdf's trust score?
Asammdf has a Nerq Trust Score of 74.0/100, 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 Asammdf?
Asammdf's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Asammdf and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
Asammdf Across Platforms
Same developer/company in other registries:
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Safety Guide: Asammdf
What is Asammdf?
Asammdf is a Python package — ASAM MDF measurement data file parser.
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=asammdf
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Asammdf has a Nerq Trust Score of 74/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Asammdf has a Trust Score of 74/100 (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.
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.