Is Msgspec Safe?
Msgspec — Nerq Trust Score 68.2/100 (B- grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-11
Msgspec is a Python package with a Nerq Trust Score of 68.2/100 (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 Msgspec safe?
Trust Score Breakdown — Msgspec has a Nerq Trust Score of 68.2/100 (B-). Measured across 2 independent trust signals (as of 2026-08-11).
What is Msgspec's trust score?
Msgspec has a Nerq Trust Score of 68.2/100, 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 Msgspec?
Msgspec's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Msgspec and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Msgspec
What is Msgspec?
Msgspec is a Python package — A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML..
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=msgspec
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Msgspec has a Nerq Trust Score of 68/100 (B-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Msgspec has a Trust Score of 68/100 (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.