Is Zombie Imp Safe?
Zombie Imp — Nerq Trust Score 57.2/100 (C grade). Score based on 2 independent trust signals. Last analyzed: 2026-11-01
Zombie Imp is a Python package with a Nerq Trust Score of 57.2/100 (C), based on 3 independent data dimensions. Last analyzed: 2026-11-01 Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-11-01. Machine-readable data (JSON).
Is Zombie Imp safe?
Trust Score Breakdown — Zombie Imp has a Nerq Trust Score of 57.2/100 (C). Measured across 2 independent trust signals (as of 2026-11-01).
What is Zombie Imp's trust score?
Zombie Imp has a Nerq Trust Score of 57.2/100, 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 Zombie Imp?
Zombie Imp's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Zombie Imp and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Zombie Imp
What is Zombie Imp?
Zombie Imp is a Python package — A copy of the `imp` module that was removed in Python 3.12.
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=zombie-imp
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Zombie Imp has a Nerq Trust Score of 57/100 (C). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Zombie Imp has a Trust Score of 57/100 (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.