Is Chaosloader Safe?
Chaosloader — Nerq Trust Score 46.8/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-21
Chaosloader is a Python package with a Nerq Trust Score of 46.8/100 (D), based on 3 independent data dimensions. Last analyzed: 2026-03-21 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-21. Machine-readable data (JSON).
Is Chaosloader safe?
Trust Score Breakdown — Chaosloader has a Nerq Trust Score of 46.8/100 (D). Measured across 2 independent trust signals (as of 2026-03-21).
What is Chaosloader's trust score?
Chaosloader has a Nerq Trust Score of 46.8/100, earning a D grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Chaosloader?
Chaosloader's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Chaosloader and who maintains it?
| Author | Alberto Iglesias Gallego |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Chaosloader
What is Chaosloader?
Chaosloader is a Python package — Chaos loader command line.
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=chaosloader
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Chaosloader has a Nerq Trust Score of 47/100 (D). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Chaosloader has a Trust Score of 47/100 (D).
- 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.