Is Aws Lambda Decorators Safe?
Aws Lambda Decorators — Nerq Trust Score 70.0/100 (B grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-05-07.
Yes, Aws Lambda Decorators is safe to use. Aws Lambda Decorators is a Python package with a Nerq Trust Score of 70.0/100 (B), based on 3 independent data dimensions. Recommended for production use. Security: 90/100. Popularity: 45/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Aws Lambda Decorators safe?
YES — Aws Lambda Decorators has a Nerq Trust Score of 70.0/100 (B). It meets Nerq's trust threshold with strong signals across security, maintenance, and community adoption. Recommended for production use — review the full report below for specific considerations.
What is Aws Lambda Decorators's trust score?
Aws Lambda Decorators has a Nerq Trust Score of 70.0/100, 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 Aws Lambda Decorators?
Aws Lambda Decorators's strongest signal is security at 90/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Aws Lambda Decorators and who maintains it?
| Author | Grid Smarter Cities |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Aws Lambda Decorators
What is Aws Lambda Decorators?
Aws Lambda Decorators is a Python package — A set of python decorators to simplify aws python lambda development.
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=aws-lambda-decorators
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Aws Lambda Decorators has a Nerq Trust Score of 70/100 (B) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Aws Lambda Decorators has a Trust Score of 70/100 (B).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 90/100 |
| Popularity | 45/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Aws Lambda Decorators collect?
Privacy assessment for Aws Lambda Decorators is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Aws Lambda Decorators secure?
Security score: 90/100. Aws Lambda Decorators has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under MIT, allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Aws Lambda Decorators Security Report
How we calculated this score
Aws Lambda Decorators's trust score of 70.0/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (90/100), popularity (45/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on May 07, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.