Is Markdown Safe?
Markdown — Nerq Trust Score 75.8/100 (B+ grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-09.
Yes, Markdown is safe to use. Markdown is a Python package with a Nerq Trust Score of 75.8/100 (B+), based on 3 independent data dimensions. Recommended for production use. Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-09. Machine-readable data (JSON).
Is Markdown safe?
YES — Markdown has a Nerq Trust Score of 75.8/100 (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 Markdown's trust score?
Markdown has a Nerq Trust Score of 75.8/100, 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 Markdown?
Markdown's strongest signal is popularity at 100/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Markdown and who maintains it?
| Author | Manfred Stienstra, Yuri Takhteyev |
| Category | Python Packages |
| Source | N/A |
Markdown Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Markdown
What is Markdown?
Markdown is a Python package — Python implementation of John Gruber's Markdown..
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=markdown
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Markdown has a Nerq Trust Score of 76/100 (B+) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Markdown has a Trust Score of 76/100 (B+).
- Recommended for use — passes trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Privacy | 80/100 |
| Reliability | 90/100 |
| Transparency | 50/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Markdown collect?
Markdown is a Python package maintained by Manfred Stienstra, Yuri Takhteyev. It receives approximately 23,557,485 weekly downloads.
As a development package, Markdown does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Markdown Privacy Report · Privacy review
Is Markdown secure?
Security score: 90/100. Markdown has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
License information not available. 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: Markdown Security Report
Markdown Across Platforms
Same developer/company in other registries:
How we calculated this score
Markdown's trust score of 75.8/100 (B+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), privacy (80/100), reliability (90/100), transparency (50/100), maintenance (60/100). 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 April 09, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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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.