Is Toml Safe?
Toml — Nerq Trust Score 72.2/100 (B grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-04-01.
Yes, Toml is safe to use. Toml is a Python package with a Nerq Trust Score of 72.2/100 (B), based on 3 independent data dimensions. It is 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: 2026-04-01. Machine-readable data (JSON).
Is Toml safe?
YES — Toml has a Nerq Trust Score of 72.2/100 (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 Toml's trust score?
Toml has a Nerq Trust Score of 72.2/100, 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 Toml?
Toml's strongest signal is popularity at 100/100. No known vulnerabilities have been detected. It meets the Nerq Verified threshold of 70+.
What is Toml and who maintains it?
| Author | William Pearson |
| Category | pypi |
| Source | N/A |
Toml Across Platforms
Same developer/company in other registries:
Similar Pypi by Trust Score
Safety Guide: Toml
What is Toml?
Toml is a Python package — Python Library for Tom's Obvious, Minimal Language.
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=toml
Key Safety Concerns for Python packages
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Toml has a Nerq Trust Score of 72/100 (B) and meets Nerq trust threshold. This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Toml has a Trust Score of 72/100 (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 | 85/100 |
| Maintenance | 60/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Toml collect?
Toml is a Python package maintained by William Pearson. It receives approximately 28,972,698 weekly downloads. Licensed under MIT.
As a development package, Toml 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: Toml Privacy Report · Privacy review
Is Toml secure?
Security score: 90/100. Toml 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: Toml Security Report
Toml Across Platforms
Same developer/company in other registries:
How we calculated this score
Toml's trust score of 72.2/100 (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 (85/100), 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 01, 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.