Is Toml Safe?
Toml — Nerq Trust Score 72.2/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-18
Toml is a Python package with a Nerq Trust Score of 72.2/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-08-18 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-08-18. Machine-readable data (JSON).
Is Toml safe?
Trust Score Breakdown — Toml has a Nerq Trust Score of 72.2/100 (B). Measured across 2 independent trust signals (as of 2026-08-18).
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.
What is Toml and who maintains it?
| Author | William Pearson |
| Category | Python Packages |
| 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 package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Toml has a Nerq Trust Score of 72/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Toml has a Trust Score of 72/100 (B).
- 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.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 66/100 |
| Popularity | 100/100 |
| 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 Toml collect?
Toml is a Python package maintained by William Pearson. It receives approximately 52,208,969 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), maintenance (66/100), popularity (100/100), 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.
Signals last measured on August 18, 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 measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.