Is Llama Index Safe?
Llama Index — Nerq Trust Score 74.2/100 (B grade). Score based on 2 independent trust signals. Last analyzed: 2026-04-12
Llama Index is a Python package with a Nerq Trust Score of 74.2/100 (B), based on 3 independent data dimensions. Last analyzed: 2026-04-12 Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-12. Machine-readable data (JSON).
Is Llama Index safe?
Trust Score Breakdown — Llama Index has a Nerq Trust Score of 74.2/100 (B). Measured across 2 independent trust signals (as of 2026-04-12).
What is Llama Index's trust score?
Llama Index has a Nerq Trust Score of 74.2/100, 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 Llama Index?
Llama Index's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Llama Index and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Llama Index
What is Llama Index?
Llama Index is a Python package — Interface between LLMs and your data.
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=llama-index
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Llama Index has a Nerq Trust Score of 74/100 (B). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Llama Index has a Trust Score of 74/100 (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 | 100/100 |
| Popularity | 90/100 |
| Quality | 40/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Llama Index collect?
Llama Index is a Python package maintained by Unknown. It receives approximately 2,527,920 weekly downloads.
As a development package, Llama Index 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: Llama Index Privacy Report · Privacy review
Is Llama Index secure?
Security score: 90/100. Llama Index 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: Llama Index Security Report
How we calculated this score
Llama Index's trust score of 74.2/100 (B) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (90/100), quality (40/100), 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 April 12, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
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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.