---
title: "auto-sklearn trust report"
type: "trust-report"
slug: "automl-auto-sklearn"
canonical_url: "https://www.graphcanon.com/tools/automl-auto-sklearn/trust"
computed_at: "2026-07-11T23:33:28.902Z"
maintenance_label: "Active"
security_status: "findings"
---

# auto-sklearn trust report

_Sourced [trust signals](/glossary/trust-and-signals/trust-signal) from public GitHub metadata and optional dependency [security scans](/glossary/trust-and-signals/security-scan). Not a security guarantee._

## Maintenance

- Label: Active (82% recency signal)
- Days since last push: 12
- Last release: 2023-02-13T12:35:21Z
- Methodology: github_public_v1

## Provenance

- GitHub repo id: 38441254
- Not a fork
- Owner type: Organization
- Computed at: 2026-07-11T23:33:28.902Z
- Methodology: github_public_v1

## Security scan

- Status: Findings present (0 critical, 0 high, 0 medium, 22 low) · last scan 2026-07-11T23:33:29.386Z

### Findings

- **low**: GHSA-5545-2q6w-2gh6 (numpy@1.9.0 · requirements.txt)
- **low**: GHSA-6p56-wp2h-9hxr (numpy@1.9.0 · requirements.txt)
- **low**: GHSA-9fq2-x9r6-wfmf (numpy@1.9.0 · requirements.txt)
- **low**: GHSA-f7c7-j99h-c22f (numpy@1.9.0 · requirements.txt)
- **low**: GHSA-fpfv-jqm9-f5jm (numpy@1.9.0 · requirements.txt)
- **low**: GHSA-frgw-fgh6-9g52 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2017-1 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2019-108 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2021-854 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2021-855 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2021-856 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2021-857 (numpy@1.9.0 · requirements.txt)
- **low**: PYSEC-2023-102 (scipy@1.7.0 · requirements.txt)
- **low**: PYSEC-2023-114 (scipy@1.7.0 · requirements.txt)
- **low**: GHSA-jw8x-6495-233v (scikit-learn@0.24.0 · requirements.txt)
- **low**: GHSA-jxfp-4rvq-9h9m (scikit-learn@0.24.0 · requirements.txt)
- **low**: PYSEC-2024-110 (scikit-learn@0.24.0 · requirements.txt)
- **low**: GHSA-c336-7962-wfj2 (distributed@2012.12 · requirements.txt)
- **low**: GHSA-hwqr-f3v9-hwxr (distributed@2012.12 · requirements.txt)
- **low**: PYSEC-2021-871 (distributed@2012.12 · requirements.txt)
- **low**: PYSEC-2026-169 (distributed@2012.12 · requirements.txt)
- **low**: PYSEC-2020-73 (pandas@1.0 · requirements.txt)

## Method and caveats

These are heuristics from public GitHub data and optional dependency scans, sourced and dated. 
"No criticals found on 2026-07-11" is not a guarantee of safety.
See the full methodology: [/trust-methodology](/trust-methodology.md).

## Common questions

### Is auto-sklearn maintained?

GraphCanon rates auto-sklearn "Active" (82% maintenance signal from public GitHub metadata, computed today). Last push was 12 days ago. This is a recency heuristic, not a guarantee the project will stay maintained.

### Is auto-sklearn safe to use?

Last scanned today (deps profile). Status: 22 low - 22 low finding(s) in the latest scan. GraphCanon does not claim the project is safe or vulnerability-free; review findings on the trust report. GraphCanon does not certify auto-sklearn as safe - review maintenance, provenance, and scan findings on this page before adopting.

### Is auto-sklearn a fork?

No. auto-sklearn is not flagged as a fork in GitHub metadata at the time of the last refresh.

### Does auto-sklearn have known security vulnerabilities?

Last scanned today (deps profile). Status: 22 low - 22 low finding(s) in the latest scan. GraphCanon does not claim the project is safe or vulnerability-free; review findings on the trust report.

### How often is the auto-sklearn trust report updated?

Trust signals refresh on GitHub ingest/refresh cycles and optional dependency/MCP scans. This report was computed today (methodology github_public_v1).

### What does GraphCanon never claim about auto-sklearn?

We never publish a composite safety grade, pen-test endorsement, or "verified secure" label for auto-sklearn. Signals are sourced heuristics with explicit limits - see [trust methodology](/trust-methodology).

### How does GraphCanon assess trust for auto-sklearn?

Signals are sourced from public GitHub metadata and optional dependency/MCP manifest scans, each tagged with methodology version and computed date. GraphCanon does not publish a composite safety grade. Read [trust methodology](/trust-methodology) for full scope and limits.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/automl-auto-sklearn/trust`](/api/graphcanon/tools/automl-auto-sklearn/trust)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_