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Trust and health report

auto-sklearn - trust report

Sourced, dated trust signals - maintenance label posture, repository provenance, and security scan status. Not a composite safety grade.

GraphCanon updated today · GitHub synced today

Maintenance

Recency and activity heuristics from public GitHub metadata (maintenance label, momentum); methodology: github_public_v1.

Active82% signal

last push 12d ago · last release 3y

Provenance

Repository identity and fork provenance (github_public_v1).

Security scan

Dependency advisory security scan when a lockfile is present. Not a full code audit.

Status
22 low
Last scan
today
Scanner
osv@v1

Findings

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

Method and caveats:these are sourced, dated heuristics from public GitHub data and optional dependency scans. A status like "no criticals found on 2026-07-11" is not a guarantee of safety. Read the full trust methodology · JSON report at /api/graphcanon/tools/automl-auto-sklearn/trust.

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.
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 for full scope and limits.