GoldenMatch

vgoldencheck-v1.3.0 scope: goldencheck Security

This release includes 1 security fix for security teams reviewing exposed deployments.

Published 1mo Data Pipelines & ETL

View tool

✓ No known CVEs patched

Read the diff → Tool health → What is this tool? →

This release patches 1 known CVE

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution

+14 more

fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Affected surfaces

auth rce_ssrf

Summary

AI summary

SSRF guard added to the serve /scan-url endpoint.

Full changelog

Minor release. New scan_dataframe export (scan an in-memory Polars DataFrame), identity-safe primary-key preflight warning, and an SSRF guard on the serve /scan-url endpoint. See packages/python/goldencheck/CHANGELOG.md (1.3.0).

Security Fixes

SSRF guard implemented on the serve /scan-url endpoint

View diff on GitHub

Weekly OSS security release digest.

The CVE patches and breaking changes that affected production tools this week. One email, every Sunday.

No spam, unsubscribe anytime.

Share this release

Share on X Share on Bluesky

Track GoldenMatch

Get notified when new releases ship.

About GoldenMatch

All releases →

Related context

Related tools

vector

Earlier breaking changes

v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.