Great Expectations
PyPIgreat-expectationsGreat Expectations is a Python data quality library that validates datasets by defining and running expectation suites against DataFrames, SQL queries, and files. It integrates with dbt, Airflow, and Spark. Production deployments connect it to data warehouses to validate tables as part of pipeline quality gates.
Checking Great Expectations
great-expectations 0.18.0 is a clean, monitored version with no known supply chain compromise. The example response returns supply_chain_monitored: true and compromised: false with an empty sources array.
curl "https://api.attestd.io/v1/check?product=great-expectations&version=0.18.0" \
-H "Authorization: Bearer YOUR_API_KEY"{
"product": "great-expectations",
"version": "0.18.0",
"supported": true,
"risk_state": "none",
"risk_factors": [],
"actively_exploited": false,
"remote_exploitable": false,
"authentication_required": false,
"patch_available": false,
"fixed_version": null,
"confidence": 0.9,
"cve_ids": [],
"cves": null,
"max_epss": null,
"typosquat": null,
"supply_chain_monitored": true,
"supply_chain": {
"compromised": false,
"sources": [],
"malware_type": null,
"description": null,
"advisory_url": null,
"compromised_at": null,
"removed_at": null
},
"last_updated": "2026-05-01T00:00:00Z"
}Why this package is monitored
Great Expectations samples production tables and DataFrames to build expectation statistics in CI and Airflow gates. Attackers who control the package can exfiltrate those samples while still returning passing validation results.
Attestd monitors great-expectations using the following detection sources:
registryManually curated advisories in the Attestd registry, verified by a human analyst. Confidence 1.0.
osvOSV.dev malicious-package advisories with IDs prefixed MAL-. Confidence 0.95.
pypi_yankVersions yanked on PyPI with a security-related yanked_reason annotation. Confidence 0.80.