Dask
PyPIdaskDask is a parallel computing library for Python that scales NumPy, pandas, and scikit-learn workflows across multi-core machines and clusters. It is used in data engineering pipelines that process datasets too large to fit in memory. Dask clusters can span many worker nodes with access to shared storage.
Checking Dask
dask 2024.8.0 is a clean version with no known supply chain compromise. The response returns compromised: false with an empty sources array.
curl "https://api.attestd.io/v1/check?product=dask&version=2024.8.0" \
-H "Authorization: Bearer YOUR_API_KEY"{
"product": "dask",
"version": "2024.8.0",
"supported": true,
"risk_state": "none",
"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
Distributed computing frameworks run worker processes on multiple machines with access to shared storage systems and credentials. A compromised scheduler or worker package can harvest cloud storage credentials from environment variables visible across the cluster.
Attestd monitors dask 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.