Which versions of spark-ml-utilities are malicious?

The following PyPI versions of spark-ml-utilities are flagged malicious: 1.0.0, 1.0.1. Treat any of these as compromised.

How do I remediate spark-ml-utilities if I installed it?

spark-ml-utilities is built to steal secrets, so assume every credential the build or runtime could read is compromised. Remove it from your project and lockfile, then rotate ALL exposed secrets — npm/registry tokens, cloud keys, CI/CD secrets, SSH keys, and any .env values — from a known-clean machine. Audit logs for unauthorized use of those credentials.

I already installed spark-ml-utilities — how do I know if it already compromised me?

If spark-ml-utilities was ever installed, its post-install/runtime payload may have already executed. O3's L7 egress monitoring and runtime eBPF sensors detect the credential exfiltration or command-and-control callback after install and block the malicious outbound channel, so you catch and contain the actual compromise — not just the presence of the package.

Is spark-ml-utilities part of a known attack campaign?

Yes — spark-ml-utilities is associated with the 2026-02-urllib-slim campaign. Packages in the same campaign often share infrastructure and payloads, so check whether any related packages are also in your dependency tree.

How do I find spark-ml-utilities across my projects, lockfiles, and CI pipelines?

Scan your lockfiles (package-lock.json, pnpm-lock.yaml, yarn.lock, requirements.txt, poetry.lock, etc.) and build artifacts for spark-ml-utilities (2 malicious versions). O3 Security's supply-chain scanner checks every dependency against known-malicious package intelligence at install time and in CI, flagging spark-ml-utilities across your stack and pipelines.

Malicious package

spark-ml-utilitiesPyPI

Malicious code in spark-ml-utilities (PyPI) Remove it immediately and rotate any exposed credentials.

MAL-2026-1224

Immediate action

Remove the package, then rotate any secrets the build/runtime could reach.

pip uninstall spark-ml-utilities

What this malware does

During installation, the package starts obfuscated code that downloads and runs remote executables in specific environments. In some packages in the campaign, the code only attempts to exfiltrate some basic information using DNS requests and then likely cover tracks by installing a similarly named package from private repository

Related campaigns: 2026-02-spark-audit-notify, 2026-03-geekennedy

Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers.

Campaign: 2026-02-urllib-slim

Reasons (based on the campaign):

typosquatting
Downloads and executes a remote executable.
obfuscation
dependency-confusion

Malicious versions

2 flagged

1.0.01.0.1

Indicators of compromise (SHA-256)

1cc5162c67fd3da36fe459ff93841ca5e1902970e9b175cb4dc1a2c5564a51f5

11a049821a1ccb21e9167374d5c19f97ac85b0910f1a2176d48aee401aeb6a27

3c1db0bd2243007553e09eff3018d49b00dbdf3a5183d364225d32f80f7b773f

Detection & response playbook

Credential / info stealer

Find it
Scan your lockfiles (package-lock.json, pnpm-lock.yaml, yarn.lock, requirements.txt, poetry.lock, etc.) and build artifacts for spark-ml-utilities (2 malicious versions). O3 Security's supply-chain scanner checks every dependency against known-malicious package intelligence at install time and in CI, flagging spark-ml-utilities across your stack and pipelines.
If you installed it — respond
spark-ml-utilities is built to steal secrets, so assume every credential the build or runtime could read is compromised. Remove it from your project and lockfile, then rotate ALL exposed secrets — npm/registry tokens, cloud keys, CI/CD secrets, SSH keys, and any .env values — from a known-clean machine. Audit logs for unauthorized use of those credentials.
Did it already run?
If spark-ml-utilities was ever installed, its post-install/runtime payload may have already executed. O3's L7 egress monitoring and runtime eBPF sensors detect the credential exfiltration or command-and-control callback after install and block the malicious outbound channel, so you catch and contain the actual compromise — not just the presence of the package.
How O3 protects you
O3 blocks spark-ml-utilities before install through its supply-chain scanner, and if it has already run, detects and severs the exfiltration or C2 callback at runtime through L7 egress monitoring and eBPF.

Frequently asked questions

No. spark-ml-utilities on PyPI has been identified as a malicious package (versions 1.0.0, 1.0.1 flagged). It should be removed immediately — do not install or keep it in your dependency tree.

Campaign

2026-02-urllib-slim

References

Credits

Kamil Mańkowski (kam193) · analyst

Detect & block this

O3 blocks spark-ml-utilities-class packages before install and in CI — and if it already ran, its runtime egress monitoring catches the credential exfiltration and severs the channel.

Malware detection Runtime egress monitoring