Which versions of ml-shared are malicious?

The following PyPI versions of ml-shared are flagged malicious: 6.3.0, 8.8.0. Treat any of these as compromised.

How do I remediate ml-shared if I installed it?

ml-shared 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 ml-shared — how do I know if it already compromised me?

If ml-shared 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 ml-shared part of a known attack campaign?

Yes — ml-shared is associated with the 2026-07-ml-shared, IN-MAL-2026-011072, IN-MAL-2026-011089 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 ml-shared 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 ml-shared (2 malicious versions). O3 Security's supply-chain scanner checks every dependency against known-malicious package intelligence at install time and in CI, flagging ml-shared across your stack and pipelines.

Malicious package

ml-sharedPyPI

ml-shared is a confirmed malicious PyPI package (MAL-2026-11202) that steals credentials and exfiltrates sensitive data (malicious versions 6.3.0, 8.8.0). Do not install it — remove it immediately and rotate any exposed credentials.

Malicious code in ml-shared (PyPI)

MAL-2026-11202

Immediate action

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

pip uninstall ml-shared

What this malware does

setup.py defines a function named analytics() that collects the installer's hostname, username, current working directory, and the entire process environment (dict(os.environ)) and POSTs it as JSON to https://request-forwarder.a-d-a-m.workers.dev/. The routine fires both at module top-level (executed during pip's build/install of the sdist) and via a PostInstallCommand hooked into cmdclass={'install':...}, so it runs on pip install without user interaction. On CI/build agents the bulk os.environ dump routinely contains credentials such as AWS_*, GITHUB_TOKEN, NPM_TOKEN, PYPI_TOKEN, and other secret-shaped variables. The destination is an anonymous Cloudflare Workers subdomain unrelated to any declared publisher, the function is named analytics as a cover, and code lines are padded with trailing whitespace consistent with evasion of casual review.

During installation, package exfiltrates basic info and all environmental variables.

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

Campaign: 2026-07-ml-shared

Reasons (based on the campaign):

exfiltration-env-variables
The package contains code to exfiltrate basic data from the system, like IP or username. It has a limited risk.

Malicious versions

2 flagged

6.3.08.8.0

Indicators of compromise (SHA-256)

4f4ae8fee891c54953efa7c796a605e700fc827131feff0243f0b3bc8bd6a260

1ba1dc0bbc1690cd8c43d159eeb9306837b653cb92d6ed9eed9d14dd115a9990

89a726ddb6cfd4ce7da05b1b8c75e896e38789aa418c71d4db76f499b5727cb3

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 ml-shared (2 malicious versions). O3 Security's supply-chain scanner checks every dependency against known-malicious package intelligence at install time and in CI, flagging ml-shared across your stack and pipelines.
If you installed it — respond
ml-shared 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 ml-shared 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 ml-shared 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. ml-shared on PyPI has been identified as a malicious package (versions 6.3.0, 8.8.0 flagged). It should be removed immediately — do not install or keep it in your dependency tree.

Campaign

2026-07-ml-sharedIN-MAL-2026-011072IN-MAL-2026-011089

References

Credits

Amazon Inspector · finder
Kamil Mańkowski (kam193) · reporter

Detect & block this

O3 blocks ml-shared-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

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