Which versions of aiogram-msgeffect are malicious?

The following PyPI versions of aiogram-msgeffect are flagged malicious: 1.1.4, 1.2.1. Treat any of these as compromised.

How do I remediate aiogram-msgeffect if I installed it?

aiogram-msgeffect 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 aiogram-msgeffect — how do I know if it already compromised me?

If aiogram-msgeffect was installed, its post-install payload may already have run. Removing the package does not undo that — check outbound connections and credential use from the install window onward.

Is aiogram-msgeffect part of a known attack campaign?

Yes — aiogram-msgeffect is associated with the 2025-11-tgeffect, RLMA-2025-06549, RLUA-2026-00040 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 aiogram-msgeffect across my projects, lockfiles, and CI pipelines?

Search your lockfiles and build artifacts for aiogram-msgeffect (2 malicious versions).

Malicious package

aiogram-msgeffectPyPI

Advisory published Nov 22, 2025Updated Mar 19, 2026

aiogram-msgeffect is a confirmed malicious PyPI package (MAL-2025-191674) that steals credentials and exfiltrates sensitive data (malicious versions 1.1.4, 1.2.1). Do not install it — remove it immediately and rotate any exposed credentials.

Malicious code in aiogram-msgeffect (PyPI)

MAL-2025-191674

Immediate action

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

pip uninstall aiogram-msgeffect

What this malware does

Importing the module starts obfuscated code which then look for data related to some Telegram clients and attempt to exfiltrate them

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

Campaign: 2025-11-tgeffect

Reasons (based on the campaign):

obfuscation
target:telegram
exfiltration-credentials

Malicious versions

2 flagged

1.1.41.2.1

Indicators of compromise (SHA-256)

41de8b9061c97a329f2c0c212b774e139752834b73fdd5683104f56385965a1a

edd5a99e6d1cebb47e713991f08b50dee4b5bf93ae487f6adc446318ccdba6e7

98351a28d67c948095d0be173cefde8d82e298a8788fb6ad61cc5ffdfc0bf904

08bd452a81a13506c01ddc1ea26c6291567708e60b1498ec105f80c442169064

bdd09ae6ee2f772195d3aec2a7d5e30b94fac0a2e360230ba9c53c78a65f8c5f

Detection & response playbook

Credential / info stealer

Find it
Search your lockfiles and build artifacts for aiogram-msgeffect (2 malicious versions).
If you installed it — respond
aiogram-msgeffect 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 aiogram-msgeffect was installed, its post-install payload may already have run. Removing the package does not undo that — check outbound connections and credential use from the install window onward.

Frequently asked questions

No. aiogram-msgeffect on PyPI has been identified as a malicious package (versions 1.1.4, 1.2.1 flagged). It should be removed immediately — do not install or keep it in your dependency tree.

Campaign

2025-11-tgeffectRLMA-2025-06549RLUA-2026-00040

References

bad-packages.kam193.eu

Credits

Kamil Mańkowski (kam193)
ReversingLabs · finder

Detect & block this

O3 blocks aiogram-msgeffect-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

Explore

All malware advisories How O3 detects malware Vulnerability database