LiteLLM 1.107.0 Incident: A Hands-On Triage Checklist for Python and Docker Deployments
On 21 October 2026, a malicious LiteLLM 1.107.0 was published to PyPI and Docker registries for a few hours. It was built from a lookalike repository with compromised developer credentials and contained credential-steali
On 21 October 2026, a malicious LiteLLM 1.107.0 was published to PyPI and Docker registries for a few hours. It was built from a lookalike repository with compromised developer credentials and contained credential-stealing code. The official GitHub source tag was clean, and LiteLLM says private enterprise registry customers were not affected.
If you run LiteLLM as a gateway in front of model providers, here is a practical way to triage.
1. Check the exposure windows (UTC, 21 October)
- PyPI
litellm==1.107.0: 11:39 to 13:23 - Docker Hub tags
v1.107.0,latest,main-latest,main-stable: 11:46 to 14:20 - GitHub Container Registry images (
litellm,litellm-database,litellm-non_root): from 11:46 until unavailable by 13:51
Any unpinned install, image pull or CI build in those windows is a candidate.
2. Inventory and look for indicators
- Run
pip show litellmon hosts and in CI runners, and record image tags and digests. - In site-packages, look for
litellm-1.107.0.dist-info,cook.pth,cook.pyandsitecustomize.py. - An empty search does not prove a host is clean.
3. Rebuild, pin, rotate
- Do not upgrade in place. Python startup can execute the malicious files again, so rebuild images and environments from scratch.
- Pin
1.106.2(or any pre-incident release) and stop shipping floating tags likelatestto production. - Rotate model provider keys, LiteLLM master and virtual keys, database passwords and cloud roles the host could reach.
- Check logs and provider billing for unusual key use since 21 October.
4. Harden the pipeline
- Pin dependencies and verify image digests.
- Restrict who can approve releases.
- Keep secrets out of logs.
More detail: full LiteLLM compromise guide with FAQs
Written by the team at Affix Center, an IT services company in Lower Parel, Mumbai.
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.