Malware in PyTorch Lightning: I Simulated the Same Supply Chain Attack Vector on My ML Dependencies in Production 94% of active Python ML projects on GitHub have at least one transitive dependency without a verified hash in their requirements.txt . Yeah, you read that right. I'm not talking about abandoned 2018 repos — I'm talking about repos with commits from this week. And that completely changes how you need to think about security for any stack that touches PyPI. I found out about the PyTorch Lightning incident through HN (396 points — for a supply chain topic in ML, that number makes noise). It's not the first incident in the ecosystem — there was torchtriton , noblai , packages typosquatting tensorflow with one letter off. But what shook me this time wasn't the news itself. It was realizing that I have ML dependencies touching production, and I had never audited them with the same rigor I applied to my Node dependencies. That was uncomfortable enough to make me actually do something about it.…