Over 100 weaponized AI models were sitting on Hugging Face, waiting to be downloaded. That's not a hypothetical. JFrog researchers found them in early 2024. Some contained hidden backdoors that activated the moment a developer loaded the file. Here's what made it so dangerous. These weren't broken models. They worked perfectly on every benchmark. Passed every test. But buried inside the weights was code that opened a direct connection to an attacker's server. The scariest part? The standard security tool everyone relied on to catch this had its own vulnerabilities. Attackers could slip right past it. Our team dug deep into what this means for any organization building with AI. The pattern we keep seeing is the same → teams grab models from public repositories because waiting on approvals feels slow. Nobody scans them. Nobody tracks where they came from. And suddenly you've got unvetted code running in production. The research shows that the vast majority of enterprises have zero automated controls to catch this. No model registry. No provenance checks. No way to even know which AI tools employees are using. We wrote a full analysis breaking down the attack chain, the governance gaps, and what a secure ML lifecycle actually looks like in practice. Honest question for anyone building with AI right now → does your team have a formal process for vetting models before they hit production, or is it still mostly trust-based? #AIecurity #MLOps #SupplyChainSecurity