100+ backdoored AI models were found on a major public hub in 2024. Most teams had zero idea they downloaded one. Here's the part nobody talks about enough: ML model files aren't just data. Certain serialization formats can execute arbitrary code the moment you load them. One researcher found a model that opened a reverse shell on import. No click required. No warning. And that's just the front door. The deeper problem is what our research calls the "shadow AI gap." 90% of enterprise AI usage happens outside security team visibility. Developers grab unvetted models from public repos, skip review pipelines, and push straight to production. The result? Enterprises are running inference on artifacts they've never inspected, built on training data they can't verify, inside frameworks they don't monitor. Our latest whitepaper breaks down the full threat surface: → Why standard model scanners miss critical exploits (up to 96% false positive rates desensitize teams to real threats) → How poisoned training data as small as 250 documents can embed permanent backdoors in billion-parameter models → Why SafeTensors, ML Bills of Materials, and cryptographic model signing are becoming non-negotiable → Where confidential computing fits for high-stakes deployments in finance, healthcare, and defense The uncomfortable truth: most organizations treat AI artifacts like regular software dependencies. They aren't. Model weights are opaque binary structures where malicious behavior hides inside millions of parameters. Governance policies alone won't fix this. Automated verification, provenance tracking, and hardware-backed trust boundaries will. Save this as a reference next time your team evaluates a new model source 🔒 What's your org's current process when someone wants to pull a model from a public repository? Formal review, informal check, or total free-for-all? #MLSecOps #AISupplyChain #ModelSecurity #AdversarialML #DeepAISecurity