Building Mithridatium: Detecting Hidden Backdoors in ML Models

As pretrained AI models become more common, one growing concern is whether those models can actually be trusted. A model may appear completely normal during testing, but behave maliciously when exposed to a hidden trigger. These attacks are known as backdoor or poisoning attacks, and they represent a serious security risk for real-world AI systems. This semester, our team built Mithridatium - an open-source framework designed to help detect hidden backdoors in pretrained machine learning models.