Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Hongbin Liu Michael K. Reiter Neil Zhenqiang Gong
Foundation model has become the backbone of the AI ecosystem. In particular,
a foundation model can be used as a general-purpose feature extractor to build
various downstream classifiers. However, foundation models are vulnerable to
backdoor attacks and a backdoored foundation model is a single-point-of-failure
of the AI ecosystem, e.g., multiple downstream classifiers inherit the backdoor
vulnerabilities simultaneously. In this work, we propose Mudjacking, the first
method to patch foundation models to remove backdoors. Specifically, given a
misclassified trigger-embedded input detected after a backdoored foundation
model is deployed, Mudjacking adjusts the parameters of the foundation model to
remove the backdoor. We formulate patching a foundation model as an
optimization problem and propose a gradient descent based method to solve it.
We evaluate Mudjacking on both vision and language foundation models, eleven
benchmark datasets, five existing backdoor attacks, and thirteen adaptive
backdoor attacks. Our results show that Mudjacking can remove backdoor from a
foundation model while maintaining its utility.