Title: Backdoor Unlearning By Linear Task Decomposition
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Backdoors, Poisoning, Task Arithmetic, Weight Disentanglement, Multimodal Models, Vision-Language Models, Unlearning

Abstract: Foundation models have revolutionized computer vision by enabling broad generalization across diverse tasks. Yet, they remain highly susceptible to adversarial perturbations and targeted backdoor attacks. Mitigating such vulnerabilities remains an open challenge, especially given that the large scale of the models prohibits retraining to ensure safety. Existing backdoor removal approaches rely on costly fine-tuning to override the harmful behavior, and can often degrade performance on other unrelated tasks. This raises the question of whether backdoors can be unlearned without compromising the general capabilities of the models. In this work, we study how backdoors are encoded in the model weight space and find that they are *disentangled* from other benign tasks. Specifically, this separation enables the isolation and erasure of the backdoor's influence on the model's weights with minimal impact on clean performance. Building on this insight, we introduce a simple unlearning method that leverages such disentanglement. Through extensive experiments with CLIP-based models and common adversarial triggers, we show that, given the knowledge of the attack, our method achieves approximately perfect unlearning, while retaining on average 96\% of clean accuracy. Additionally, we demonstrate that even when the type of attack is unknown, our method successfully unlearns backdoors by proper estimation using reverse-engineered triggers. Overall, our method consistently yields better unlearning and clean accuracy tradeoffs when compared to present state-of-the-art defenses.