Buffer Overflow in Mixture of Experts
Jamie Hayes Ilia Shumailov Itay Yona
Mixture of Experts (MoE) has become a key ingredient for scaling large
foundation models while keeping inference costs steady. We show that expert
routing strategies that have cross-batch dependencies are vulnerable to
attacks. Malicious queries can be sent to a model and can affect a model's
output on other benign queries if they are grouped in the same batch. We
demonstrate this via a proof-of-concept attack in a toy experimental setting.