Capacity Overflow: A Blind Spot for Backdoor Attacks in Vision MoE

2026-08-26

AI Summary

本文发现Vision MoE架构中容量依赖性行为存在安全隐患，并通过三阶段框架提出一种隐蔽的供应链后门攻击方法，利用此特性实现对模型的安全威胁。

Abstract

Mixture-of-Experts (MoE) has become a prevalent paradigm for scaling Vision Transformers efficiently. To ensure computational scalability and prevent expert overload, Vision MoE architectures employ a capacity-bounded token dispatch mechanism, where each expert's processing budget depends on the inference batch size. This work identifies this batch-dependent behavior as an overlooked attack surface, and proposes a stealthy supply-chain backdoor attack that exploits this property through a three-phase framework. First, we inject a backdoor into an early MoE layer. Second, we train a neutralizer in a deeper MoE layer that suppresses the backdoor under normal capacity. Third, we configure a batch-adaptive capacity factor that preserves high capacity for small batches while reducing it for large batches, naturally disabling the neutralizer via token overflow at deployment-scale batch sizes. The attack remains in dormant mode during small-batch security audits and enters activation mode during large-batch deployment. Experiments on V-MoE and Swin-MoE across ImageNet-100 and GTSRB demonstrate activation-mode attack success rates of 76-87% with dormant-mode ASR below 9%, while evading Neural Cleanse, STRIP, Fine-Pruning, and Activation Clustering. Our findings reveal a fundamental security risk arising from batch-dependent execution in scalable Vision MoE architectures.

Problem

• Capacity Overflow
• Backdoor Attacks
• Vision MoE
• Batch-Dependent Behavior
• Security Risk

Innovation

• Capacity Overflow
• Backdoor Attack
• Vision MoE
• Batch-Dependent Execution
• Supply-Chain Security