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This paper introduces the Forced Deferral Attack (FDA), an adversarial image attack that manipulates confidence scores in multimodal LLM cascades, causing queries to be unnecessarily routed to stronger (more expensive) models, thereby shifting compute costs to the provider without degrading answer correctness.