Edge-cloud split inference runs the early layers of a multimodal large language model (MLLM) on a device and sends intermediate features to a server to complete the task. While keeping raw images local is often perceived as a privacy benefit, an honest-but-curious server can still infer sensitive attributes, such as age, gender, and race, from the received features. Existing protections intervene at the input, transmitted representation, or model's responses, but their native evaluations rely on final-response refusals or fixed-classifier failures, which underestimate an adaptive server's capabilities. To address this, we propose a label-budgeted attribute-inference framework that measures representation-level privacy through a recovery curve: the share of the unprotected leakage the server recovers as a function of its labeled-sample budget, from zero (a classifier trained on unprotected features) to a full labeled set. Our evaluation across two popular MLLMs reveals two critical findings. First, in unprotected pipelines, intermediate features leak all three attributes regardless of the split depth. Second, even when protection mechanisms are deployed across different stages, sensitive information remains recoverable under limited label budgets. For example, Safe-LLaVA removes biometric content from the model's responses, but from the features it transmits in split inference, 20 labeled samples per class recover 75-85% of the unprotected leakage. This work provides a label-budgeted measure of feature-level privacy for split MLLM inference and motivates defenses that act on the transmitted representation.

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