Large Multimodal Language Models (MLLMs) are emerging as one of the foundational tools in an expanding range of applications. Consequently, understanding training-data leakage in these systems is increasingly critical. Log-probability-based membership inference attacks (MIAs) have become a widely adopted approach for assessing data exposure in large language models (LLMs), yet their effect in MLLMs remains unclear. We present the first comprehensive evaluation of extending these text-based MIA methods to multimodal settings. Our experiments under vision-and-text (V+T) and text-only (T-only) conditions across the DeepSeek-VL and InternVL model families show that in in-distribution settings, logit-based MIAs perform comparably across configurations, with a slight V+T advantage. Conversely, in out-of-distribution settings, visual inputs act as regularizers, effectively masking membership signals.

翻译：大型多模态语言模型（MLLMs）正逐渐成为日益广泛的应用领域中的基础工具之一。因此，理解这些系统中的训练数据泄露问题变得愈发关键。基于对数概率的成员推断攻击（MIAs）已成为评估大型语言模型（LLMs）中数据暴露程度的广泛采用方法，但其在MLLMs中的效果尚不明确。我们首次全面评估了将这些基于文本的MIA方法扩展到多模态场景的效果。我们在DeepSeek-VL和InternVL模型系列上，通过视觉-文本（V+T）和纯文本（T-only）条件下的实验表明：在分布内设置中，基于逻辑值的MIAs在不同配置下表现相当，V+T条件略有优势；相反，在分布外设置中，视觉输入起到了正则化作用，有效掩盖了成员身份信号。