Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models

Understand

Recent advances in multi-modal large reasoning models (MLRMs) have shown significant ability to interpret complex visual content.

• While these models enable impressive reasoning capabilities, they also introduce novel and underexplored privacy risks.
• In this paper, we identify a novel category of privacy leakage in MLRMs: Adversaries can infer sensitive geolocation information, such as a user's home address or neighborhood, from user-generated images, including selfies captured in private settings.
• To formalize and evaluate these risks, we propose a three-level visual privacy risk framework that categorizes image content based on contextual sensitivity and potential for location inference.