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[2602.05023] Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?
Summary
This article examines whether vision-language models (VLMs) respect contextual integrity when disclosing location information, highlighting privacy risks and proposing a new benchmark for evaluation.
Why It Matters
As VLMs become increasingly capable of geolocating images, understanding their implications for privacy is crucial. This research addresses the potential misuse of these technologies and advocates for design principles that prioritize user consent and contextual awareness, which is essential in today's data-driven environment.
Key Takeaways
- VLMs can geolocate images with high precision, raising privacy concerns.
- Current measures to restrict geolocation disclosures are insufficient.
- The study introduces VLM-GEOPRIVACY, a benchmark for evaluating contextual integrity.
- Findings indicate VLMs often over-disclose sensitive location information.
- New design principles are needed for multimodal systems to balance privacy and utility.
Computer Science > Cryptography and Security arXiv:2602.05023 (cs) [Submitted on 4 Feb 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Do Vision-Language Models Respect Contextual Integrity in Location Disclosure? Authors:Ruixin Yang, Ethan Mendes, Arthur Wang, James Hays, Sauvik Das, Wei Xu, Alan Ritter View a PDF of the paper titled Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?, by Ruixin Yang and 6 other authors View PDF Abstract:Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This poses a significant privacy risk, as these widely accessible models can be exploited to infer sensitive locations from casually shared photos, often at street-level precision, potentially surpassing the level of detail the sharer consented or intended to disclose. While recent work has proposed applying a blanket restriction on geolocation disclosure to combat this risk, these measures fail to distinguish valid geolocation uses from malicious behavior. Instead, VLMs should maintain contextual integrity by reasoning about elements within an image to determine the appropriate level of information disclosure, balancing privacy and utility. To evaluate how well models respect contextual integrity, we introduce VLM-GEOPRIVACY, a benchmark that challenges VLMs to interpret latent social norms and contextual cues in real-world i...
