GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents

Images shared on social media often expose geographic cues.While early geolocation methods required expert effort and lacked gen-eralization, the rise of Large Vision Language Models (LVLMs) now en-ables accurate geolocation even for ordinary users. However, existingapproaches are not optimized for this task. To explore the full potentialand associated privacy risks, we present GEO-Detective, an agent thatmimics human reasoning and tool use for image geolocation inference.It follows a procedure with four steps that adaptively selects strategiesbased on image difficulty and is equipped with specialized tools suchas visual reverse search, which emulates how humans gather externalgeographic clues. Experimental results show that GEO-Detective out-performs baseline large vision language models (LVLMs) overall, par-ticularly on images lacking visible geographic features. In country levelgeolocation tasks, it achieves an improvement of over 11.1% compared tobaseline LLMs, and even at finer grained levels, it still provides around a5.2% performance gain. Meanwhile, when equipped with external clues,GEO-Detective becomes more likely to produce accurate predictions, re-ducing the “unknown” prediction rate by more than 50.6%. We furtherexplore multiple defense strategies and find that GEO-Detective exhibitsstronger robustness, highlighting the need for more effective privacy safe-guards.