What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media

Public social media posts can reveal private information through clues in text and images. Details that seem harmless in a single post can jointly expose a user's home address, workplace location, or daily routine when combined across posts. However, current research lacks a shared benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy, hindering consistent comparisons across methods. To support cross-post evaluation, we introduce SopriBench, a synthetic benchmark that groups posts by user and provides ground-truth private attributes with post-level supporting evidence. Guided by leakage patterns identified on public social media, it contains 50 user profiles, 500 posts, and 1,569 images. The accompanying Privacy Exposure Score (PES) scores correctly inferred information by its specificity and sensitivity in the user's context. To connect clues across posts, we develop Argus, a training-free agentic framework that keeps track of possible attribute values, verifies supporting evidence, and assembles user-level privacy profiles. On SopriBench, Argus achieves a 25% relative PES gain over the strongest baseline, SingleAgent. In a study where 11 users verify inferences about themselves, it achieves 75.5% precision, compared with 51.4% for SingleAgent. An ablation shows that removing verification increases binary accuracy but lowers the average specificity score.