Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that appear harmless in isolation may jointly expose a user's home, workplace, or routine. However, current research lacks a unified benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy. To address these gaps, we propose SopriBench, a synthetic benchmark guided by leakage patterns abstracted from a private reference corpus of Rednote and Instagram accounts, covering 50 user profiles and 1,569 images with attributes, contextual sensitivity, granularity, leakage type, inference difficulty, and supporting evidence. We further introduce the Privacy Exposure Score (PES), which weights value granularity by contextual sensitivity. Inspired by abductive reasoning, we introduce Argus, a training-free agentic framework for cumulative leakage inference. Argus forms hypotheses from accumulated evidence, verifies supporting evidence, and aggregates cross-post cues into privacy profiles, achieving 0.55 PES, a 25% improvement over the strongest baseline, with the largest gain on cross-post leakage.

翻译：公开的社交媒体帖子可能通过散布在文本、图像或元数据中的微弱线索泄露隐私信息。此类泄露通常具有累积性和跨帖子性：单独看似无害的线索联合起来可能暴露用户的住所、工作地点或日常行程。然而，当前研究缺乏针对用户级多模态隐私泄露的统一基准，以及能超越二元准确率、量化泄露严重程度的评估指标。为填补这些空白，我们提出SopriBench——一个基于从Rednote和Instagram账户私密参考语料库中抽象出的泄露模式构建的合成基准，涵盖50个用户画像和1,569张图像，包含属性、上下文敏感性、粒度、泄露类型、推理难度及支撑证据等维度。我们进一步引入隐私暴露分数（PES），该指标通过上下文敏感性对价值粒度进行加权。受溯因推理启发，我们提出Argus——一个无需训练的累积性泄露推理智能体框架。Argus从累积证据中形成假设，验证支撑证据，并将跨帖子线索聚合为隐私画像，最终达到0.55的PES，相较于最强基线提升25%，其中跨帖子泄露的增益最为显著。