Vision Language Models (VLMs) are increasingly integrated into privacy-critical domains, yet existing evaluations of personally identifiable information (PII) leakage largely treat privacy as a static extraction task and ignore how a subject’s online presence—the volume of their data available online—influences privacy alignment. We introduce **PII-VisBench**, a novel benchmark containing 4,000 unique probes designed to evaluate VLM safety through the *continuum of online presence*. The benchmark stratifies 200 subjects into four visibility categories: *high, medium, low,* and *zero*—based on the extent and nature of their information available online. We evaluate 18 open-source VLMs (0.3B–32B) based on two key metrics: percentage of PII probing queries refused (*Refusal Rate*) and the fraction of non-refusal responses flagged for containing PII (*Conditional PII Disclosure Rate*). Across models, we observe a consistent pattern: refusals increase and PII disclosures decrease (9.10% high → 5.34% low) as subject visibility drops. We identify that models are more likely to disclose PII for high-visibility subjects, alongside substantial model-family heterogeneity and PII-type disparities. Finally, paraphrasing and jailbreak-style prompts expose attack- and model-dependent failures, motivating visibility-aware safety evaluation and training interventions.