WebPII: Benchmarking Visual PII Detection for Computer-Use Agents
Computer use agents create new privacy risks: training data collected from real websites inevitably contains sensitive information, and cloud-hosted inference exposes user screenshots. Detecting personally identifiable information in web screenshots is critical for privacy-preserving deployment, but no public benchmark exists for this task. We introduce WebPII, a fine-grained synthetic benchmark of 44,865 annotated e-commerce UI images designed with three key properties: extended PII taxonomy including transaction-level identifiers that enable reidentification, anticipatory detection for partially-filled forms where users are actively entering data, and scalable generation through VLM-bas...
| Authors | Nathan Zhao |
|---|---|
| Primary category | cs.CR |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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