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arXiv · 2606.07595

VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents

Youting Wang·Yuan Tang·Yitian Qian·Chen Zhao
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Abstract

Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools. We study a concrete failure mode in this setting: action-boundary propagation, where sensitive or unsafe visible text is copied from an image into downstream tool arguments. We present VisualLeakBench, a diversified 500-image benchmark spanning UI, chat, document, form, and dashboard scenes, and evaluate a stratified 100-image agent subset with four production VLM systems under two workflows: note capture and external handoff. At baseline, target strings are propagated into tool arguments in 78.8% of PII cases and 85.5% of rendered unsafe-text cases. Under a defensive system prompt, rendered unsafe-text propagation remains high at 52.6%, while PII tool propagation falls to 2.0%, largely by suppressing tool use rather than preserving utility. Rates are tool-surface dependent: search-like tools suppress PII propagation, but rendered unsafe text still crosses tool boundaries. We measure visual-to-tool propagation rather than downstream instruction execution. We additionally provide a labeled-target oracle upper-bound diagnostic that localizes most failures at the tool boundary while leaving response-side leakage as residual risk.

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Youting Wang, Yuan Tang, Yitian Qian, Chen Zhao. 2026-05-29. VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents. https://arxiv.org/abs/2606.07595

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