VisualLeakBench: Auditing the Fragility of Large Vision-Language Models against PII Leakage and Social Engineering

As Large Vision-Language Models (LVLMs) are increasingly deployed in agent-integrated workflows and other deployment-relevant settings, their robustness against semantic visual attacks remains under-evaluated -- alignment is typically tested on explicit harmful content rather than privacy-critical multimodal scenarios. We introduce VisualLeakBench, an evaluation suite to audit LVLMs against OCR Injection and Contextual PII Leakage using 1,000 synthetically generated adversarial images with 8 PII

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ethics.ai (11 March 2026), “VisualLeakBench: Auditing the Fragility of Large Vision-Language Models against PII Leakage and Social Engineering,” evidence record 7394, https://ethics.ai/record/7394 (originally published by arXiv).

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