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 types, validated on 50 in-the-wild (IRL) real-world screenshots spanning diverse visual contexts. We evaluate four frontier systems (GPT-5.2, Claude~4, Gemini-3 Flash, Grok-4) with Wilson 95% conf...
| Authors | Youting Wang, Yuan Tang, Yitian Qian et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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