VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models 事件

VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models arXiv:2510.17759v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored vulnerabilities. Existing multimodal red-teaming methods largely rely on brittle templates, focus on single-attack settings, and expose only a narrow subset of vulnerabilities. To address these limitations, we introd