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Hauptverfasser: Cai, Wei, Zhao, Jian, Jiang, Yuchu, Zhang, Tianle, Li, Xuelong
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2508.08926
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author Cai, Wei
Zhao, Jian
Jiang, Yuchu
Zhang, Tianle
Li, Xuelong
author_facet Cai, Wei
Zhao, Jian
Jiang, Yuchu
Zhang, Tianle
Li, Xuelong
contents Large Vision-Language Models face growing safety challenges with multimodal inputs. This paper introduces the concept of Implicit Reasoning Safety, a vulnerability in LVLMs. Benign combined inputs trigger unsafe LVLM outputs due to flawed or hidden reasoning. To showcase this, we developed Safe Semantics, Unsafe Interpretations, the first dataset for this critical issue. Our demonstrations show that even simple In-Context Learning with SSUI significantly mitigates these implicit multimodal threats, underscoring the urgent need to improve cross-modal implicit reasoning.
format Preprint
id arxiv_https___arxiv_org_abs_2508_08926
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Safe Semantics, Unsafe Interpretations: Tackling Implicit Reasoning Safety in Large Vision-Language Models
Cai, Wei
Zhao, Jian
Jiang, Yuchu
Zhang, Tianle
Li, Xuelong
Artificial Intelligence
Large Vision-Language Models face growing safety challenges with multimodal inputs. This paper introduces the concept of Implicit Reasoning Safety, a vulnerability in LVLMs. Benign combined inputs trigger unsafe LVLM outputs due to flawed or hidden reasoning. To showcase this, we developed Safe Semantics, Unsafe Interpretations, the first dataset for this critical issue. Our demonstrations show that even simple In-Context Learning with SSUI significantly mitigates these implicit multimodal threats, underscoring the urgent need to improve cross-modal implicit reasoning.
title Safe Semantics, Unsafe Interpretations: Tackling Implicit Reasoning Safety in Large Vision-Language Models
topic Artificial Intelligence
url https://arxiv.org/abs/2508.08926