  In this paper, we study the harmlessness alignment problem of multimodal
large language models (MLLMs). We conduct a systematic empirical analysis of
the harmlessness performance of representative MLLMs and reveal that the image
input poses the alignment vulnerability of MLLMs. Inspired by this, we propose
a novel jailbreak method named HADES, which hides and amplifies the harmfulness
of the malicious intent within the text input, using meticulously crafted
images. Experimental results show that HADES can effectively jailbreak existing
MLLMs, which achieves an average Attack Success Rate (ASR) of 90.26% for
LLaVA-1.5 and 71.60% for Gemini Pro Vision. Our code and data will be publicly
released.
