JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks

arXiv:2404.03027v1 Announce Type: cross
Abstract: With the rapid advancements in Multimodal Large Language Models (MLLMs), securing these models against malicious inputs while aligning them with human values has emerged as a critical challenge. In this paper, we investigate an important and unexplored question of whether techniques that successfully jailbreak Large Language Models (LLMs) can be equally effective in jailbreaking MLLMs. To explore this issue, we introduce JailBreakV-28K, a pioneering benchmark designed to assess the transferability of LLM jailbreak techniques to …

abstract arxiv attacks benchmark challenge cs.ai cs.cl cs.cr human inputs jailbreak language language models large language large language models mllms multimodal paper question robustness them type values