AI RESEARCH
GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models
arXiv CS.CV
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ArXi:2601.03416v2 Announce Type: replace Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous work has shown that increasing inference steps can disrupt safety mechanisms and lead MLLMs to generate attacker-desired harmful content. However, most existing attacks focus on increasing the complexity of the modified visual task itself and do not explicitly leverage the model's own reasoning incentives.