The rapid proliferation of Multimodal Large Language Models (MLLMs) has introduced unprecedented security challenges, particularly in phishing detection within academic environments. Academic institutions and researchers are high-value targets, facing dynamic, multilingual, and context-dependent threats that leverage research backgrounds, academic collaborations, and personal information to craft highly tailored attacks. Existing security benchmarks largely rely on datasets that do not incorporate specific academic background information, making them inadequate for capturing the evolving attack patterns and human-centric vulnerability factors specific to academia. To address this gap, we present AdapT-Bench, a unified methodological framework and benchmark suite for systematically evaluating MLLM defense capabilities against dynamic phishing attacks in academic settings.

翻译：多模态大语言模型（MLLMs）的快速扩散引发了前所未有的安全挑战，尤其是在学术环境中的钓鱼检测领域。学术机构与研究人员作为高价值目标，面临着动态、多语言且依赖上下文的威胁——攻击者利用研究背景、学术合作与个人信息构建高度定制化的攻击。现有安全基准主要依赖未纳入特定学术背景信息的数据集，导致其难以捕捉学术界特有的演化攻击模式与以人为中心的脆弱性因素。为填补这一空白，我们提出AdapT-Bench：一个用于系统评估MLLMs在学术环境中抵御动态钓鱼攻击防御能力的统一方法论框架与基准套件。