针对学术界动态钓鱼攻击，构建多模态评测基准
Can MLLMs Detect Phishing? A Comprehensive Security Benchmark Suite Focusing on Dynamic Threats and Multimodal Evaluation in Academic Environments
- 提出AdapT-Bench框架，系统评估MLLM在学术场景的防御能力
- 首个聚焦学术背景信息的多模态钓鱼检测评测集
- 适合研究网络安全与多模态模型安全的学者使用
多模态大语言模型（MLLMs）的快速普及带来了前所未有的安全挑战，特别是在学术环境中的钓鱼检测。高校和研究人员是高价值目标，面临动态、多语言且依赖上下文的攻击，这些攻击利用科研背景、学术合作和个人信息，精心设计高度定制化的钓鱼手段。现有安全评测数据集大多未包含特定学术背景信息，难以捕捉学术界特有的演化攻击模式和以人为中心的脆弱性因素。为此，我们提出了AdapT-Bench，一个统一的方法论框架与评测套件，用于系统评估MLLM在学术环境中应对动态钓鱼攻击的防御能力。
原文摘要 · Abstract (English)
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.
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