🤖 AI Summary
Existing security benchmarks lack academic domain specificity and fail to capture the dynamic phishing threats and human cognitive vulnerabilities faced by multimodal large language models (MLLMs) in scholarly contexts. Method: We propose AdapT-Bench—the first multilingual, contextualized, multimodal phishing detection benchmark explicitly grounded in academic knowledge—supporting dynamic attack simulation, cross-lingual threat modeling, and human cognitive vulnerability assessment. Our approach integrates multimodal reasoning, context-aware analysis, and controllable data generation to enable fine-grained identification of highly customized phishing content. Contribution/Results: Experiments demonstrate that AdapT-Bench significantly improves MLLMs’ phishing detection accuracy in academic settings, empirically validating the effectiveness and necessity of academic knowledge injection and joint multimodal-contextual modeling.
📝 Abstract
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.