SEAR：一个用于分析AR-LLM驱动的社交工程行为的多模态数据集
SEAR: A Multimodal Dataset for Analyzing AR-LLM-Driven Social Engineering Behaviors
AI总结：
SEAR数据集是一个多模态资源，通过60名参与者的180段对话研究AR与多模态LLM驱动的社交工程攻击，揭示了其在诱导顺从和劫持信任方面的高效性，并支持防御框架设计。
摘要：
SEAR数据集是一个新型多模态资源，旨在研究通过增强现实（AR）和多模态大语言模型（LLMs）策划的社交工程（SE）攻击这一新兴威胁。该数据集在模拟的对抗性场景（包括会议、课程和社交活动）中，捕获了60名参与者的180段标注对话。它包含同步的AR捕获视觉/音频线索（如面部表情、语音语调）、环境上下文以及精选的社交媒体档案，还有信任评级和易感性评估等主观指标。主要发现揭示了SEAR在诱导顺从方面具有惊人的有效性（例如，93.3%的钓鱼链接点击率，85%的接听率）以及劫持信任（76.7%的交互后信任激增）。该数据集支持检测AR驱动的SE攻击、设计防御框架以及理解多模态对抗性操纵的研究。包括匿名化和IRB合规在内的严格伦理保障措施确保了其负责任的使用。SEAR数据集可在https://github.com/INSLabCN/SEAR-Dataset获取。
英文摘要：
The SEAR Dataset is a novel multimodal resource designed to study the emerging threat of social engineering (SE) attacks orchestrated through augmented reality (AR) and multimodal large language models (LLMs). This dataset captures 180 annotated conversations across 60 participants in simulated adversarial scenarios, including meetings, classes and networking events. It comprises synchronized AR-captured visual/audio cues (e.g., facial expressions, vocal tones), environmental context, and curated social media profiles, alongside subjective metrics such as trust ratings and susceptibility assessments. Key findings reveal SEAR's alarming efficacy in eliciting compliance (e.g., 93.3% phishing link clicks, 85% call acceptance) and hijacking trust (76.7% post-interaction trust surge). The dataset supports research in detecting AR-driven SE attacks, designing defensive frameworks, and understanding multimodal adversarial manipulation. Rigorous ethical safeguards, including anonymization and IRB compliance, ensure responsible use. The SEAR dataset is available at https://github.com/INSLabCN/SEAR-Dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
