Social engineering scams increasingly employ personalized, multi-turn deception, exposing the limits of traditional detection methods. While Large Language Models (LLMs) show promise in identifying deception, their cognitive assistance potential remains underexplored. We propose ScriptMind, an integrated framework for LLM-based scam detection that bridges automated reasoning and human cognition. It comprises three components: the Crime Script Inference Task (CSIT) for scam reasoning, the Crime Script-Aware Inference Dataset (CSID) for fine-tuning small LLMs, and the Cognitive Simulation-based Evaluation of Social Engineering Defense (CSED) for assessing real-time cognitive impact. Using 571 Korean phone scam cases, we built 22,712 structured scammer-sequence training instances. Experimental results show that the 11B small LLM fine-tuned with ScriptMind outperformed GPT-4o by 13%, achieving superior performance over commercial models in detection accuracy, false-positive reduction, scammer utterance prediction, and rationale quality. Moreover, in phone scam simulation experiments, it significantly enhanced and sustained users' suspicion levels, improving their cognitive awareness of scams. ScriptMind represents a step toward human-centered, cognitively adaptive LLMs for scam defense.

翻译：社会工程诈骗日益采用个性化、多轮次的欺骗手段，暴露出传统检测方法的局限性。尽管大型语言模型在识别欺骗方面展现出潜力，但其认知辅助潜力仍未得到充分探索。我们提出了ScriptMind，这是一个基于LLM的诈骗检测集成框架，旨在连接自动化推理与人类认知。它包含三个组成部分：用于诈骗推理的犯罪脚本推理任务，用于微调小型LLM的犯罪脚本感知推理数据集，以及用于评估实时认知影响的社会工程防御认知模拟评估。利用571个韩国电话诈骗案例，我们构建了22,712个结构化的诈骗者序列训练实例。实验结果表明，使用ScriptMind微调的110亿参数小型LLM在检测准确率、误报减少、诈骗者话语预测和推理质量方面优于GPT-4o达13%，性能超越了商业模型。此外，在电话诈骗模拟实验中，它显著提升并维持了用户的怀疑水平，增强了他们对诈骗的认知意识。ScriptMind代表了向以人为中心、认知自适应的诈骗防御LLM迈出的一步。