The rapid evolution of online scams, driven by transnational networks and mass produced social engineering scenarios, has exposed the speed limitations of conventional detection, necessitating tighter interagency coordination. While LLMs show promise in scam identification, their role in accelerating integrated response frameworks remains underexplored. We propose Counter Scam, a unified LLM based multiagent framework that orchestrates end to end response from initial detection to crime investigation. The framework first proposes safe data guidelines, emphasizing nonpublic scam data and secure dataset construction via scam specific NER. Developed with insights from 37 stakeholders to reduce delays and improve analytical efficiency, the system integrates CSRA for multiagent mitigation, CSRT comprising nine role aligned NLP tasks, and CSRD, a corpus of 185,300 scam cases and 38,587 knowledge entries. Experiments show that fine tuned sLLMs surpass commercial models by more than 10% across all CSRT tasks and achieve a 0.24 F1 improvement in scam specific NER. These results demonstrate the framework's capability to enable rapid and collaborative mitigation of online scams.

翻译：在线诈骗因跨国网络和大规模生产的社交工程场景而快速演变，暴露出传统检测方法的速度限制，亟需更紧密的跨机构协作。尽管大语言模型在诈骗识别方面展现出潜力，但其在加速整合响应框架中的应用仍待深入探索。我们提出Counter Scam——一个基于LLM的统一多智能体框架，可协调从初始检测到犯罪调查的端到端响应。该框架首先提出安全数据指南，强调非公开诈骗数据及通过诈骗特定命名实体识别构建安全数据集。基于37位利益相关者的洞察以缩短延误并提升分析效率，系统整合了用于多智能体缓解的CSRA模块、包含九项角色对齐NLP任务的CSRT模块，以及包含185,300个诈骗案例和38,587条知识条目的CSRD语料库。实验表明，经微调的小型语言模型在所有CSRT任务上超越商业模型超过10%，并在诈骗特定NER中实现0.24的F1改进。这些结果验证了该框架实现快速协同缓解在线诈骗的能力。