Nardine Basta, Robin Carpentier, Benjamin Zhao, Dali Kaafar · 2026 · Lecture notes in computer science
DOI: 10.1007/978-3-032-37682-4_23 · cited by 0 works (OpenAlex) · open-access PDF
전화 사기 행위는 전 세계 개인과 금융 시스템에 상당하고 증가하는 비용을 부과하지만 기존의 대책은 범인에게 자원 비용을 부과하지 않습니다. 전략적 사기 배팅, 자동 에이전트가 사기꾼을 고용하여 운영 능력을 소모시키는 경우, 선동적 인 대안을 제공하지만 정적 시스템이 제공 할 수없는 역동적 인 전략 적응이 필요합니다. 우리는 LLM이 적절한 조정없이 컨텍스트 경험을 통해 효과적인 대책 전략을 배울 수있는 게임 이론 프레임 워크를 도입합니다.
Machine translation (Meta M2M100); the English original follows.
Phone scams impose substantial and growing costs on individuals and financial systems worldwide, yet existing countermeasures impose no resource cost on perpetrators. Strategic scam-baiting, in which automated agents engage scammers to deplete their operational capacity, offers a proactive alternative but requires dynamic strategy adaptation that static systems cannot provide. We introduce a game-theoretic framework enabling LLMs to learn effective counter-strategies through in-context experience without fine-tuning. The framework models scammer-baiter interactions as a two-player game with em
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Topics: Computer science, Nash equilibrium, Game theory, Benchmark (surveying), Phone, Computer security
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