arXiv:2607. 11707v1 Announce Type: cross Abstract: Following the rapid progress of generative Artificial Intelligence, there is a growing threat posed by conversational scams.
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The paper introduces a dataset of 10,015 real scam and spam phone calls collected over 53 days using an active voice‑agent honeypot. Each call is recorded, transcribed, and automatically labeled, yielding 328,869 turn‑level transcripts and 895 hours of audio from 5,665 distinct numbers. The corpus distinguishes between predatory‑but‑legal lead generation and outright scams, with labels validated by human review and technical checks on realism.
The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.
The paper presents a cumulative turn‑based risk assessment framework for detecting financial scams targeting older adults, which aggregates conversational turns and updates risk estimates at each step. A multi‑turn dialogue dataset covering investment, charity, and tech support scams is created, with annotations for risk level, score, rationale, and safety recommendation at every cumulative stage. Four small language models (Phi‑4, LLaMA‑3.2, DeepSeek‑R1, Qwen3) are fine‑tuned; Phi‑4 and LLaMA‑3.2 outperform others in turn‑aware risk estimation, demonstrating that compact models can effectively support incremental scam detection in resource‑constrained, privacy‑aware deployments.
Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of audio and 330,956 trans...
arXiv:2406. 13049v3 Announce Type: replace-cross Abstract: Personalized phishing is difficult to defend against because messages can be tailored to a target's work, interests, and social context.
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.