A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot

arXiv:2609.29528 · cs.CR, cs.CL, cs.LG · Submitted 2026-08-25 · Read on arXiv

cs.CR, cs.CL, cs.LG

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Benchmark — 6× in this paper · explained in 2 episodes
• AUC — 5× in this paper · explained in 2 episodes
• AI Agents — 3× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• ScamAgents: How AI Agents Can Simulate Human-Level Scam Calls
• Voice-Enabled AI Agents can Perform Common Scams
• AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning
• TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection
• How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study
• Combating Phone Scams with LLM-based Detection: Where Do We Stand?
• Send to which account? Evaluation of an LLM-based Scambaiting System
• An analysis of scam baiting calls: Identifying and extracting scam stages and scripts
• GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment

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