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

arXiv: https://arxiv.org/abs/2609.29528 · HTML: https://mlsanity.com/paper/2609.29528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [How well are open sourced AI-generated image detection models out-of-the-box: A comprehensive benchmark study](https://mlsanity.com/paper/2602.07814/) (2026)
- [TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection](https://mlsanity.com/paper/2503.24115/) (2025)
- [Dial One for Scam: A Large-Scale Analysis of Technical Support Scams](https://mlsanity.com/paper/1607.06891/) (2016)
- [An analysis of scam baiting calls: Identifying and extracting scam stages and scripts](https://mlsanity.com/paper/2307.01965/) (2023)
- [Send to which account? Evaluation of an LLM-based Scambaiting System](https://mlsanity.com/paper/2509.08493/) (2025)
- [Under the Shadow of Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks](https://mlsanity.com/paper/1804.00451/) (2018)

## Abstract (arXiv)

Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest: passive honeypots overwhelmingly capture automated messages and hang-ups, large-scale studies characterize call metadata rather than dialogue, and manual scam-baiting does not scale. We present a dataset of real scam-call conversations collected by an active voice-agent honeypot. Dedicated numbers are seeded into the lead-generation channels fraud operations harvest; inbound callers are answered by a low-latency conversational agent that adopts a plausible target persona and sustains the interaction while every call is recorded, transcribed, and automatically labeled. Over an initial 53-day window we captured 10,015 inbound scam and spam calls (6,601 with two or more turns): roughly 895 hours of audio and 328,869 transcribed turns from 5,665 distinct originating numbers. Under a holistic classifier the substantive calls are predominantly predatory-but-legal lead generation ("spam", about three in five), while about one in seven is an outright "scam" (949 in this snapshot). Each call carries a turn-level transcript, three-channel audio, per-turn latency telemetry, and layers of automatic labels, including a holistic scam/spam/legitimate judgment corroborated by independent human review (75% agreement on the binary decision). We describe the collection system, the record structure, and technical validation of the corpus's realism and label quality, including that the agent is recognized as non-human in only about 5% of engaged calls. We also benchmark established scam-detection methods, where detectors trained on published synthetic dialogue collapse in precision on real traffic.