Automatic Scam-Baiting Using ChatGPT

TL;DR

This study evaluates ChatGPT-based automatic scam-baiters over a month, showing they significantly increase scammer engagement and conversation length compared to traditional methods, indicating improved effectiveness in online fraud countermeasures.

Contribution

The paper introduces and empirically tests ChatGPT-based scam-baiters, demonstrating their superior engagement with fraudsters over a month-long real-world experiment.

Findings

ChatGPT-based baiters increase scammer response rates.

They sustain longer conversations with fraudsters.

Outperform previous scam-baiting approaches.

Abstract

Automatic scam-baiting is an online fraud countermeasure that involves automated systems responding to online fraudsters in order to waste their time and deplete their resources, diverting attackers away from real potential victims. Previous work has demonstrated that text generation systems are capable of engaging with attackers as automatic scam-baiters, but the fluency and coherence of generated text may be a limit to the effectiveness of such systems. In this paper, we report on the results of a month-long experiment comparing the effectiveness of two ChatGPT-based automatic scam-baiters to a control measure. Within our results, with engagement from over 250 real email fraudsters, we find that ChatGPT-based scam-baiters show a marked increase in scammer response rate and conversation length relative to the control measure, outperforming previous approaches. We discuss the implications of these results and practical considerations for wider deployment of automatic scam-baiting.