ScamPilot: Simulating Conversations with LLMs to Protect Against Online Scams
Owen Hoffman, Kangze Peng, Sajid Kamal, Zehua You, and Sukrit Venkatagiri

TL;DR
ScamPilot is a conversational tool using simulated scam interactions with LLMs to educate users on recognizing and defending against online scams, improving scam detection and user confidence.
Contribution
This paper introduces ScamPilot, a novel interactive system that uses LLM-powered agents to simulate scams and train users through real-time feedback and dynamic interaction.
Findings
8% increase in scam recognition
19% increase in self-efficacy
Users provided more action-oriented advice
Abstract
Fraud continues to proliferate online, from phishing and ransomware to impersonation scams. Yet automated prevention approaches adapt slowly and may not reliably protect users from falling prey to new scams. To better combat online scams, we developed ScamPilot, a conversational interface that inoculates users against scams through simulation, dynamic interaction, and real-time feedback. ScamPilot simulates scams with two large language model-powered agents: a scammer and a target. Users must help the target defend against the scammer by providing real-time advice. Through a between-subjects study (N=150) with one control and three experimental conditions, we find that blending advice-giving with multiple choice questions significantly increased scam recognition (+8%) without decreasing wariness towards legitimate conversations. Users' response efficacy and change in self-efficacy was…
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Taxonomy
TopicsSpam and Phishing Detection · Advanced Malware Detection Techniques · Cybercrime and Law Enforcement Studies
