Logo image
Back
The Imitation Game: Using Large Language Models to Disrupt Chinese Chat-Based Cybercrime
Journal article

The Imitation Game: Using Large Language Models to Disrupt Chinese Chat-Based Cybercrime

Yifan Yao, Baojuan Wang, Jinhao Duan, Kaidi Xu, Chuankai Guo, Zhibo Eric Sun and Yue Zhang
2026 IEEE/ACM International Symposium on Quality of Service (IWQoS)
26 Aug 2026
DOI: https://doi.org/10.1109/IWQoS70441.2026.11661137

Abstract

Chat-based cybercrime has emerged as a pervasive threat, with attackers leveraging real-time messaging platforms to conduct scams that rely on trust-building, deception, and psychological manipulation. Traditional defense mechanisms, which operate on static rules or shallow content filters, struggle to identify these conversational threats, especially when attackers use multimedia obfuscation and context-aware dialogue. In this work, we ask a provocative question inspired by the classic Imitation Game: Can machines convincingly pose as human victims to turn deception against cybercriminals? We present LURE (LLM-based User Response Engagement), the first system to deploy Large Language Models (LLMs) as active agents (not as passive classifiers) embedded within adversarial chat environments. LURE combines automated discovery, adversarial interaction, and OCR-based analysis of image-embedded payment data. Applied to the setting of Chinese illicit video chat scams on Telegram, our system engaged 53 actors across 98 groups. In over 56% of interactions, the LLM maintained multi-round conversations without being noticed as a bot, effectively “winning” the imitation game. Our findings reveal key behavioral patterns in scam operations, such as payment flows, upselling strategies, and platform migration tactics.

Metrics

1 Record Views

Details

Logo image