Developing a GPT-3-Based Automated Victim for Advance Fee Fraud Disruption

Advance Fee Fraud (AFF) is amongst the most prevalent and destructive forms of cybercrime. Scammers typically commit AFF by tricking victims into making upfront payments for goods or services that are never provided. These payments are small compared to the alleged gains, and can thus be attractive for victims, particularly if they are vulnerable or in a heightened emotional state. Given that approximately three billion fraudulent emails are sent every day, the scale and impact of AFF demands innovative approaches. In this paper we document the development of an automated victim for AFF. The system leverages GPT-3, a large language model, in conjunction with deliberately engineered prompts to generate plausible responses to AFF emails, allowing fraud to be disrupted and actionable information relating to perpetrators to be obtained.