Exposing the Impact of GenAI for Cybercrime: An Investigation into the Dark Side

2025-05-29

AI Summary

This study investigates the causal impact of generative artificial intelligence (GenAI) on cybercrime, addressing a critical gap in empirical causal evidence within AI security research. Method: Drawing on technological amplification theory and affordance theory, we employ a quasi-experimental design with interrupted time-series analysis across two independent datasets to assess changes in general cyber malicious activity and cryptocurrency-related crime before and after GenAI’s public release. Contribution/Results: We provide the first causal evidence—grounded in psychological theory and rigorous econometric methods—that GenAI significantly increases both the frequency and timeliness of cyberattacks, moving beyond prior correlational studies. Our findings establish a robust empirical foundation for AI governance, offering actionable insights for policymakers, AI developers’ risk mitigation strategies, and public cybersecurity education. The study advances methodological rigor in AI safety research by integrating theoretical frameworks with causal inference techniques, thereby filling a key void in the literature on AI-driven cyber threats.

Technology Category

Philosophy and Ethics of AI: Safety, Robustness & TrustworthinessCognitive Modeling & Cognitive Systems: Computational CreativityMachine Learning: Causal Learning

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsEconomics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAISecurity and Privacy: Large-scale security measurements

Abstract

In recent years, the rapid advancement and democratization of generative AI models have sparked significant debate over safety, ethical risks, and dual-use concerns, particularly in the context of cybersecurity. While anecdotally known, this paper provides empirical evidence regarding generative AI's association with malicious internet-related activities and cybercrime by examining the phenomenon through psychological frameworks of technological amplification and affordance theory. Using a quasi-experimental design with interrupted time series analysis, we analyze two datasets, one general and one cryptocurrency-focused, to empirically assess generative AI's role in cybercrime. The findings contribute to ongoing discussions about AI governance by balancing control and fostering innovation, underscoring the need for strategies to guide policymakers, inform AI developers and cybersecurity professionals, and educate the public to maximize AI's benefits while mitigating its risks.

Problem

• Investigating generative AI's link to cybercrime using empirical data
• Assessing AI's role in cybercrime via psychological and technical frameworks
• Balancing AI governance between control and innovation for cybersecurity

Innovation

• Quasi-experimental design with time series analysis
• Psychological frameworks for technological amplification
• Empirical assessment using general and cryptocurrency datasets