Unintentional Consequences: Generative AI Use for Cybercrime
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912744962260992 |
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| author | Luu, Truong Jack Samuel, Binny M. |
| author_facet | Luu, Truong Jack Samuel, Binny M. |
| contents | The democratization of generative AI introduces new forms of human-AI interaction and raises urgent safety, ethical, and cybersecurity concerns. We develop a socio-technical explanation for how generative AI enables and scales cybercrime. Drawing on affordance theory and technological amplification, we argue that generative AI systems create new action possibilities for cybercriminals and magnify pre-existing malicious intent by lowering expertise barriers and increasing attack efficiency. To illustrate this framework, we conduct interrupted time series analyses of two large datasets: (1) 464,190,074 malicious IP address reports from AbuseIPDB, and (2) 281,115 cryptocurrency scam reports from Chainabuse. Using November 30, 2022, as a high-salience public-access shock, we estimate the counterfactual trajectory of reported cyber abuse absent the release, providing an early-warning impact assessment of a general-purpose AI technology. Across both datasets, we observe statistically significant post-intervention increases in reported malicious activity, including an immediate increase of over 1.12 million weekly malicious IP reports and about 722 weekly cryptocurrency scam reports, with sustained growth in the latter. We discuss implications for AI governance, platform-level regulation, and cyber resilience, emphasizing the need for multi-layer socio-technical strategies that help key stakeholders maximize AI's benefits while mitigating its growing cybercrime risks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_23733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unintentional Consequences: Generative AI Use for Cybercrime Luu, Truong Jack Samuel, Binny M. Computers and Society Artificial Intelligence Human-Computer Interaction The democratization of generative AI introduces new forms of human-AI interaction and raises urgent safety, ethical, and cybersecurity concerns. We develop a socio-technical explanation for how generative AI enables and scales cybercrime. Drawing on affordance theory and technological amplification, we argue that generative AI systems create new action possibilities for cybercriminals and magnify pre-existing malicious intent by lowering expertise barriers and increasing attack efficiency. To illustrate this framework, we conduct interrupted time series analyses of two large datasets: (1) 464,190,074 malicious IP address reports from AbuseIPDB, and (2) 281,115 cryptocurrency scam reports from Chainabuse. Using November 30, 2022, as a high-salience public-access shock, we estimate the counterfactual trajectory of reported cyber abuse absent the release, providing an early-warning impact assessment of a general-purpose AI technology. Across both datasets, we observe statistically significant post-intervention increases in reported malicious activity, including an immediate increase of over 1.12 million weekly malicious IP reports and about 722 weekly cryptocurrency scam reports, with sustained growth in the latter. We discuss implications for AI governance, platform-level regulation, and cyber resilience, emphasizing the need for multi-layer socio-technical strategies that help key stakeholders maximize AI's benefits while mitigating its growing cybercrime risks. |
| title | Unintentional Consequences: Generative AI Use for Cybercrime |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.23733 |