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Digital deception: generative artificial intelligence in social engineering and phishing
Open accessAbstract
The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.
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“Coordinated fleets of specialized agents can launch thousands of subtle, context-aware interactions that, taken together, are far more likely to sway or manipulate individuals than a single adversary could (Schmitt & Flechais, 2024;Falade, 2023).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Schmitt & Flechais, 2024
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Metadata
- Authors
- Schmitt, M., & Flechais, I.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Schmitt, Marc
- ORCID
- Schmitt, Marc
- Venue
- Artificial Intelligence Review
- Published
- 2024-10-12
- Volume
- 57
- Issue
- 12
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W4403360640
- Cited by
- 198
- References
- 50
- Citations by year
- 2026: 100 · 2025: 79 · 2024: 18
- FWCI
- 74.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Artificial Intelligence Review (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- generative AI, social engineering, phishing, AI-driven attacks, countermeasures, human-computer interaction security, deception
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