Cited by 199Spam and Phishing DetectionAdversarial Robustness in Machine LearningInformation and Cyber Security

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Digital deception: generative artificial intelligence in social engineering and phishing

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Schmitt, Marc, Flechais, Ivan·2024·Artificial Intelligence Review

DOI 10.1007/s10462-024-10973-2

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Abstract

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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“The expectation that individuals can scrutinize every email they receive is impractical and counterproductive, especially in today’s fast-paced digital environment, that comes with heavy cognitive workload (Kosch et al. 2023 ).”
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EU General Data Protection Regulation: Changes and implications for personal data collecting companies
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A comprehensive survey of AI-enabled phishing attacks detection techniques
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Flexible brain–computer interfaces
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Show all 50 references

Research works printed but not linked: 1

Daniel K (2013) Thinking, fast and slow. Farrar, Straus and Giroux

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Authors: Schmitt, M., & Flechais, I.
Affiliations: University of Oxford (GB)
Corresponding author: Schmitt, Marc
ORCID: Schmitt, Marc
Venue: Artificial Intelligence Review
Publisher: Springer Science and Business Media LLC
Published: 2024-10-12
Volume: 57
Issue: 12
ISSN: 0269-2821 (ISSN-L)
1573-7462
DOI: 10.1007/s10462-024-10973-2
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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Digital deception: generative artificial intelligence in social engineering and phishing

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