Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks

Authors: Polra Victor Falade

Published: 2023-10-09 10:31:04+00:00

Comment: 185 - 198 pages

Journal Ref: International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 Volume 9, Issue 5 September-October-2023

AI Summary

This research analyzes the multifaceted applications of generative AI models like ChatGPT, FraudGPT, and WormGPT in social engineering attacks, offering insights into the evolving threat landscape through blog mining. It highlights how these AI models amplify cybercriminal arsenals, enabling convincing phishing lures and deepfakes that exploit human cognitive biases. The paper also outlines various counter-strategies, emphasizing the importance of vigilance, awareness, and robust security measures against AI-enhanced social engineering threats.

Abstract

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using the blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.

Key findings

The study found a growing utilization of generative AI by malicious actors, enhancing the effectiveness and complexity of social engineering attacks, leading to more personalized and contextually relevant messages. This development poses significant challenges for detection, as conventional cybersecurity measures struggle to keep pace with the rapid evolution of AI models and the lack of comprehensive training data. The research underscores the urgent need for adaptive and innovative cybersecurity strategies.

Approach

The research employs a blog mining technique, systematically extracting information from publicly available blogs and online content using Google Blog Search. A initial set of 76 blogs, identified by the keyword 'the use of generative AI in social engineering attacks,' was manually scrutinized and filtered down to 39 relevant blogs for analysis. This methodology was used to understand the evolving trends, tactics, and techniques of AI-enhanced social engineering attacks.

Datasets

39 selected blogs from an initial retrieval of 76 blogs identified via Google Blog Search using the keyword "the use of generative AI in social engineering attacks."

Model(s)

UNKNOWN

Author countries

Nigeria

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