  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.
