AI-Powered Social Engineering and Impersonation Attacks

Abstract

This chapter presents a comprehensive analysis of advanced social engineering attack vectors enhanced by Generative Artificial Intelligence (GenAI) technologies, focusing on impersonation methodologies and synthetic content generation. The research employs a systematic evaluation framework to analyze attack patterns leveraging Large Language Models (LLMs) and deepfake architectures within social engineering campaigns. Through quantitative analysis, we investigate the efficacy of GenAI-driven phishing content, examining success rates, behavioral patterns, and attack sophistication metrics across multiple enterprise environments. The study presents empirical evidence demonstrating a 45% increase in attack effectiveness when leveraging AI-driven impersonation techniques compared to traditional methods. Our primary contribution encompasses the development and validation of novel detection methodologies and defensive frameworks engineered for AI-enhanced social engineering threats.