Jailbreaking Generative AI: Multivector Phishing Threats and Transformer based Defenses

The rise of Generative AI (GenAI) has reshaped the cybersecurity landscape by enabling new attack vectors and lowering the barrier for executing advanced social engineering campaigns. This study conducts an empirical analysis of jailbreaking vulnerabilities in ChatGPT-4o-Mini, showing that novices can bypass safeguards to generate complete multivector phishing attacks across email, web, SMS, and voice channels. Controlled experiments reveal that role-based jailbreaks produce fully operational attack paths capable of credential harvesting. User studies further demonstrate the disruptive potential of GenAI: novice participants exhibited a 240% increase in perceived phishing competence, a 400% improvement in task completion rates, and a 57% reduction in implementation time when assisted by GenAI compared to traditional internet resources. To address these risks, a transformer-based detection framework was developed, achieving an F1-score of 0.9864 (XLNET) for identifying malicious prompts. The work underscores the urgency of strengthening LLM guardrails and provides an annotated dataset to support future defenses.