Trust, Privacy, Robustness, and Adversarial Issues in Generative AI

Generative Artificial Intelligence (GenAI) represents a transformative paradigm in how machines create, reason, and interact with data. Despite its rapid evolution, GenAI introduces critical concerns related to trust, privacy, robustness, and adversarial vulnerabilities. Trust determines the reliability and interpretability of generated outputs; privacy ensures the protection of sensitive user data; robustness maintains performance under noisy or manipulated inputs; and adversarial issues involve deliberate attacks designed to deceive generative models. This paper presents a comprehensive study of these challenges, reviewing key literature and identifying existing research gaps. It further proposes an integrated reliability framework that combines explainability, differential privacy, and adversarial defense mechanisms to enhance GenAI security. Experimental results demonstrate measurable improvements in model transparency, privacy preservation, and resistance to attacks, contributing to the development of secure, ethical, and responsible GenAI systems.