Survey on Adversarial Prompt Generation and Robustness Analysis in Large Language Models

Abstract

The rapid integration of Large Language Models (LLMs) into critical domains has amplified concerns about their robustness, particularly against adversarial prompts that exploit subtle vulnerabilities. These crafted inputs can cause models to generate biased, misleading, or harmful outputs, posing serious ethical, societal, and operational risks. In this survey, we provide a comprehensive analysis of adversarial prompting strategies, ranging from input manipulation techniques to semantic and structural distortions. We systematically review benchmark datasets and evaluation metrics developed to assess model resilience under adversarial conditions. Furthermore, we explore defense strategies across preprocessing, model-level, postprocessing, and hybrid strategies, highlighting recent advances and their limitations. Special attention is given to emerging challenges, such as multimodal vulnerabilities, the balance between safety and expressiveness, and ethical considerations in adversarial defense design. Finally, we outline future research directions that aim to build LLMs that are not only more robust and trustworthy but also adaptable to evolving adversarial tactics. By synthesizing the current landscape, this work serves as a foundation for advancing the secure deployment of LLMs in real-world applications.