The integration of large language models (LLMs) into critical sectors such as health care, education, finance, and cybersecurity has led to substantial improvements in efficiency and decision-making. However, these advances are accompanied by significant risks, particularly from adversarial attacks. These attacks compromise both the security and quality of outputs across multiple domains, resulting in misinformation, biased decision-making, data breaches, and an erosion of trust in AI systems. This review article synthesizes findings from existing studies on the cross-domain impacts of adversarial attacks on LLMs, with a focus on quality degradation and its implications for high-stakes sectors. We propose a comprehensive review of the current state of adversarial vulnerabilities, outline key areas where LLMs are most susceptible to attacks, and discuss proposed strategies to enhance the robustness and security of these models in critical sectors.