Large Language Models (LLMs) have transformed natural language processing, demonstrating remarkable capabilities across diverse tasks. However, deploying these models in real-world applications need to ensure their trustworthiness. This paper explores three critical dimensions of trustworthiness: privacy, security, and robustness. We examine privacy concerns related to data handling and user confidentiality, highlighting the need for transparent practices and robust data protection measures. Security issues, including adversarial attacks and model vulnerabilities, are analyzed to underscore the potential risks and necessary countermeasures. Furthermore, we address robustness challenges, focusing on bias, overfitting, and the impact of noisy inputs on model performance. By systematically synthesizing existing research and strategies for improvement, this study aims to provide a comprehensive framework for enhancing the trustworthiness of LLMs, ultimately fostering their safe and effective deployment in real-world scenarios.