This survey explores the security and privacy challenges of Large Language Models (LLMs), highlighting their vulnerabilities and potential defense mechanisms. LLMs, which have demonstrated impressive capabilities in tasks like text generation, translation, and question-answering, are increasingly used in various domains, including transportation, education, and healthcare. However, they are susceptible to security and privacy attacks, such as jailbreaking, data poisoning, and Personally Identifiable Information (PII) leakage. The survey provides a comprehensive review of these challenges, along with existing research gaps and future research directions. LLMs are vulnerable to various types of attacks, including prompt hacking, adversarial attacks, and privacy attacks. Prompt hacking involves manipulating input prompts to influence LLM outputs, while adversarial attacks exploit the models' susceptibility to subtle input changes. Privacy attacks, such as gradient leakage and membership inference, pose significant risks by potentially revealing sensitive information. The survey discusses different categories of LLM vulnerabilities, including security and privacy attacks, and their mitigation techniques. It also highlights application-specific risks in various domains and the limitations of existing research. The paper emphasizes the importance of addressing these challenges to ensure the secure and ethical use of LLMs in real-world applications. The survey provides a detailed overview of the current state of research and outlines future directions for improving the security and privacy of LLMs.This survey explores the security and privacy challenges of Large Language Models (LLMs), highlighting their vulnerabilities and potential defense mechanisms. LLMs, which have demonstrated impressive capabilities in tasks like text generation, translation, and question-answering, are increasingly used in various domains, including transportation, education, and healthcare. However, they are susceptible to security and privacy attacks, such as jailbreaking, data poisoning, and Personally Identifiable Information (PII) leakage. The survey provides a comprehensive review of these challenges, along with existing research gaps and future research directions. LLMs are vulnerable to various types of attacks, including prompt hacking, adversarial attacks, and privacy attacks. Prompt hacking involves manipulating input prompts to influence LLM outputs, while adversarial attacks exploit the models' susceptibility to subtle input changes. Privacy attacks, such as gradient leakage and membership inference, pose significant risks by potentially revealing sensitive information. The survey discusses different categories of LLM vulnerabilities, including security and privacy attacks, and their mitigation techniques. It also highlights application-specific risks in various domains and the limitations of existing research. The paper emphasizes the importance of addressing these challenges to ensure the secure and ethical use of LLMs in real-world applications. The survey provides a detailed overview of the current state of research and outlines future directions for improving the security and privacy of LLMs.