This paper explores the security, privacy, and ethical challenges associated with Large Language Models (LLMs), emphasizing the need for responsible deployment and risk mitigation. LLMs, with their vast parameter counts and transformer-based architectures, have transformed NLP tasks, but they also introduce significant security and ethical concerns. These include information leakage, memorization of training data, and the potential for generating harmful or biased content. The paper categorizes vulnerabilities into model-based, training-time, and inference-time threats, and discusses mitigation strategies such as red teaming, model editing, and watermarking. It also highlights the risks of misuse, including bias, discrimination, and misinformation, and proposes future research directions to enhance LLM security and risk management. LLMs are trained on extensive web data, which may contain sensitive information, raising privacy concerns. Research shows that LLMs can memorize training data and leak it through responses, posing risks to personal and corporate data. Additionally, LLMs can be used to generate malicious code, such as phishing websites or ransomware, highlighting the need for secure code generation practices. The paper also discusses adversarial attacks, including model extraction, imitation, data poisoning, and backdoor attacks, which can compromise LLM integrity and security. Inference-time vulnerabilities, such as prompt injection and spoofing, further threaten LLM security by allowing adversaries to manipulate model outputs. The paper emphasizes the importance of developing robust defense mechanisms, including adversarial training, preprocessing techniques, and prompt engineering, to mitigate these risks. It also calls for ongoing research to address the evolving security challenges posed by LLMs, ensuring their safe and ethical use in various applications. Overall, the paper underscores the need for a comprehensive and interdisciplinary approach to LLM security, balancing innovation with responsibility to protect against potential threats and vulnerabilities.This paper explores the security, privacy, and ethical challenges associated with Large Language Models (LLMs), emphasizing the need for responsible deployment and risk mitigation. LLMs, with their vast parameter counts and transformer-based architectures, have transformed NLP tasks, but they also introduce significant security and ethical concerns. These include information leakage, memorization of training data, and the potential for generating harmful or biased content. The paper categorizes vulnerabilities into model-based, training-time, and inference-time threats, and discusses mitigation strategies such as red teaming, model editing, and watermarking. It also highlights the risks of misuse, including bias, discrimination, and misinformation, and proposes future research directions to enhance LLM security and risk management. LLMs are trained on extensive web data, which may contain sensitive information, raising privacy concerns. Research shows that LLMs can memorize training data and leak it through responses, posing risks to personal and corporate data. Additionally, LLMs can be used to generate malicious code, such as phishing websites or ransomware, highlighting the need for secure code generation practices. The paper also discusses adversarial attacks, including model extraction, imitation, data poisoning, and backdoor attacks, which can compromise LLM integrity and security. Inference-time vulnerabilities, such as prompt injection and spoofing, further threaten LLM security by allowing adversaries to manipulate model outputs. The paper emphasizes the importance of developing robust defense mechanisms, including adversarial training, preprocessing techniques, and prompt engineering, to mitigate these risks. It also calls for ongoing research to address the evolving security challenges posed by LLMs, ensuring their safe and ethical use in various applications. Overall, the paper underscores the need for a comprehensive and interdisciplinary approach to LLM security, balancing innovation with responsibility to protect against potential threats and vulnerabilities.