The increasing popularity of large language models (LLMs) such as ChatGPT has led to growing concerns about their safety, security risks, and ethical implications. This paper aims to provide an overview of the different types of security risks associated with ChatGPT, including malicious text and code generation, private data disclosure, fraudulent services, information gathering, and producing unethical content. We present an empirical study examining the effectiveness of ChatGPT's content filters and explore potential ways to bypass these safeguards, demonstrating the ethical implications and security risks that persist in LLMs even when protections are in place. Based on a qualitative analysis of the security implications, we discuss potential strategies to mitigate these risks and inform researchers, policymakers, and industry professionals about the complex security challenges posed by LLMs like ChatGPT. This study contributes to the ongoing discussion on the ethical and security implications of LLMs, underscoring the need for continued research in this area.

翻译：随着ChatGPT等大型语言模型（LLMs）日益流行，其安全性、安全风险及伦理影响引发了广泛关注。本文旨在概述ChatGPT相关的各类安全风险，包括恶意文本和代码生成、隐私数据泄露、欺诈服务、信息收集以及生产不道德内容。我们通过实证研究检验ChatGPT内容过滤器的有效性，并探索绕过这些防护机制的可能方法，揭示即便在保护措施存在的情况下，LLMs中仍存在的伦理影响与安全风险。基于对安全影响的定性分析，我们讨论了缓解这些风险的潜在策略，并向研究人员、政策制定者及行业专业人士揭示ChatGPT等LLMs带来的复杂安全挑战。本研究为LLMs伦理与安全影响的持续讨论作出贡献，强调在此领域持续研究的必要性。