  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.
