Beyond the Safeguards: Exploring the Security Risks of ChatGPT
Erik Derner, Kristina Batistič
TL;DR
This paper analyzes security risks in ChatGPT by empirically testing the effectiveness of its content filters and exploring bypass methods. It characterizes six risk categories—information gathering, malicious text, malicious code, personal data disclosure, fraudulent services, and unethical content—and provides concrete prompt-based examples to highlight bypass possibilities. Through qualitative analysis, it discusses ethical implications, potential mitigations, and the need for ongoing collaboration among researchers, policymakers, and industry to address evolving safeguards. The findings underscore that, even with RLHF and filtering, sophisticated prompts and role-play strategies can provoke unsafe outputs, signaling persistent security challenges for LLMs in real-world use. The work informs stakeholders about practical risks and actionable avenues for strengthening defense mechanisms and governance around conversational AI systems.
Abstract
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.