BiasJailbreak:Analyzing Ethical Biases and Jailbreak Vulnerabilities in Large Language Models

Understand

Although large language models (LLMs) demonstrate impressive proficiency in various tasks, they present potential safety risks, such as `jailbreaks', where malicious inputs can coerce LLMs into generating harmful content bypassing safety alignments.

• In this paper, we delve into the ethical biases in LLMs and examine how those biases could be exploited for jailbreaks.
• Notably, these biases result in a jailbreaking success rate in GPT-4o models that differs by 20\% between non-binary and cisgender keywords and by 16\% between white and black keywords, even when the other parts of the prompts are identical.
• We introduce the concept of BiasJailbreak, highlighting the inherent risks posed by these safety-induced biases.