MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse
  Worlds
Xiaolong Jin Zhuo Zhang Xiangyu Zhang
Large Language Model (LLM) alignment aims to ensure that LLM outputs match
with human values. Researchers have demonstrated the severity of alignment
problems with a large spectrum of jailbreak techniques that can induce LLMs to
produce malicious content during conversations. Finding the corresponding
jailbreaking prompts usually requires substantial human intelligence or
computation resources. In this paper, we report that LLMs have different levels
of alignment in various contexts. As such, by systematically constructing many
contexts, called worlds, leveraging a Domain Specific Language describing
possible worlds (e.g., time, location, characters, actions and languages) and
the corresponding compiler, we can cost-effectively expose latent alignment
issues. Given the low cost of our method, we are able to conduct a large scale
study regarding LLM alignment issues in different worlds. Our results show that
our method outperforms the-state-of-the-art jailbreaking techniques on both
effectiveness and efficiency. In addition, our results indicate that existing
LLMs are extremely vulnerable to nesting worlds and programming language
worlds. They imply that existing alignment training focuses on the real-world
and is lacking in various (virtual) worlds where LLMs can be exploited.