JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large
  Language Models
Yingchaojie Feng Zhizhang Chen Zhining Kang Sijia Wang Minfeng Zhu Wei Zhang Wei Chen
The proliferation of large language models (LLMs) has underscored concerns
regarding their security vulnerabilities, notably against jailbreak attacks,
where adversaries design jailbreak prompts to circumvent safety mechanisms for
potential misuse. Addressing these concerns necessitates a comprehensive
analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and
identify potential weaknesses. However, the complexity of evaluating jailbreak
performance and understanding prompt characteristics makes this analysis
laborious. We collaborate with domain experts to characterize problems and
propose an LLM-assisted framework to streamline the analysis process. It
provides automatic jailbreak assessment to facilitate performance evaluation
and support analysis of components and keywords in prompts. Based on the
framework, we design JailbreakLens, a visual analysis system that enables users
to explore the jailbreak performance against the target model, conduct
multi-level analysis of prompt characteristics, and refine prompt instances to
verify findings. Through a case study, technical evaluations, and expert
interviews, we demonstrate our system's effectiveness in helping users evaluate
model security and identify model weaknesses.