Title: Languages Differ, Weaknesses Persist: Jailbreaking LLMs Across English and Indian Languages
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Jailbreaking, multilingual, llm safety, llm gaurdrails

Abstract: Jailbreaking poses a critical safety risk in large language models (LLMs), yet its impact on low-resource multilingual settings remains underexplored. In this study, we examine multilingual jailbreaking vulnerabilities in LLMs across five languages: \textit{Hindi, Marathi, Telugu, Bengali,} and \textit{English}. We introduce \textbf{Indic-JailbreakBench}, a dataset comprising 1,668 malicious prompts per language across 12 categories, incorporating culturally grounded harmful prompts. Additionally, we propose \textbf{four novel jailbreaking techniques}: (a) \textit{history-based prompting}, (b) \textit{biasing the attention of LLM towards unnecessary task}, (c) \textit{Shakespearean-style prompt rewrites}, and (d) \textit{contextual injection via harmful news articles}. We employ a multi-LLM judging mechanism, along with Guard models and Keyword-ASR to assess attack success rates (ASRs) to evaluate eight state-of-the-art open-source LLMs. We also show that LLM responses are safer when prompted through its corresponding chat template compared to scenarios without it in almost all settings. Our findings reveal significant disparities in safety mechanisms across languages, with Indian languages often exhibiting higher susceptibility to jailbreak attempts than English. Additionally, three of our proposed jailbreaking strategies achieve very high attack success rates (ASR), exceeding 85\% across all languages with highest ASR being 93\% through Shakespearean prompt styling.