  This study identifies the potential vulnerabilities of Large Language Models
(LLMs) to 'jailbreak' attacks, specifically focusing on the Arabic language and
its various forms. While most research has concentrated on English-based prompt
manipulation, our investigation broadens the scope to investigate the Arabic
language. We initially tested the AdvBench benchmark in Standardized Arabic,
finding that even with prompt manipulation techniques like prefix injection, it
was insufficient to provoke LLMs into generating unsafe content. However, when
using Arabic transliteration and chatspeak (or arabizi), we found that unsafe
content could be produced on platforms like OpenAI GPT-4 and Anthropic Claude 3
Sonnet. Our findings suggest that using Arabic and its various forms could
expose information that might remain hidden, potentially increasing the risk of
jailbreak attacks. We hypothesize that this exposure could be due to the
model's learned connection to specific words, highlighting the need for more
comprehensive safety training across all language forms.
