**Adversarial Robustness Degradation in Gemini Models from Multilingual Pretraining Imbalance**

Abstract

Abstract: In an era dominated by Large Language Models (LLMs), understanding their capabilities and limitations, especially in high-stakes fields like law, is crucial. While LLMs such as Meta's LLaMA, OpenAI's ChatGPT, Google's Gemini, DeepSeek, and other emerging models are increasingly integrated into legal workflows, their performance in multilingual, jurisdictionally diverse, and adversarial contexts remains insufficiently explored. This work evaluates LLaMA and Gemini on multilingual legal and non-legal benchmarks, and assesses their adversarial robustness in legal tasks through character and word-

Research Question

To what extent does multilingual pretraining data imbalance degrade the adversarial robustness of Gemini models when evaluating cross-jurisdictional legal constraints in non-English languages?

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