Evaluating LLMs Robustness in Less Resourced Languages with Proxy Models
Authors: name: Maciej Chrabąszcz, name: Katarzyna Lorenc, name: Karolina Seweryn

Large language models (LLMs) have demonstrated impressive capabilities across
various natural language processing (NLP) tasks in recent years. However, their
susceptibility to jailbreaks and perturbations necessitates additional
evaluations. Many LLMs are multilingual, but safety-related training data
contains mainly high-resource languages like English. This can leave them
vulnerable to perturbations in low-resource languages such as Polish. We show
how surprisingly strong attacks can be cheaply created by altering just a few
characters and using a small proxy model for word importance calculation. We
find that these character and word-level attacks drastically alter the
predictions of different LLMs, suggesting a potential vulnerability that can be
used to circumvent their internal safety mechanisms. We validate our attack
construction methodology on Polish, a low-resource language, and find potential
vulnerabilities of LLMs in this language. Additionally, we show how it can be
extended to other languages. We release the created datasets and code for
further research.