10.9CLMay 20, 2025
Attributional Safety Failures in Large Language Models under Code-Mixed PerturbationsSomnath Banerjee, Pratyush Chatterjee, Shanu Kumar et al.
Recent advancements in LLMs have raised significant safety concerns, particularly when dealing with code-mixed inputs and outputs. Our study systematically investigates the increased susceptibility of LLMs to produce unsafe outputs from code-mixed prompts compared to monolingual English prompts. Utilizing explainability methods, we dissect the internal attribution shifts causing model's harmful behaviors. In addition, we explore cultural dimensions by distinguishing between universally unsafe and culturally-specific unsafe queries. This paper presents novel experimental insights, clarifying the mechanisms driving this phenomenon.