Generative AI models are primarily accessed through chat interfaces, where user queries often contain typographical errors. While these models perform well in English, their robustness to noisy inputs in low-resource languages like Yorùbá remains underexplored. This work investigates a Yorùbá question-answering (QA) task by introducing synthetic typographical noise into clean inputs. We design a probabilistic noise injection strategy that simulates realistic human typos. In our experiments, each character in a clean sentence is independently altered, with noise levels ranging from 10% to 40%. We evaluate performance across three strong multilingual models using two complementary metrics: (1) a multilingual BERTScore to assess semantic similarity between outputs on clean and noisy inputs, and (2) an LLM-as-judge approach, where the best Yorùbá-capable model rates fluency, comprehension, and accuracy on a 1–5 scale. Results show that while English QA performance degrades gradually, Yorùbá QA suffers a sharper decline. At 40% noise, GPT-4o experiences over a 50% drop in comprehension ability, with similar declines for Gemini 2.0 Flash and Claude 3.7 Sonnet. We conclude with recommendations for noise-aware training and dedicated noisy Yorùbá benchmarks to enhance LLM robustness in low-resource settings.