Precision vs. Perturbation: Robustness Analysis of Synonym Attacks in Ukrainian NLP

Synonym-based adversarial tests reveal fragile word patterns that accuracy metrics overlook, while virtually no such diagnostics exist for Ukrainian, a morphologically rich and low-resource language.We present the first systematic robustness evaluation under synonym substitution in Ukrainian.Adapting TEXTFOOLER and BERT-ATTACK to Ukrainian, we (i) adjust a 15000-entry synonym dictionary to match proper word forms; (ii) integrate similarity filters; (iii) adapt masked-LM search so it generates only valid inflected words.Across three text classification datasets (reviews, news headlines, social-media manipulation) and three transformer models (Ukr-RoBERTa, XLM-RoBERTa, SBERT), single-word swaps reduce accuracy by up to 12.6, while multi-step attacks degrade performance by as much as 40.27 with around 112 model queries.A few-shot transfer test shows GPT-4o, a state-of-the-art multilingual LLM, still suffers 6.9-15.0drops on the same adversarial samples.Our results underscore the need for sense-aware, morphology-constrained synonym resources and provide a reproducible benchmark for future robustness research in Ukrainian NLP.