benchmark arXiv Sep 5, 2025 · Sep 2025
Taniya Gidatkar, Oluwaseun Ajao, Matthew Shardlow · Manchester Metropolitan University
Benchmarks BERT, RoBERTa, and Flan-T5 robustness against word-substitution adversarial attacks, revealing stark vulnerability differences
Input Manipulation Attack nlp
This study evaluates the resilience of large language models (LLMs) against adversarial attacks, specifically focusing on Flan-T5, BERT, and RoBERTa-Base. Using systematically designed adversarial tests through TextFooler and BERTAttack, we found significant variations in model robustness. RoBERTa-Base and FlanT5 demonstrated remarkable resilience, maintaining accuracy even when subjected to sophisticated attacks, with attack success rates of 0%. In contrast. BERT-Base showed considerable vulnerability, with TextFooler achieving a 93.75% success rate in reducing model accuracy from 48% to just 3%. Our research reveals that while certain LLMs have developed effective defensive mechanisms, these safeguards often require substantial computational resources. This study contributes to the understanding of LLM security by identifying existing strengths and weaknesses in current safeguarding approaches and proposes practical recommendations for developing more efficient and effective defensive strategies.
transformer llm Manchester Metropolitan University
ML Security Papers