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arXiv · 2405.02764

Assessing Adversarial Robustness of Large Language Models: An Empirical Study

Zeyu Yang·Zhao Meng·Xiaochen Zheng·Roger Wattenhofer
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Abstract

Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white-box style attack approach that exposes vulnerabilities in leading open-source LLMs, including Llama, OPT, and T5. We assess the impact of model size, structure, and fine-tuning strategies on their resistance to adversarial perturbations. Our comprehensive evaluation across five diverse text classification tasks establishes a new benchmark for LLM robustness. The findings of this study have far-reaching implications for the reliable deployment of LLMs in real-world applications and contribute to the advancement of trustworthy AI systems.

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Zeyu Yang, Zhao Meng, Xiaochen Zheng, Roger Wattenhofer. 2024-05-04. Assessing Adversarial Robustness of Large Language Models: An Empirical Study. https://arxiv.org/abs/2405.02764

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