Assessing Adversarial Robustness of Large Language Models: An Empirical Study

• URL: http://arxiv.org/abs/2405.02764v2
• Date: Thu, 12 Sep 2024 22:18:03 GMT
• Title: Assessing Adversarial Robustness of Large Language Models: An Empirical Study
• Authors: Zeyu Yang, Zhao Meng, Xiaochen Zheng, Roger Wattenhofer,
• Abstract summary: Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We present a novel white-box style attack approach that exposes vulnerabilities in leading open-source LLMs, including Llama, OPT, and T5.
• Score: 24.271839264950387
• License: http://creativecommons.org/licenses/by/4.0/
• 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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