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Assessing Adversarial Robustness of Large Language Models: An Empirical Study

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arxiv 2405.02764 v2 pith:ZDY5Z3HV submitted 2024-05-04 cs.CL cs.LG

Zeyu Yang , Zhao Meng , Xiaochen Zheng , Roger Wattenhofer This is my paper · ORCID
classification cs.CLcs.LG
keywords adversariallanguagellmsrobustnesslargemodelsacrossadvancement
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol

    cs.SE 2025-08 conditional novelty 5.0 of 10

    This position paper classifies testing methods for LLM applications into three layers and proposes AICL, a structured protocol for testable agent communication; neither the framework nor the protocol is empirically validated.

  2. Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach

    cs.CL 2025-07 reject novelty 4.0 of 10

    A Jacobian-magnitude regularization called GBM improves empirical robustness of CNN/LSTM/S4 text classifiers to synonym-substitution attacks, but the claimed certified robustness is not delivered.

  3. On Adversarial Robustness of Language Models in Transfer Learning

    cs.CL 2024-12 reject novelty 4.0 of 10

    Sequential fine-tuning across related bias-detection tasks tends to raise adversarial attack success rates, but the size-resilience pattern the paper highlights is not borne out by its own data.

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