Advancing NLP Security by Leveraging LLMs as Adversarial Engines

TL;DR

This paper advocates for using Large Language Models as adversarial engines to generate diverse, effective, and human-like attacks in NLP, aiming to improve model robustness and security.

Contribution

It introduces a novel paradigm of employing LLMs for a broader spectrum of adversarial attacks beyond word-level examples, enhancing NLP security research.

Findings

LLMs can generate semantically coherent adversarial examples.

Expanding attack types increases vulnerability detection.

Potential to improve NLP model robustness.

Abstract

This position paper proposes a novel approach to advancing NLP security by leveraging Large Language Models (LLMs) as engines for generating diverse adversarial attacks. Building upon recent work demonstrating LLMs' effectiveness in creating word-level adversarial examples, we argue for expanding this concept to encompass a broader range of attack types, including adversarial patches, universal perturbations, and targeted attacks. We posit that LLMs' sophisticated language understanding and generation capabilities can produce more effective, semantically coherent, and human-like adversarial examples across various domains and classifier architectures. This paradigm shift in adversarial NLP has far-reaching implications, potentially enhancing model robustness, uncovering new vulnerabilities, and driving innovation in defense mechanisms. By exploring this new frontier, we aim to contribute to the development of more secure, reliable, and trustworthy NLP systems for critical applications.