Adversarial attacks are label-preserving modifications to inputs of machine learning
classifiers designed to fool machines but not humans. Natural Language Processing (NLP) …

Natural Language Processing (NLP) models based on Machine Learning (ML) are
susceptible to adversarial attacks--malicious algorithms that imperceptibly modify input text …

Although pre-trained language models (PrLMs) have achieved significant success, recent
studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating …

Abstract Machine learning algorithms are often vulnerable to adversarial examples that have
imperceptible alterations from the original counterparts but can fool the state-of-the-art …

Adversarial attacks on deep neural networks keep raising security concerns in natural
language processing research. Existing defenses focus on improving the robustness of the …

We study an important and challenging task of attacking natural language processing
models in a hard label black box setting. We propose a decision-based attack strategy that …

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Abstract Language models are known to be vulnerable to textual adversarial attacks, which
add humanimperceptible perturbations to the input to mislead DNNs. It is thus imperative to …

State-of-the-art machine learning models are prone to adversarial attacks”:” Maliciously
crafted inputs to fool the model into making a wrong prediction, often with high confidence …