An Adversarial Attack Approach on Financial LLMs Driven by Embedding-Similarity Optimization

Abstract

Adversarial attacks on financial sentiment analysis models are a critical area of research within NLP. We introduce a novel white-box attack method that leverages a pre-trained general-purpose language model to generate high-quality and human-imperceptible attacks. Unlike existing methods that rely on training specialized adversarial models or computationally-intensive gradient optimization routines, our approach employs carefully-designed instructions and a novel embedding-similarity function to maintain semantic integrity while prod Show more