The security and robustness of machine learning algorithms have become increasingly important as they are used in critical applications such as natural language processing (NLP), e.g., text-based spam detection. Recently, the emerging brain-inspired hyperdimensional computing (HDC), compared to deep learning methods, has shown advantages such as compact model size, energy efficiency, and capability of few-shot learning in various NLP applications. While HDC has been demonstrated to be vulnerable to adversarial attacks in image and audio input, there is currently very limited study on its adversarial security to NLP tasks, which is arguable one of the most suitable applications for HDC. In this paper, we present a novel study on the adversarial attack of HDC-based NLP applications. By leveraging the unique properties in HDC, the similarity-based inference, we propose similarity-guided approaches to automatically generate adversarial text samples for HDC. Our approach is able to achieve up to 89% attack success rate. More importantly, by comparing with unguided brute-force approach, similarity-guided attack achieves a speedup of 2.4X in generating adversarial samples. Our work opens up new directions and challenges for future adversarially-robust HDC model design and optimization.