Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in all adversarial scenarios. Furthermore, the Support Vector Machine (SVM) model performed best with prompt response features, particularly excelling in prompt type classification tasks. Classification results revealed that certain types of adversarial attacks, such as “Word Level” and “Adversarial Prefix”, were particularly difficult to detect, as indicated by their low recall and F1-scores. These findings suggest that more subtle manipulations can evade detection mechanisms. In contrast, attacks like “Sentence Level” and “Adversarial Insertion” were easier to identify, due to the model’s effectiveness in recognizing inserted content. Natural Language Processing (NLP) techniques played a critical role by enabling the extraction of semantic and syntactic features from both prompts and their corresponding responses. These insights highlight the importance of combining traditional and deep learning approaches, along with advanced NLP techniques, to build more reliable adversarial prompt detection systems for LLMs.

The purpose of this study is to develop a reliable method for distinguishing between AI-generated, paraphrased, and human-written texts, which is crucial for maintaining the integrity of research and ensuring accurate information flow in critical fields such as healthcare. To achieve this, we propose HybridGAD, a novel hybrid model that combines Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) architectures with an attention mechanism. Our methodology involves training this hybrid model on a dataset of radiology abstracts, encompassing texts generated by AI, paraphrased by AI, and written by humans. The major findings of our analysis indicate that HybridGAD achieves a high accuracy of 98%, significantly outperforming existing state-of-the-art models. This high performance is attributed to the model’s ability to effectively capture the contextual nuances and structural differences between AI-generated and human-written texts. In conclusion, HybridGAD not only enhances the accuracy of text classification in the field of radiology but also paves the way for more advanced medical diagnostic processes by ensuring the authenticity of textual information. Future research will focus on integrating textual and visual data for comprehensive radiology assessments and improving model generalization with partially labeled data. This study underscores the potential of HybridGAD in transforming medical text classification and highlights its applicability in ensuring the integrity and reliability of research in healthcare and beyond.