Transformer-based Text Classifiers (TTC), exemplified by models like BERT, Roberta, T5, and GPT, have showcased impressive proficiency in Natural Language Processing (NLP). Nonetheless, their vulnerability to adversarial examples (AEs) presents a substantial security concern. Existing efforts to fortify model robustness either entail high computational costs or lack transparency and interpretability. This paper introduces a holistic framework calledExplainability-drivenDetection,Identification, andTransformation (EDIT) to address the shortcomings of prevailing inference-time defenses. (i) EDIT employs explainability methodologies (e.g., attention maps, integrated gradients) and frequency features for automated detection and identification of perturbed words while providing insights into the model's decision-making process. (ii) Building upon detection, EDIT purifies detected adversarial examples through an optimal transformation mechanism that utilizes pre-trained embeddings and model feedback to find the best replacements for identified perturbed words. (iii) To integrate human intelligence, the framework automates the process and triggers alerts for a security analyst to ensure secure safeguards and enable proactive human feedback. We comprehensively evaluate our framework on BERT and ROBERTA, trained on four SOTA text classification datasets: IMDB, YELP, AGNEWS, and SST2. We also assess our framework against seven SOTA Word Substitution Attacks (WSA) at character, word, and multi-levels of granularity. The results show that EDIT achieves an average F-score and balanced accuracy of 89.69% and 89.70%, respectively, across all datasets and classifiers. Compared to four SOTA inference defenses, our detector is 1.22 times better in balanced accuracy (BAL_ACC) and 1.33 times better in F1-Score. It also excels in operational efficiency, being about 83 times faster in feature extraction. The identification module outperforms techniques like ReplaceScore, FreqScore, and ExplainScore, achieving 1.125 times better BAL_ACC and Recall, 1.142 times higher AUC, 1.120 times greater Precision, and 1.149 times improved F1-Score. It significantly reduces false positives and negatives by approximately 1.537 and 1.172 times, respectively. Our transformation module effectively converts adversarial examples into non-adversarial counterparts with an accuracy of 91%. EDIT identifies the necessity for human intervention with an average median accuracy of 89%. With alerts integrated, EDIT achieves an average median accuracy of 90% across all evaluated datasets, models, and types of attacks, all while maintaining computational efficiency, averaging 6.94 seconds.

Software Engineering researchers are increasingly using Natural Language Processing (NLP) techniques to automate Software Vulnerabilities (SVs) assessment using the descriptions in public repositories. However, the existing NLP-based approaches suffer from concept drift. This problem is caused by a lack of proper treatment of new (out-of-vocabulary) terms for the evaluation of unseen SVs over time. To perform automated SVs assessment with concept drift using SVs' descriptions, we propose a systematic approach that combines both character and word features. The proposed approach is used to predict seven Vulnerability Characteristics (VCs). The optimal model of each VC is selected using our customized time-based cross-validation method from a list of eight NLP representations and six well-known Machine Learning models. We have used the proposed approach to conduct large-scale experiments on more than 100,000 SVs in the National Vulnerability Database (NVD). The results show that our approach can effectively tackle the concept drift issue of the SVs' descriptions reported from 2000 to 2018 in NVD even without retraining the model. In addition, our approach performs competitively compared to the existing word-only method. We also investigate how to build compact concept-drift-aware models with much fewer features and give some recommendations on the choice of classifiers and NLP representations for SVs assessment.