Personalised services, inventory optimisation, and fraud detection have all been made possible by the incorporation of machine learning (ML) into e-commerce. But it also makes platforms vulnerable to adversarial attacks, in which criminals alter data inputs in order to take advantage of flaws in the model. System dependability, data security, and customer trust are all compromised by these threats, which range from phishing emails to tampered reviews and transaction data. Critical defence techniques provided by Natural Language Processing (NLP) include anomaly detection, ensemble modelling, adversarial training, and context-aware analysis with models such as BERT and GPT. Dynamic feature selection is one technique that reduces computational load while increasing performance. Unusual patterns in user behaviour and content can be found with the aid of behavior-based techniques and NLP-driven analytics. When combined, these tactics strengthen resistance to changing threats. For cybersecurity in sustainable e-commerce, continuous research, real-time