Transformers, the powerhouse behind many AI applications, have a hidden weakness: they're vulnerable to adversarial attacks. Even tiny, almost imperceptible changes to input data can dramatically alter their output, leading to unpredictable and sometimes harmful results. Imagine a spam filter tricked by a single misplaced comma or a self-driving car misinterpreting a slightly altered road sign. This vulnerability poses significant security risks and limits the reliability of AI systems. Researchers have introduced ProTransformer, a clever solution designed to enhance the robustness of these crucial models. Instead of retraining the entire model, ProTransformer introduces a new, plug-and-play component called ProAttention. This component acts like a shield, protecting the transformer's core attention mechanism from being overly influenced by manipulated input tokens. The magic lies in a novel interpretation of the attention mechanism as a weighted least-squares estimator and the implementation of robust token estimators. ProAttention cleverly down-weights or even removes outlier data points caused by adversarial tweaks. Through rigorous testing across various tasks like topic classification, sentiment analysis, and even jailbreak attacks on large language models, ProTransformer consistently improves robustness without compromising accuracy. For instance, in experiments, ProTransformer boosted the resilience of models like BERT, ALBERT, and even large language models like T5 and LLaMA by significant margins under various attack scenarios. Impressively, this resilience extends beyond just text; ProTransformer also shows promise in strengthening vision and graph-based AI models, highlighting its adaptability. While further research is ongoing to optimize efficiency, ProTransformer's elegant plug-and-play nature paves the way for more robust and trustworthy AI systems across a multitude of applications.