Web attack is a major threat to cyberspace security, so web attack detection models have become a critical task. Traditional supervised learning methods learn features of web attacks with large amounts of high-confidence labeled data, which are extremely expensive in the real world. Pre-trained models offer a novel solution with their ability to learn generic features on large unlabeled datasets. However, designing and deploying a pre-trained model for real-world web attack detection remains challenges. In this paper, we present a pre-trained model for web attack detection, including a pre-processing module, a pre-training module, and a deployment scheme. Our model significantly improves classification performance on several web attack detection datasets. Moreover, we deploy the model in real-world systems and show its potential for industrial applications.