Automatic vulnerability detection is of paramount importance to promote the security of an application and should be exercised at the earliest stages within the software development life cycle (SDLC) to reduce the risk of exposure. Despite the advancements with state-of-the-art deep learning techniques in software vulnerability detection, the development environments are not yet leveraging their performance. In this work, we integrate the Transformers architecture, one of the main highlights of advances in deep learning for Natural Language Processing, within a developer-friendly tool for code security. We introduce VDet for Java, a transformer-based VS Code extension that enables one to discover vulnerabilities in Java files. Our preliminary model evaluation presents an accuracy of 85.8% for multi-label classification and can detect up to 21 vulnerability types. The demonstration of our tool can be found at https://youtu.be/OjiUBQ6TdqE.