The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work, we propose Random Prompt Assisted Weakly-supervised 3D Object Detection, termed as Prompt3D, a weakly-supervised approach that leverages position-level labels to overcome this challenge. Explicitly, our method focuses on enhancing labeling using synthetic scenes crafted from 3D shapes generated via random prompts. First, a Synthetic Scene Generation (SSG) module is introduced to assemble synthetic scenes with a curated collection of 3D shapes, created via random prompts for each category. These scenes are enriched with automatically generated point-level annotations, providing a robust supervisory framework for training the detection algorithm. To enhance the transfer of knowledge from virtual to real datasets, we then introduce a Prototypical Proposal Feature Alignment (PPFA) module. This module effectively alleviates the domain gap by directly minimizing the distance between feature prototypes of the same class proposals across two domains. Compared with sota BR, our method improves by 5.4% and 8.7% on mAP with VoteNet and GroupFree3D serving as detectors respectively, demonstrating the effectiveness of our proposed method. Code is available at: https://github.com/huishengye/prompt3d.