Autonomous Driving Systems (ADS) are advancing rapidly due to progress in deep learning, yet critical challenges remain, particularly in the realm of safety verification. As safety-critical systems, ADS must undergo rigorous testing across diverse scenarios. Real-world data, while valuable, are inherently inflexible for interaction and scenario customization. In contrast, simulator-generated synthetic scenarios provide a platform that enables interaction, control, editability, and adaptability to specific needs. However, current simulation approaches are limited—either relying on costly, manually crafted, overly templated scenarios or generating unconditioned trivial behaviors based on learned distributions. In this work, we propose LASER, an innovative framework that leverages large language models (LLMs) to conduct traffic simulations based on natural language inputs. The framework operates in two phases. First, it generates driving scripts from user-provided descriptions. Second, it executes these scripts by guiding autonomous agents within the CARLA simulator to perform tasks in real-time. This method effectively decomposes tasks, allocates controls, and integrates interactive elements to create dynamic and scalable simulations. By using LASER, we overcome the rigid constraints of traditional simulation methods, enabling the creation of complex, diverse, flexible and on-demand driving scenarios. The approach significantly enhances the process of generating ADS training and testing data, addressing the scalability and variability issues associated with previous simulation models. The code and all demos are available anonymously at https://njudeepengine.github.io/LASER/.