Researchers are exploring the limits of scaling down large language models to enable their integration into 6G networks, which are being designed as AI-native systems. The goal is to embed high-level semantic reasoning layers into these networks, operating above standardized control and data-plane functions. This effort is crucial as 6G standardization efforts are underway within organizations such as 3GPP, IETF, and the O-RAN Alliance. Current large language models like Qwen2.5-7B and Olmo-3-7B have demonstrated robust reasoning capabilities, but their size and complexity pose significant challenges for deployment in resource-constrained 6G networks. To address this, researchers are investigating methods to shrink these models while preserving their reasoning abilities, which is essential for realizing the full potential of AI-native 6G networks1. The development of smaller, more efficient language models has significant implications for the security landscape of 6G networks, as these models will introduce new capability and risk surfaces. As the 6G ecosystem continues to take shape, the security community must pay close attention to the evolving threat landscape and develop strategies to mitigate potential risks. The ability to scale down large language models will play a critical role in determining the security and functionality of 6G networks, making it a key area of focus for researchers and practitioners alike.