The transformer, a neural network introduced by Google researchers nine years ago, has become a crucial component in large language models (LLMs). However, as LLMs continue to grow in size and complexity, the transformer's dense attention mechanism has become a significant bottleneck, increasing computational costs. This limitation has sparked a new wave of innovation, with startups exploring alternative architectures to overcome the transformer's shortcomings1. The shift towards more efficient and scalable models is driven by the need to improve performance while reducing computational expenses. As LLMs continue to advance, their potential impact on security and risk surfaces will be significant, making it essential for practitioners to stay informed about the latest developments. The evolution of LLMs will have far-reaching implications, and understanding these changes is crucial for mitigating potential security risks.