Researchers have introduced Prism, a novel plug-in infrastructure designed to facilitate scalable multimodal continual instruction tuning for large language models. This development addresses a significant bottleneck in current research, where the need for continuous adaptation to emerging tasks is hindered by severe engineering challenges. Prism enables reproducible and efficient instruction tuning, allowing multimodal large language models to adapt to new tasks and datasets seamlessly. The framework's scalability and flexibility make it an essential tool for real-world deployment, particularly in scenarios where state-aligned threat activity raises the stakes from criminal to geopolitical 1. By streamlining the instruction tuning process, Prism has significant implications for the development of more versatile and adaptable language models. This matters to practitioners because it enables them to stay ahead of emerging threats and adapt to changing landscapes, extending the implications beyond the immediate target.