Researchers have introduced OpenSeeker-v2, a novel approach to developing search agents with advanced capabilities, leveraging informative and high-difficulty trajectories. This method challenges the conventional industry pipeline, which relies heavily on resource-intensive pre-training, continual pre-training, supervised fine-tuning, and reinforcement learning. By exploring alternative trajectories, OpenSeeker-v2 aims to push the boundaries of search agents, particularly in the context of Large Language Models (LLMs). The development of LLMs using reinforcement learning has significant implications for both capability and risk surfaces1. As LLMs become more prevalent, their potential risks and security vulnerabilities must be carefully considered. The introduction of OpenSeeker-v2 contributes to the ongoing effort to advance LLM capabilities while highlighting the need for careful evaluation of their security implications. This matters to practitioners because the security risks associated with LLMs can have far-reaching consequences, making it essential to prioritize their secure development and deployment.