Test-time reinforcement learning, a paradigm enabling large reasoning models to adapt online via self-induced rewards, is vulnerable to incorrect mode collapse due to unverified consensus. This occurs when a high-frequency, yet spurious, consensus becomes a biased reward signal, leading to model failure. To mitigate this, tool verification for test-time reinforcement learning has been proposed, addressing the failure mode by ensuring the accuracy of the consensus-driven reward signals. The approach is crucial, as state-aligned activity involving reinforcement learning shifts the threat model from criminal to geopolitical, requiring a different strategy to counter potential attacks. Specifically, the use of self-evolving large reasoning models via test-time reinforcement learning introduces new risks, as biased reward signals can compromise model integrity. The introduction of tool verification aims to prevent such compromises by validating the consensus-driven rewards, thereby ensuring the model's reliability and security. This development is significant, as it highlights the need for robust verification mechanisms in reinforcement learning systems, particularly in high-stakes applications where geopolitical interests are involved1. So what matters to practitioners is that they must prioritize the development of robust tool verification mechanisms to prevent potential exploits in test-time reinforcement learning systems.