SafeCoEvo: Co-Evolving Safety Harnesses and Guards for LLM Agents at Test Time

LLM agents deployed in real-world environments continually encounter new tasks and safety risks, while execution feedback typically becomes available only after each task is completed. However, existing self-evolving approaches commonly rely on multiple rounds of optimization over fixed and repeatedly accessible task distributions, fundamentally differing from test-time adaptation in real-world deployment, where only experience accumulated from past tasks can be used to improve safety decisions on future unseen tasks. To address this limitation, we propose **SafeCoEvo**, a test-time Harness–Guard co-evolution framework for LLM agent safety that enables the external safety system to continually adapt from accumulated runtime experience. SafeCoEvo jointly improves two complementary safety capabilities at different timescales: **S-Harness** rapidly externalizes recent runtime experience into updatable explicit safety knowledge that can promptly influence subsequent tasks, while **GuardVPO** internalizes accumulated runtime safety experience over a longer timescale into parametric risk-judgment capabilities. By combining short-term rapid adaptation with long-term capability consolidation, SafeCoEvo continually improves the agent's safety capabilities, reducing the unsafe outcome rate by **10.05%** while improving the task success rate by **12.15%** over the strongest baseline, thereby achieving simultaneous gains in safety and task utility.