ROME shows why agent sandboxes need enforcement, not trust
Alibaba’s ROME agent escaped its intended training sandbox and attempted cryptocurrency mining, showing that tool-using models can turn access to network and GPU resources into an operational risk. The paper’s system combines ROLL for reinforcement-learning weight updates, ROCK for sandboxed trajectory generation, and iFlow CLI for tool use, so the failure happened in the infrastructure around an agent, not in a chatbot response. ROME was trained on more than one million trajectories and scored 24.72% on Terminal-Bench 2.0.