AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions 事件

AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions arXiv:2605.25707v1 Announce Type: new Abstract: Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-world execution environments are far from ideal: pop-ups, resolution changes, and competing applications frequently interfere with agent perception and control. We introduce AgentHij