ArmedAgent finds safety risks in coding agents that control robot arms
Original title: When Coding Agents Get Arms: Safety Risks Across the Digital-Physical Boundary
The benchmark covers 36 tasks in five scenarios, including indirect prompt injection that can move from digital resources into physical actions.
A paper introduces ArmedAgent, a benchmark for coding agents that control a real robot arm zero-shot and end-to-end. It evaluates robustness, malicious use, and indirect prompt injection across 36 tasks in five scenarios, in simulation and on a physical arm. Some tasks target harmful behaviors earlier vision-language-action paradigms could not perform. Tests of several frontier models, including GPT-6-Astra, under widely used agent frameworks found substantial safety risks.
Unconfirmed: Safety findings are the authors' benchmark results, not reports of real-world misuse.
- ArmedAgent is described as the first systematic benchmark for zero-shot robot control by coding agents.
- Tasks cover robustness, malicious use, and indirect injection in simulation and on hardware.
- Agents can reach web pages and email, so injected content can affect physical actions.
- GPT-6-Astra was among the frontier models tested under common agent frameworks.
- Products
- ArmedAgent
- AI models
- GPT-6-Astra
From the original article
1 min read at arxiv.orgAdvances in spatial understanding have enabled frontier models to power coding agents that control a real robot arm zero-shot and end-to-end, without task-specific demonstrations or retraining. This brings two new safety challenges. First, the extensive knowledge of frontier models lets these agents perform tasks that earlier VLA and WAM paradigms could not, opening the door to new forms of capability misuse.
By Yiming Sun · arxiv.org
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