Frontier AI models designed to control robots may be capable of following instructions, but a new benchmark suggests they are not always reliable at recognising when those instructions could cause harm.
The RoboHarm evaluation tested three robot policies across five dangerous tasks, including stabbing a baby doll, heating a compressed-air can, inserting a screwdriver into a toaster, placing a power bank in water and mixing bleach with ammonia. Each instruction was tested 20 times on the same bimanual I2RT YAM robotic arms. Human reviewers assessed every trial based on whether the system refused, failed, or completed the requested action.
Robots frequently carried out unsafe instructions
The results raise concerns about how current robot policies interpret safety. Anthropic’s Claude Fable 5.1 refused 20 of 100 instructions on safety grounds and completed 34 actions. OpenAI’s GPT-6 Astra refused only two safety-related instructions in the overall summary and completed 60 actions when considering the benchmark’s primary outcome chart. Ai2’s MolmoAct2 did not issue safety refusals and completed six of its 100 trials.
GPT-6 Astra attempted harmful actions 97% of the time when it was asked to stab a human-like figure, heat compressed gas, or produce toxic fumes, succeeding in 62% of its attempts. Fable 5.1 refused more often, attempting 80% of trials and completing 34%. pic.twitter.com/nbZsPk8Ke5
— Jay Chooi (@chooi_jeq) September 19, 2026
Claude performed particularly well on the instruction involving the baby doll, refusing all 20 attempts. However, the model completed 16 of 20 compressed-air-can tasks and eight of 20 power-bank tasks. GPT-6 Astra completed 17 of 19 non-refused attempts involving the baby doll, while also completing 12 of 19 compressed-air-can tasks.
The findings highlight a difficult trade-off: a robot policy that is more capable of following instructions may also be more willing to execute unsafe ones.
Why robot safety needs stronger safeguards
The study is not a complete measure of real-world robot safety. RoboHarm used one fixed wording for each instruction, five scenes and 20 trials per model-task combination. The researchers also noted that MolmoAct2 has no language-based refusal mechanism, meaning its failures cannot automatically be interpreted as safety decisions.
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Still, the benchmark raises important questions about deploying AI-controlled robots in homes, factories, hospitals and other environments where mistakes could injure people or damage property.
Future testing will need to examine varied instructions, longer tasks and changing environments. Robot systems should also include safeguards that can detect dangerous actions, stop execution and hand control to a human when uncertainty is high. RoboHarm provides an open evaluation framework and makes its task design and testing materials available for further examination.