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This research introduces PhoneSafety, a new benchmark that distinguishes between an agent's deliberate safe choices and mere inability to act due to capability limits. The study finds that high general performance on standard tasks does not guarantee safety in critical moments, highlighting a crucial gap in current evaluation metrics for embodied AI agents. By isolating decision-making at risky moments across diverse apps, the work provides essential insights for developing more robust and trustworthy phone-use agents.
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Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents

arXiv3 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2605.07630v2 Announce Type: replace-cross Abstract: When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent recognized the risk and chose the safe action, or because it failed to understand the screen or execute any relevant action at all. These cases have different causes and call for different fixes, yet current benchmarks often merge them under task success, refusal, or final harmful outcome. We address this problem with PhoneSafety, a benchmark of 700 safety-critical moments drawn from real phone interactions across more than 130 apps. Each instance isolates the next decision at a risky moment and asks a simple question: does the model take the safe action, take the unsafe action, or fail to do anything useful? We evaluate eight representative phone-use agents under this framework. Our results reveal two main patterns. First, stronger general phone-use ability does not reliably imply safer choices at risky moments. Models that perform better on ordinary app tasks are not always the ones that behave more safely when the next action matters. Second, failures to do anything useful behave like a capability signal rather than a safety signal: they track broader phone-use performance and increase when visual input is removed. Across models, failures split into two recurring patterns: unsafe choices in settings where the model can act but chooses wrongly, and inability to act in more visually and operationally demanding screens. Overall, a harmless outcome is not enough to count as evidence of safety. Evaluating phone-use agents requires separating unsafe judgment from inability to act. Code and public data: \url{https://github.com/tangzhy/PhoneSafety}.

Title:Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents

Abstract:When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent recognized the risk and chose the safe action, or because it failed to understand the screen or execute any relevant action at all. These cases have different causes and call for different fixes, yet current benchmarks often merge them under task success, refusal, or final harmful outcome. We address this problem with PhoneSafety, a benchmark of 700 safety-critical moments drawn from real phone interactions across more than 130 apps. Each instance isolates the next decision at a risky moment and asks a simple question: does the model take the safe action, take the unsafe action, or fail to do anything useful? We evaluate eight representative phone-use agents under this framework. Our results reveal two main patterns. First, stronger general phone-use ability does not reliably imply safer choices at risky moments. Models that perform better on ordinary app tasks are not always the ones that behave more safely when the next action matters. Second, failures to do anything useful behave like a capability signal rather than a safety signal: they track broader phone-use performance and increase when visual input is removed. Across models, failures split into two recurring patterns: unsafe choices in settings where the model can act but chooses wrongly, and inability to act in more visually and operationally demanding screens. Overall, a harmless outcome is not enough to count as evidence of safety. Evaluating phone-use agents requires separating unsafe judgment from inability to act. Code and public data: \url{this https URL}.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2605.07630 [cs.CL]

(or arXiv:2605.07630v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.07630 Focus to learn more arXiv-issued DOI via DataCite

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