aiqbench analysis
This research identifies critical safety vulnerabilities in embodied AI systems where automatic speech recognition errors can bypass refusal mechanisms and execute harmful instructions. The study demonstrates that while some correction strategies mitigate risk, they are not universally effective, highlighting the need for robust input validation in voice-controlled robotics.
When Robots Mishear Us: Mapping the Safety Risks of Voice-Controlled Embodied AI
arXiv:2608.28518v1 Announce Type: new Abstract: We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that ASR errors can lead to harmful instructions being accepted and executed by EAI models, thereby reducing safety. We simulate ASR errors and combine them with existing safety benchmarks (SafeAgentBench and POEX) to evaluate how different errors affect embodied AI safety. We find that some of them preserve semantic structure but increase harmful ambiguity, while others weaken the model refusal behaviour and allow unsafe plans to be generated and executed. We show that in some cases automatic correction of ASR errors can reduce the risk, but this is not always effective. Overall, we show that ASR errors lead to significant safety risks for embodied AI.
Abstract:We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that ASR errors can lead to harmful instructions being accepted and executed by EAI models, thereby reducing safety. We simulate ASR errors and combine them with existing safety benchmarks (SafeAgentBench and POEX) to evaluate how different errors affect embodied AI safety. We find that some of them preserve semantic structure but increase harmful ambiguity, while others weaken the model refusal behaviour and allow unsafe plans to be generated and executed. We show that in some cases automatic correction of ASR errors can reduce the risk, but this is not always effective. Overall, we show that ASR errors lead to significant safety risks for embodied AI.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
arXiv:2608.28518 [cs.AI]
(or arXiv:2608.28518v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.28518 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 228 words
