Researchers have found that large language models can be vulnerable to 'jailbreaks' where seemingly safe instructions become unsafe when executed in the physical world. To address this issue, they developed a method called PRISM that uses hidden-state probing to identify potential safety risks. The study used various benchmarks and showed that PRISM achieved high accuracy in detecting physically grounded jailbreaks while keeping false-positive rates relatively low. The results suggest that hidden-state probing can be an effective approach for ensuring physical safety beyond text moderation. --- Why it matters: This research matters because it highlights the importance of considering the physical implications of language models' outputs, which can have real-world consequences. Engineers and researchers working on AI systems need to develop methods like PRISM to ensure their creations do not cause harm or damage. Source: https://arxiv.org/abs/2607.15218