When Words Are Safe But Actions Kill: Probing Physical Danger Beyond Text Safety in Hidden-State Risk Space
Score
79首次系统区分LLM表征中的物理危险与内容危险，提出分离性证明和轻量探针，信息增量显著。
实验设计严谨（随机分割假设检验、多模型验证、新基准构建），结果清晰，论文组织完整。
PRISM探针轻量有效，可直接用于具身Agent安全部署，但需进一步验证真实环境泛化性。
发布于2025年7月，正值LLM具身应用快速发展期，物理安全是前沿热点。
来源arXiv，但作者单位及实验细节充分，且基于多个开源模型和基准，结果可复现。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper is methodologically rigorous, combining separability evidence with a lightweight probe and a new benchmark, making a significant contribution to LLM embodied safety.
AI commentary
「This paper hits the core safety blind spot in LLM embodiment: text safety does not guarantee action safety. PRISM, a single-layer logistic probe, outperforms LLM judges by a large margin, offering strong practical value. It is a must-read for researchers working on robotics, autonomous driving, and AI agent safety, providing a new representation-level detection paradigm that could replace traditional text-based moderation.」
Quick overview
This paper reveals that physical danger (PD) and content danger (CD) form separable signals in LLM hidden-state representations when LLMs serve as planners for embodied agents. It proposes PRISM, a single-layer L2-regularized logistic probe, achieving 86.2–87.7% accuracy and 11.7–13.7% FPR on SafeAgentBench, outperforming same-scale LLM judges (24.7–39.0% FPR). It also introduces PhysicalSafetyBench-1K (PSB-1K), a contrastive benchmark without explicit harm keywords, where PRISM attains 99.6% accuracy and 0.7% FPR, while a Qwen2.5-3B judge wrongly rejects 67.8% safe tasks. Results replicate on SafeText and EARBench, supporting hidden-state probing as a representation-level method for physical safety beyond text moderation.
- Hidden-state direction analysis and random-split null tests show that physical danger (PD) and content danger (CD) are separable signals in LLM representations.
- PRISM, a single-layer logistic probe, achieves high accuracy with low false positive rate on SafeAgentBench.
- PSB-1K benchmark eliminates direct harm keywords to test physical danger detection beyond explicit unsafe wording.
- PRISM replicates across multiple models and benchmarks, suggesting hidden-state probing as a general representation-level method for physical safety.
Full text
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