SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents. SENTINEL is the first to provide multi-level safety evaluation across semantic interpretation, plan generation, and physical execution within a unified formal framework. Unlike prior methods that rely on heuristic rules or subjective FM judgments, SENTINEL grounds practical safety requirements in formal temporal logic (TL) semantics that can precisely specify state invariants, temporal dependencies, and timing constraints. It employs a multi-level verification pipeline where (i) at the semantic level, intuitive natural language safety requirements are formalized into TL formulas and the agent's understanding of these requirements is probed for alignment with the TL formulas; (ii) at the plan level, high-level action plans and subgoals generated by the agent are verified against the TL formulas to detect unsafe plans before execution; and (iii) at the trajectory level, multiple execution trajectories are merged into a computation tree and efficiently verified against physically-detailed TL specifications for a final safety check. We apply SENTINEL in VirtualHome and AI2-THOR, and formally evaluate multiple FM-based embodied agents against diverse safety requirements. Our experiments show that by grounding physical safety in temporal logic and applying verification methods across multiple levels, SENTINEL provides a rigorous foundation for systematically evaluating the safety of FM-based embodied agents in simulation-based physical environments, and can effectively expose potential safety violations in interpreting, planning, and executing the tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 53 references that are in the map. First posted on arXiv 14 Oct 2025.
Builds on
14 of 53 references.
Closest ancestors · 5
Same direction, newest first
- 2025IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks19 Jun
- 2025ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning26 Mar
- 2024SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents17 Dec
- 2024EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents8 Aug
- 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agents18 Jan
Other references · 9
- 2020Measuring Massive Multitask Language Understandingcited by 7,768
- 2025OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoningcited by 105
- 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 82
- 2024SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanationscited by 37
- 2024HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environmentscited by 32
- 2024LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Plannercited by 31
- 2025Generating Robot Constitutions & Benchmarks for Semantic Safetycited by 28
- 2024LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labscited by 24
- 2024Defining and Evaluating Physical Safety for Large Language Modelscited by 9
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents
- 2025RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic
- 2025A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
- 2025AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions
- 2026SafeRelBench: A Spatial-Relation-Aware Benchmark for Process-Level Safety in VLM-Driven Embodied Agents
- 2026ARGOS: Automated Functional Safety Requirement Synthesis for Embodied AI via Attribute-Guided Combinatorial Reasoning
- 2026EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents
- 2026SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models