SENTINEL is the first benchmark that formally evaluates the physical safety of foundation model (LLM/VLM) based embodied agents across three complementary levels: semantic interpretation, high-level planning, and physical trajectory execution. Unlike prior safety evaluations that rely on heuristics or subjective LLM judgments, SENTINEL grounds safety requirements in temporal logic (LTL/CTL), enabling precise, reproducible, and mechanically verifiable assessments. SENTINEL defines safety using formal semantics—state invariants, temporal orderings, conditional prohibitions, and long-horizon constraints—and evaluates whether agents (i) correctly interpret safety rules, (ii) generate safe high-level plans, and (iii) execute physically safe trajectories in simulation. This repo, SENTINEL-Physical-Safety-Benchmark, is instantiated in ALFRED (AI2-THOR) with a focus on trajectory-level evaluation. We implement an evaluation pipeline that runs an embodied agent in simulation, records traces, and checks them against **CTL safety specifications**, producing a structured report of task success and safety violations following **A2A** protocols. For more details on the SENTINEL framework, please visit our project website (https://nu-ideas-lab.github.io/Sentinel/) and check out our arXiv paper (https://arxiv.org/abs/2510.12985). They provide details on the motivation and methodology of SENTINEL, as well as implantation details and experimental results for an older version of it that focused on LLM-based embodied agents. Importantly, since following AgentBeats platform, we've noticed that running AI2THOR through docker image is extremely time consuming. So we have only provided a small set of examples and kept the interaction between green and purple agent to one time only. For more extensive task scenarios as well as VLM support through stepwise planning, please visit our project website.