Large language models (LLMs) are increasingly used as high-level planners for robots: a natural-language mission is turned into a sequence of actions that a classical controller executes. Recent work has shown that such LLM-controlled robots can be jailbroken into performing physically harmful actions, often with 100% attack success, on commercial platforms [1]. Proposed defenses either wrap the planner in a guardrail [2] or restrict what the planner may emit. Planner–executor agents such as PEACE [3] take the second route: the LLM produces a single, fully typed plan over a fixed tool schema instead of free-form code or a step-by-step reasoning loop. Whether this architectural choice actually reduces the attack surface has never been measured. This project builds a reusable red-teaming harness for LLM-enabled robot planners in simulation and uses it to compare architectures under a common set of attacks.