SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems

• URL: http://arxiv.org/abs/2603.21523v1
• Date: Mon, 23 Mar 2026 03:31:51 GMT
• Title: SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems
• Abstract summary: Large Language Models (LLMs) have recently begun to be integrated into various cyber-physical systems.<n>LLMs produce "hallucinations" - outputs that are coherent yet factually incorrect or contextually unsuitable.<n>We propose SafePilot, a novel hierarchical neuro-symbolic framework that provides end-to-end assurance for CPS according to attribute-based and temporal specifications.
• Score: 2.2566909388480743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs), deep learning architectures with typically over 10 billion parameters, have recently begun to be integrated into various cyber-physical systems (CPS) such as robotics, industrial automation, and autopilot systems. The abstract knowledge and reasoning capabilities of LLMs are employed for tasks like planning and navigation. However, a significant challenge arises from the tendency of LLMs to produce "hallucinations" - outputs that are coherent yet factually incorrect or contextually unsuitable. This characteristic can lead to undesirable or unsafe actions in the CPS. Therefore, our research focuses on assuring the LLM-enabled CPS by enhancing their critical properties. We propose SafePilot, a novel hierarchical neuro-symbolic framework that provides end-to-end assurance for LLM-enabled CPS according to attribute-based and temporal specifications. Given a task and its specification, SafePilot first invokes a hierarchical planner with a discriminator that assesses task complexity. If the task is deemed manageable, it is passed directly to an LLM-based task planner with built-in verification. Otherwise, the hierarchical planner applies a divide-and-conquer strategy, decomposing the task into sub-tasks, each of which is individually planned and later merged into a final solution. The LLM-based task planner translates natural language constraints into formal specifications and verifies the LLM's output against them. If violations are detected, it identifies the flaw, adjusts the prompt accordingly, and re-invokes the LLM. This iterative process continues until a valid plan is produced or a predefined limit is reached. Our framework supports LLM-enabled CPS with both attribute-based and temporal constraints. Its effectiveness and adaptability are demonstrated through two illustrative case studies.

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