ElecSafety: Diagnosing Large Language Model Safety Judgments for Microcontroller Boards

2026-10-01

AI Summary

This study addresses the inability of large language models (LLMs) to assess board-level electrical safety for microcontrollers in embedded development by proposing ElecSafety, the first benchmark tailored to this domain. Methodologically, electrical constraints are extracted from manufacturer datasheets and formalized into natural language to construct a test suite encompassing complex scenarios such as operational violations, boundary conditions, and missing information. Six open-source LLMs are evaluated under multiple conditions. Results demonstrate that incorporating board-level rules significantly improves model accuracy; however, LLMs remain unreliable in boundary and information-deficient scenarios, frequently depending on erroneous constraints. These findings reveal critical limitations of current models in hardware safety applications.

Abstract

Large language models (LLMs) progressively support embedded hardware development, but a plausible recommendation may pose safety risks when acting on the physical hardware. Because electrical safety can be different between every microcontroller board, a practical question is raised: can LLMs have the capability to make the right decision on whether a proposed user operation is safe for the board? Although benchmarks for embedded development exist, they primarily target code generation and hardware design tasks. This highlights the lack of tested board-specific judgment regarding electrical safety. To address this gap, we introduce the ElecSafety benchmark to evaluate whether an LLM can respond with the correct label and derive that label from the proper manufacturer constraints and rules. We extract these constraints from vendor datasheets and hardware manuals. Each scenario is defined by a decisive condition and a gold label (safe, hazardous, or cannot determine) before translating the scenario into natural language. We categorize the scenarios into violation and compliant cases, board-swap, near-boundary, and missing-information. We then evaluated the scenario on six open-weight LLMs under various conditions of rule access and token limitations. The finding shows board rules can improve accuracy, whereas limiting the token has a smaller impact. Even the best configuration remains unreliable on near-boundary and missing-information scenarios, and a correct label often rests on the inaccurate constraints.

Problem

• Large Language Models
• Electrical Safety
• Microcontroller Boards
• Safety Judgment
• Benchmark Evaluation

Innovation

• ElecSafety Benchmark
• Electrical Safety Judgment
• Large Language Models
• Microcontroller Boards
• Constraint Extraction