# SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models

arXiv: https://arxiv.org/abs/2607.06748 · HTML: https://mlsanity.com/paper/2607.06748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DocCGen: Document-based Controlled Code Generation](https://mlsanity.com/paper/2406.11925/) (2024)
- [Can LLMs Write CI? A Study on Automatic Generation of GitHub Actions Configurations](https://mlsanity.com/paper/2507.17165/) (2025)
- [TAPFixer: Automatic Detection and Repair of Home Automation Vulnerabilities based on Negated-property Reasoning](https://mlsanity.com/paper/2407.09095/) (2024)
- [Automation Configuration in Smart Home Systems: Challenges and Opportunities](https://mlsanity.com/paper/2408.04755/) (2024)
- [Cracking IoT Security: Can LLMs Outsmart Static Analysis Tools?](https://mlsanity.com/paper/2601.00559/) (2026)

## Abstract (arXiv)

Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks. Existing YAML validators, static analysis tools, and general-purpose large language models offer limited support for end-to-end diagnosis and repair because they lack domain-specific understanding and validated correction workflows. This paper presents SmartHomeSecure, a prototype for automated detection and repair of Home Assistant configuration errors using lightweight program analysis and constraint-guided large language model generation. SmartHomeSecure parses YAML files, detects syntactic and common semantic errors, normalizes error context, applies deterministic auto-fixes for routine defects, and constructs constrained prompts that guide LLMs toward minimal and structurally valid repairs. The system is implemented as a modular web application with four layers: UI Shell, Feature Orchestrator, Domain Engine, and Integration Layer. Its repair pipeline was evaluated on 100 real-world Home Assistant YAML files with manually injected errors across five categories: syntax/parsing, indentation, mapping, sequence, and scalar quoting errors. Four models were tested: gpt-oss-20b, gpt-oss-120b, llama-3.1-8b, and llama-3.3-70b. Results show that three models achieved 100% error detection accuracy, with repair success rates ranging from 87% to 93%. Manual verification found no hallucinated or incorrect repairs among successful outputs. These findings suggest that combining domain-aware program analysis with constrained generative AI is a feasible approach for improving the reliability and usability of smart home configuration repair.