SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models
arXiv:2607.06748v1 Announce Type: cross Abstract: 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 detect
The rapid advancement of large language models (LLMs) provides the necessary capabilities for nuanced semantic understanding and automated correction of complex configuration errors, which is critical as smart home systems become ubiquitous.
This development addresses a critical vulnerability in smart home automation platforms, enhancing reliability and security while simultaneously demonstrating a practical application of AI agents for configuration management.
LLMs can now move beyond content generation to reliably solve domain-specific, logic-based problems in critical infrastructure, reducing manual intervention and improving system stability.
- · Smart home platform providers
- · Smart home users
- · AI agent developers
- · Cybersecurity sector
- · Traditional static analysis tools
- · Manual configuration debugging services
Increased reliability and security of smart home systems due to automated error detection and repair.
Expansion of AI agent capabilities into other complex configuration and automation domains beyond smart homes.
Reduced friction in deploying and managing AI-driven systems across various industries due to more robust self-correction mechanisms.
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Read at arXiv cs.AI