# ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2502.09896 · HTML: https://mlsanity.com/paper/2502.09896/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 17 of 17)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [Kaleido-BERT: Vision-Language Pre-training on Fashion Domain](https://mlsanity.com/paper/2103.16110/) (2021)
- [Penetrative AI: Making LLMs Comprehend the Physical World](https://mlsanity.com/paper/2310.09605/) (2023)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligence](https://mlsanity.com/paper/2405.03446/) (2024)
- [IoT-LM: Large Multisensory Language Models for the Internet of Things](https://mlsanity.com/paper/2407.09801/) (2024)
- [IoT Device Labeling Using Large Language Models](https://mlsanity.com/paper/2403.01586/) (2024)
- [Towards Cyber Threat Intelligence for the IoT](https://mlsanity.com/paper/2406.13543/) (2024)

## Built on by (most cited first, 4 of 4)

- [Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought](https://mlsanity.com/paper/2505.06307/) (2025)
- [Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes](https://mlsanity.com/paper/2603.16207/) (2026)
- [Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs](https://mlsanity.com/paper/2509.19485/) (2025)
- [VeriChat: An Agentic Conversational AI Assistant for Hardware Security Verification](https://mlsanity.com/paper/2607.01668/) (2026)

## Abstract (arXiv)

Internet of Things (IoT) has gained widespread popularity, revolutionizing industries and daily life. However, it has also emerged as a prime target for attacks. Numerous efforts have been made to improve IoT security, and substantial IoT security and threat information, such as datasets and reports, have been developed. However, existing research often falls short in leveraging these insights to assist or guide users in harnessing IoT security practices in a clear and actionable way. In this paper, we propose ChatIoT, a large language model (LLM)-based IoT security assistant designed to disseminate IoT security and threat intelligence. By leveraging the versatile property of retrieval-augmented generation (RAG), ChatIoT successfully integrates the advanced language understanding and reasoning capabilities of LLM with fast-evolving IoT security information. Moreover, we develop an end-to-end data processing toolkit to handle heterogeneous datasets. This toolkit converts datasets of various formats into retrievable documents and optimizes chunking strategies for efficient retrieval. Additionally, we define a set of common use case specifications to guide the LLM in generating answers aligned with users' specific needs and expertise levels. Finally, we implement a prototype of ChatIoT and conduct extensive experiments with different LLMs, such as LLaMA3, LLaMA3.1, and GPT-4o. Experimental evaluations demonstrate that ChatIoT can generate more reliable, relevant, and technical in-depth answers for most use cases. When evaluating the answers with LLaMA3:70B, ChatIoT improves the above metrics by over 10% on average, particularly in relevance and technicality, compared to using LLMs alone.