# Generative AI for Internet of Things Security: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2502.08886 · HTML: https://mlsanity.com/paper/2502.08886/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 75 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 28 of 28)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [PentestGPT: An LLM-empowered Automatic Penetration Testing Tool](https://mlsanity.com/paper/2308.06782/) (2023)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software](https://mlsanity.com/paper/2107.08760/) (2021)
- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [Large Language Models for Supply Chain Optimization](https://mlsanity.com/paper/2307.03875/) (2023)
- [LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning](https://mlsanity.com/paper/2401.16185/) (2024)
- [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)
- [A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions](https://mlsanity.com/paper/2405.14487/) (2024)
- [LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks](https://mlsanity.com/paper/2310.11409/) (2023)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [LLM for SoC Security: A Paradigm Shift](https://mlsanity.com/paper/2310.06046/) (2023)
- [LLMs in Web Development: Evaluating LLM-Generated PHP Code Unveiling Vulnerabilities and Limitations](https://mlsanity.com/paper/2404.14459/) (2024)
- [LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward](https://mlsanity.com/paper/2401.03374/) (2024)
- [Contextual LSTM (CLSTM) models for Large scale NLP tasks](https://mlsanity.com/paper/1602.06291/) (2016)
- [LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation](https://mlsanity.com/paper/2310.04963/) (2023)
- [Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing](https://mlsanity.com/paper/2403.03897/) (2024)
- [DefectHunter: A Novel LLM-Driven Boosted-Conformer-based Code Vulnerability Detection Mechanism](https://mlsanity.com/paper/2309.15324/) (2023)
- [Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense](https://mlsanity.com/paper/2303.12132/) (2023)
- [Lateral Phishing With Large Language Models: A Large Organization Comparative Study](https://mlsanity.com/paper/2401.09727/) (2024)
- [Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4](https://mlsanity.com/paper/2309.16422/) (2023)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Analysis and prevention of AI-based phishing email attacks](https://mlsanity.com/paper/2405.05435/) (2024)
- [The World of Generative AI: Deepfakes and Large Language Models](https://mlsanity.com/paper/2402.04373/) (2024)
- [A hybrid LLM workflow can help identify user privilege related variables in programs of any size](https://mlsanity.com/paper/2403.15723/) (2024)

## Built on by (most cited first, 1 of 1)

- [Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought](https://mlsanity.com/paper/2505.06307/) (2025)

## Abstract (arXiv)

As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT. Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further. Through the compilation, synthesis, and analysis of the latest advancements in GenAI technologies applied to IoT, this paper not only introduces fresh insights into the field, but also lays the groundwork for future research directions. It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations. Accompanied with three case studies, we provide a comprehensive overview of the progress and future prospects of GenAI applications in IoT security. This study serves as a foundational resource to improve IoT security through the innovative application of GenAI, thus contributing to the broader discourse on IoT security and technology integration.