# LLM-Driven Auto Configuration for Transient IoT Device Collaboration

arXiv: https://arxiv.org/abs/2507.03064 · HTML: https://mlsanity.com/paper/2507.03064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 92 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Synthesizing Access Control Policies using Large Language Models](https://mlsanity.com/paper/2503.11573/) (2025)
- [Say What You Mean: Natural Language Access Control with Large Language Models for Internet of Things](https://mlsanity.com/paper/2505.23835/) (2025)
- [SoK: Access Control Policy Generation from High-level Natural Language Requirements](https://mlsanity.com/paper/2310.03292/) (2023)
- [LMN: A Tool for Generating Machine Enforceable Policies from Natural Language Access Control Rules using LLMs](https://mlsanity.com/paper/2502.12460/) (2025)
- [Capability-based access control for multi-tenant systems using OAuth 2.0 and Verifiable Credentials](https://mlsanity.com/paper/2104.11515/) (2021)
- [MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices](https://mlsanity.com/paper/1310.4038/) (2013)

## Abstract (arXiv)

Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations between users and their devices. However, enabling such collaboration becomes challenging when transient devices need to interact with host devices in temporarily visited environments. In such cases, fine-grained access control policies are necessary to ensure secure interactions; however, manually implementing them is often impractical for non-expert users. Moreover, at run-time, the system must automatically configure the devices and enforce such fine-grained access control rules. Additionally, the system must address the heterogeneity of devices. In this paper, we present CollabIoT, a system that enables secure and seamless device collaboration in transient IoT environments. CollabIoT employs a Large language Model (LLM)-driven approach to convert users' high-level intents to fine-grained access control policies. To support secure and seamless device collaboration, CollabIoT adopts capability-based access control for authorization and uses lightweight proxies for policy enforcement, providing hardware-independent abstractions. We implement a prototype of CollabIoT's policy generation and auto configuration pipelines and evaluate its efficacy on an IoT testbed and in large-scale emulated environments. We show that our LLM-based policy generation pipeline is able to generate functional and correct policies with 100% accuracy. At runtime, our evaluation shows that our system configures new devices in ~150 ms, and our proxy-based data plane incurs network overheads of up to 2 ms and access control overheads up to 0.3 ms.