# MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime

arXiv: https://arxiv.org/abs/2302.01452 · HTML: https://mlsanity.com/paper/2302.01452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2023.

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

- [Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation](https://mlsanity.com/paper/2604.07321/) (2026)

## Abstract (arXiv)

Many solutions have been proposed to curb unexpected behavior of automation apps installed on programmable IoT platforms by enforcing safety policies at runtime. However, all prior work addresses a weaker version of the actual problem due to a simpler, unrealistic threat model. These solutions are not general enough as they are heavily dependent on the installed apps and catered to specific IoT platforms. Here, we address a stronger version of the problem via a realistic threat model, where (i) undesired cyber actions can come from not only automation platform backends (e.g., SmartThings) but also close-sourced third-party services (e.g., IFTTT), and (ii) physical actions (e.g., user interactions) on devices can move the IoT system to an undesirable state. We propose a runtime mechanism, dubbed Maverick, which employs an app-independent, platform-agnostic mediator to enforce policies against all undesired cyber actions and applies corrective-actions to bring the IoT system back to a safe state from an unsafe state transition. Maverick is equipped with a policy language capable of expressing rich temporal invariants and an automated toolchain that includes a policy synthesizer and a policy analyzer for user assistance. We implemented Maverick in a prototype and showed its efficacy in both physical and virtual testbeds, incurring minimal overhead.