# TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems

arXiv: https://arxiv.org/abs/2411.07114 · HTML: https://mlsanity.com/paper/2411.07114/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 131 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 23 of 23)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://mlsanity.com/paper/2101.05930/) (2021)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Adversarial Unlearning of Backdoors via Implicit Hypergradient](https://mlsanity.com/paper/2110.03735/) (2021)
- [PRADA: Protecting against DNN Model Stealing Attacks](https://mlsanity.com/paper/1805.02628/) (2018)
- [Rethinking the Trigger of Backdoor Attack](https://mlsanity.com/paper/2004.04692/) (2020)
- [Trigger Hunting with a Topological Prior for Trojan Detection](https://mlsanity.com/paper/2110.08335/) (2021)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses](https://mlsanity.com/paper/2103.14739/) (2021)
- [WaNet -- Imperceptible Warping-based Backdoor Attack](https://mlsanity.com/paper/2102.10369/) (2021)
- [Machine Learning Sensors](https://mlsanity.com/paper/2206.03266/) (2022)
- [Benchmarking Adversarial Robustness of Compressed Deep Learning Models](https://mlsanity.com/paper/2308.08160/) (2023)
- [Materiality and Risk in the Age of Pervasive AI Sensors](https://mlsanity.com/paper/2402.11183/) (2024)
- [Enhancing TinyML Security: Study of Adversarial Attack Transferability](https://mlsanity.com/paper/2407.11599/) (2024)
- [Circumventing Backdoor Defenses That Are Based on Latent Separability](https://mlsanity.com/paper/2205.13613/) (2022)
- [Datasheets for Machine Learning Sensors](https://mlsanity.com/paper/2306.08848/) (2023)
- [An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks](https://mlsanity.com/paper/2403.09441/) (2024)

## Built on by (most cited first, 3 of 3)

- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [Laplace-Bridged Randomized Smoothing for Fast Certified Robustness](https://mlsanity.com/paper/2604.24993/) (2026)
- [SoK: Attack and Defense Landscape of Mobile On-device AI Systems](https://mlsanity.com/paper/2607.00362/) (2026)

## Abstract (arXiv)

Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities two to three orders of magnitude smaller than conventional systems, make traditional software and hardware security solutions impractical. The physical accessibility of these devices exacerbates their susceptibility to side-channel attacks and information leakage. Additionally, TinyML models pose security risks, with weights potentially encoding sensitive data and query interfaces that can be exploited. This paper offers the first thorough survey of TinyML security threats. We present a device taxonomy that differentiates between IoT, EdgeML, and TinyML, highlighting vulnerabilities unique to TinyML. We list various attack vectors, assess their threat levels using the Common Vulnerability Scoring System, and evaluate both existing and possible defenses. Our analysis identifies where traditional security measures are adequate and where solutions tailored to TinyML are essential. Our results underscore the pressing need for specialized security solutions in TinyML to ensure robust and secure edge computing applications. We aim to inform the research community and inspire innovative approaches to protecting this rapidly evolving and critical field.