Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis

Computer Science > Cryptography and Security [Submitted on 12 Jun 2026] Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis Jacob Huckelberry, Andrea Mattia Garavagno, Yuke Zhang, Peter A. Beerel, James Mickens, Vijay Janapa Reddi Most TinyML hardware accelerators focus on supporting Quantized Neural Networks (QNNs) to meet stringent constraints on power consumption and size. Despite this, the security aspects of quantization within TinyML hardware remain largely unexplored. Although previous studies indicate that QNNs demonstrate similar or enhanced robustness when compared to full-precision Deep Neural Networks (DNNs) against typical evasion attacks, no attack strategies tailored specifically for TinyML hardware have been proposed yet. This paper addresses this shortfall by demonstrating how a two-step attack pipeline can surpass the current state-of-the-art in the QNN context and shows the need for more hardware-aware security research. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.14427 [cs.CR]   (or arXiv:2606.14427v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.14427 Focus to learn more Related DOI: https://doi.org/10.1109/MM.2026.3666128 Focus to learn more Submission history From: Andrea Mattia Garavagno [view email] [v1] Fri, 12 Jun 2026 13:05:34 UTC (892 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)