3 papers
cs.CR2026
Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis
Jacob Huckelberry, Andrea Mattia Garavagno, Yuke Zhang +3
Most TinyML hardware accelerators focus on supporting Quantized Neural Networks (QNNs) to meet stringent constraints on power consumption and size. Despite this, the security aspec…
cs.CR2025
Guillotine: Hypervisors for Isolating Malicious AIs
James Mickens, Sarah Radway, Ravi Netravali
As AI models become more embedded in critical sectors like finance, healthcare, and the military, their inscrutable behavior poses ever-greater risks to society. To mitigate this r…
cs.CR2024
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Jacob Huckelberry, Yuke Zhang, Allison Sansone +3
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique securit…