An empirical security study shows confused deputy attacks are practical on most edge AI accelerators via a new LLM-assisted analysis framework, with vendor-confirmed impact on over 100 million devices.
Vmud: Detecting recurring vulnerabilities with multiple fixing functions via function selection and semantic equivalent statement matching
3 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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A 3.4M-parameter foundation model pre-trained on step-count data alone achieves best AUROC on 20 of 21 health risk prediction tasks across multiple devices, regions, and diseases.
Measurement of 688 AI infra repositories shows frequent overlapping vulnerable patterns, and INFRASCOPE detects over 20 variants including 11 acknowledged and 4 with new CVEs.
citing papers explorer
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Speed Kills: Exploring Confused Deputy Attacks Through Edge AI Accelerators
An empirical security study shows confused deputy attacks are practical on most edge AI accelerators via a new LLM-assisted analysis framework, with vendor-confirmed impact on over 100 million devices.
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Physical activities enable scalable foundation modelling for broad-spectrum health prediction
A 3.4M-parameter foundation model pre-trained on step-count data alone achieves best AUROC on 20 of 21 health risk prediction tasks across multiple devices, regions, and diseases.
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Hunting Vulnerability Variants in AI Infra: Measurement and Reference-Driven Detection
Measurement of 688 AI infra repositories shows frequent overlapping vulnerable patterns, and INFRASCOPE detects over 20 variants including 11 acknowledged and 4 with new CVEs.