AgenTEE isolates LLM agent runtime, inference, and apps in independently attested cVMs on Arm-based edge devices, achieving under 5.15% overhead versus commodity OS deployments.
An Early Experience with Confidential Computing Architecture for On-Device Model Protection
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
CAEC adds confidential shared memory to Arm CCA, cutting inter-CVM communication cost by up to 209x versus encryption through hypervisor-visible memory while preserving isolation and adding attestable sharing.
A survey providing a taxonomy of TEE platforms, an agent-centric threat model, and open challenges for applying confidential computing to secure agentic AI systems.
citing papers explorer
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AgenTEE: Confidential LLM Agent Execution on Edge Devices
AgenTEE isolates LLM agent runtime, inference, and apps in independently attested cVMs on Arm-based edge devices, achieving under 5.15% overhead versus commodity OS deployments.
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Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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CAEC: Confidential, Attestable, and Efficient Inter-CVM Communication with Arm CCA
CAEC adds confidential shared memory to Arm CCA, cutting inter-CVM communication cost by up to 209x versus encryption through hypervisor-visible memory while preserving isolation and adding attestable sharing.
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When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI
A survey providing a taxonomy of TEE platforms, an agent-centric threat model, and open challenges for applying confidential computing to secure agentic AI systems.