PAL*M is a property attestation framework for large generative models that combines confidential virtual machines, security-aware GPUs, and incremental multiset hashing to achieve low-overhead integrity tracking with formal security guarantees.
Confidential LLM inference: Performance and cost across CPU and GPU TEEs
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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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PAL*M: Property Attestation for Large Generative Models
PAL*M is a property attestation framework for large generative models that combines confidential virtual machines, security-aware GPUs, and incremental multiset hashing to achieve low-overhead integrity tracking with formal security guarantees.
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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.