6 papers
Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework
Leonid Kondrashov, Hongrui Liu, JooYoung Park +14
Autonomous agents challenge conventional LLM serving by coupling repeated inference with persistent context and sandboxed tool execution. We present Aries, a full-stack experimenta…
Remora: Scale-out Deterministic Execution for Smart Contracts
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi +2
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution laye…
When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI
Javad Forough, Marios Kogias, Hamed Haddadi
Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocols such as MCP and A2A, introdu…
AgenTEE: Confidential LLM Agent Execution on Edge Devices
Sina Abdollahi, Mohammad M Maheri, Javad Forough +5
Large Language Model (LLM) agents provide powerful automation capabilities, but they also create a substantially broader attack surface than traditional applications due to their t…
Sharing is caring: Attestable and Trusted Workflows out of Distrustful Components
Amir Al Sadi, Sina Abdollahi, Adrien Ghosn +2
Confidential computing protects data in use within Trusted Execution Environments (TEEs), but current TEEs provide little support for secure communication between components. As a…
An Early Experience with Confidential Computing Architecture for On-Device Model Protection
Sina Abdollahi, Mohammad Maheri, Sandra Siby +2
Deploying machine learning (ML) models on user devices can improve privacy (by keeping data local) and reduce inference latency. Trusted Execution Environments (TEEs) are a practic…