the Trusted Execution Environment
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Related Articles from SNS
From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents
Announce Type: new Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents. These capabilities expand agent autonomy, but also make agent behavior harder to verify, debug, and audit. Final-answer accuracy alone cannot explain how an output was produced, which evidence supported each claim, whether tool calls were justified, how memory influenced later decisions, or...
Verifiable and Confidential DNN Inference on Low-End Edge Devices
Announce Type: new Abstract: Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially compromised edge system and enabling verifiable inference without incurring prohibitive overhead. Existing approaches either house partial models and inference software within trusted execution environments (TEEs), resulting in high cost and an application-dependent trusted computing base (TCB), or execute in...
Formal Verification of Secure Encrypted Virtualization
arXiv:2606.01381v1 Announce Type: new Abstract: Trusted execution environments (TEEs) provide a secure environment for data and code in use, ensuring that they are protected with respect to confidentiality and integrity. Virtual machine (VM)-based TEEs utilize virtualization technology to create isolated execution spaces that can support a complete operating system or specific applications. AMD secure encrypted virtualization (SEV) is a key technology used in confidential computing in the...
ODYSSEY: Reestablishing Confidentiality in Confidential Blockchain via Delegated Execution
arXiv:2606.04892v1 Announce Type: new Abstract: Confidential blockchains leveraging Trusted Execution Environments (TEEs) have garnered extensive attention for transaction confidentiality. In this paper, we first taxonomize two classes of attacks against confidential blockchains, i.e., execution-inference and execution-replay attacks, which exploit TEEs' long-lasting side-channel and state-continuity issues to compromise the confidentiality of existing consortium blockchains. Then, we...
TeeDAO: A Decentralized Autonomous Organization for Heterogeneous TEEs
Announce Type: new Abstract: Trusted Execution Environments (TEEs) have emerged as a critical technology for safeguarding sensitive data and ensuring code integrity in modern computing systems. However, relying on a single TEE implementation makes systems vulnerable to a central point of attack. Building distributed-trust systems leveraging heterogeneous TEEs helps disperse trust but still faces threats from centralized management and adaptive mobile adversaries.
DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning
arXiv:2606.04899v1 Announce Type: new Abstract: Trusted Execution Environments (TEEs)-aided federated learning protocols emerge as promising solutions to counter server-side adversaries and ensure the trustworthiness of the server. In this paper, we dissect existing protocols and demonstrate that server-side adversaries can still manipulate client selection and replay aggregation to compromise system robustness and privacy, by exploiting TEE limitations, i.e., state rollback and I/O...
Chimera: Protocol-Aware Recovery for Confidential BFT Consensus
arXiv:2606.09101v1 Announce Type: new Abstract: Trusted Execution Environments (TEEs) have enabled confidential Byzantine Fault-Tolerant (BFT) consensus systems with confidentiality and improved scalability. However, TEEs do not provide state continuity: during recovery, a compromised host can roll back a crashed enclave to a stale persistent state, significantly threatening both safety and availability. Existing defenses face a fundamental tradeoff: they either impose substantial overhead...
DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning
arXiv:2606.04899v2 Announce Type: replace Abstract: Trusted Execution Environments (TEEs)-aided federated learning protocols emerge as promising solutions to counter server-side adversaries and ensure the trustworthiness of the server. In this paper, we dissect existing protocols and demonstrate that server-side adversaries can still manipulate client selection and replay aggregation to compromise system robustness and privacy, by exploiting TEE limitations, i.e., state rollback and I/O...
VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents
arXiv:2606.05395v1 Announce Type: new Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior. We argue that, while foundation models have collapsed the cost of creating these skills, the cost of trusting them has not. Existing skill-evolution loops refine skills through execution feedback, unit tests, environment reward, or LLM self-critique, but these signals provide only trace-level...
Show HN: Nucleus – A security-hardened, Nix-native container runtime
Extremely lightweight, security-hardened, declarative container runtime for agents and production services Nucleus is a minimalist container runtime for Linux. It provides isolated execution environments using Linux kernel primitives without the overhead of traditional container runtimes. For production services, it is designed around a fully declarative model: Nix builds the root filesystem, the NixOS module declares the service, and Nucleus mounts a pinned, reproducible closure at runtime.