The paper proposes a federated formal verification architecture that treats verification as a polyglot proof system, successfully validating it on complex production subsystems like a Raft consensus m…

The paper introduces MolTrust, a production-deployed trust infrastructure built on W3C standards (VCs and DIDs) that provides a verifiable, multi-layered authorization framework for autonomous AI agen…

The paper introduces a certified purity architecture that strengthens governance in cognitive workflow systems by replacing insufficient runtime checks with cryptographically attested structural guara…

The paper proposes Sovereign 2.0, a control-plane-centric model redefining cloud sovereignty as enforceable governance authority and operational control, rather than mere data location.

The paper proposes a taxonomy of 20 hardware-level governance mechanisms for AI compute, finding that the most critical mechanisms needed for international treaty verification are currently the least…

The paper proposes Proof-Carrying Agent Actions (PCAA), a runtime-neutral governance model that uses action certificates to consistently track and authorize high-risk actions across diverse and hetero…

The paper proposes a Semantic Gateway and a Zero-Trust security model to formally validate and secure autonomous AI agents operating in enterprise systems, achieving a 100% discovery rate of unauthori…

This paper proposes and evaluates the integration of Federated Learning and blockchain technology over cloud-edge infrastructure to enhance data privacy and security for decentralized AI applications.

The paper proposes a compositional governance framework to provide richer, dynamic authorization semantics necessary for governing autonomous agentic AI systems, moving beyond traditional static IAM m…

The paper proposes a bottom-up, system-oriented approach to formally verify authorization algorithms for large-scale, Byzantine fault-tolerant local-first systems, using Rust and the Verus framework.

Branch Landing (BRL) is a novel forward-edge CFI framework for RISC-V that uses Bloom filters to overcome the source authorization limitations of existing hardware CFI, achieving low overhead for fine…

TeeDAO introduces a novel three-layer framework that autonomously organizes and manages multiple heterogeneous Trusted Execution Environments (TEEs) to provide robust, distributed-trust systems with h…

The paper proposes a trust-boundary architecture using Lean 4 to verify the deterministic structured computations surrounding LLM pipelines, providing verifiable certificates for high-stakes deploymen…

The paper proposes extit{codename}, an architecture that enforces verifiable workflows across untrusted networks by combining hardware-isolated control and kernel-resident data planes, achieving low-…

The paper proposes the Energetic Paradigm, a model-agnostic architectural framework that allows states to maintain decision sovereignty and control over military AI systems, even when using proprietar…

The paper proposes DIST-FL, a distributed system using multiple TEEs and an append-only ledger to enhance the security and robustness of federated learning aggregation against server-side adversaries.

This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…

Space Fabric introduces a novel satellite-based Trusted Execution Architecture (TEE) that establishes trust for orbital computing by generating cryptographic secrets and binding workload execution to…

SentinelAgent introduces a formal framework, the Intent-Preserving Delegation Protocol (IPDP), to secure federal multi-agent AI systems by verifying complex delegation chains against seven properties,…

The paper introduces a new benchmark and decomposition method, Sufficiency-Tightness Decomposition, demonstrating that current coding agents struggle to accurately infer least-privilege authorization,…