The paper introduces AIP, a novel protocol using Invocation-Bound Capability Tokens (IBCTs) to provide verifiable identity and secure delegation across Model Context Protocol (MCP) and Agent-to-Agent…

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…

AITH is a novel post-quantum continuous delegation protocol designed to securely establish, manage, and revoke trust relationships between humans and autonomous AI agents operating in variable environ…

The paper proposes the Secret-Use Delegation Protocol (SUDP) to solve the Agent Secret Use (ASU) problem, ensuring that autonomous agents can perform user-authorized operations without gaining reusabl…

The paper introduces the Sovereign Context Protocol (SCP), an open-source, attribution-aware data access layer designed to standardize how Large Language Models (LLMs) connect to and track usage of hu…

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 AgentDID, a decentralized framework using DIDs and verifiable credentials to provide trustless identity authentication and dynamic state verification for autonomous, self-managed AI…

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 introduces Heartbeat-Bound Hierarchical Credentials (HBHC), a cryptographic protocol that revokes AI agent credentials locally and deterministically when the parent agent loses liveness, sig…

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 systematizes the interaction between autonomous AI agents and blockchain platforms using a bidirectional trust framework, identifying significant gaps in current standards and proposing a ta…

The paper proposes Anumati, a formal consent model that moves beyond simple proof of acceptance to provide a verifiable, per-action proof of adherence to evolving policies in autonomous agent communic…

The paper introduces MCPSHIELD, a comprehensive formal security framework that systematically characterizes and provides a defense-in-depth architecture for the rapidly adopted but insecure Model Cont…

The paper introduces a comprehensive security framework, AgentRFC, to systematically analyze and test the security conformance of various AI agent protocols, identifying critical design gaps, especial…

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…

The paper introduces MAGIQ, a novel, quantum-resistant framework designed to securely define and enforce communication and access-control policies within multi-agent AI systems.

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper proposes AuthGraph, a dual-graph defense framework that structurally compares information provenance (what data was used) against a clean authorization baseline to detect fine-grained, param…

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…