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Authenticated delegation and authorized ai agents
23 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
23
Pith papers citing it
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abstract
The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know whom AI agents act on behalf of and guide their use appropriately, protecting online spaces while unlocking the value of task delegation to autonomous agents. We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents, where human users can securely delegate and restrict the permissions and scope of agents while maintaining clear chains of accountability. This framework builds on existing identification and access management protocols, extending OAuth 2.0 and OpenID Connect with agent-specific credentials and metadata, maintaining compatibility with established authentication and web infrastructure. Further, we propose a framework for translating flexible, natural language permissions into auditable access control configurations, enabling robust scoping of AI agent capabilities across diverse interaction modalities. Taken together, this practical approach facilitates immediate deployment of AI agents while addressing key security and accountability concerns, working toward ensuring agentic AI systems perform only appropriate actions and providing a tool for digital service providers to enable AI agent interactions without risking harm from scalable interaction.
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SentinelAgent defines seven properties for verifiable delegation chains in multi-agent AI systems and reports a protocol achieving 100% true positive rate at 0% false positives on a 516-scenario benchmark while using TLA+ to verify six deterministic properties.
VLMs preserve linearly separable visual magnitudes and can compare them, yet collapse at symbolic mapping because visual and textual number spaces remain fractured and disjoint.
Coding agents struggle to infer least-privilege file permissions by omitting needed accesses while granting unused or sensitive ones, but Sufficiency-Tightness Decomposition improves sensitive-task success by up to 15.8% and reduces attacks.
Identifies concrete attacks from a malicious Provider on SAGA and proposes SAGA-BFT, SAGA-MON, SAGA-AUD, and SAGA-HYB mitigations offering different security-performance trade-offs.
ClawNet digitizes human collaborative relationships into a network of identity-governed AI agents that collaborate on behalf of their owners through a central orchestrator enforcing binding and verification.
Execution-security research for AI coding agents is fragmented across 17 mechanism categories with five unaddressed cross-cutting gaps, including missing head-to-head isolation-vs-capability evaluation and untested real-world denylist fragility.
TokenWall mediates persistent-agent security by auditing source–sink token flows with a local small model and selective large-model escalation, cutting CIK-Bench attack success to 12.5% at low benign latency.
The web's anti-bot regime should be replaced by a framework that presumptively lets user-authorized AI agents act for their principals, requires platforms to disclose access policies, and permits agent blocking only when proportionate to concrete harms.
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
Clarus is a four-layer collaboration infrastructure with a project-agent-resource model that reformulates research as an open, traceable, multi-participant process.
AgenticRei applies the Rei deontic framework in OWL for runtime governance of agentic AI, handling obligations and waivers that standard policy engines lack.
Introduces a compositional governance framework defining delegation types, resource scope attenuation, and an overlay operator for agentic AI authorization policies.
Safety constraints in LLM-based multi-agent systems commonly weaken during execution through memory, communication, and tool use, requiring them to be maintained as explicit state rather than asserted once.
MAGIQ introduces a post-quantum governance system for multi-agent AI that supports policy budgets, session enforcement, message attribution, and UC-based security proofs while comparing overhead to SAGA.
Defines consent chain degradation in multi-robot systems and proposes the CoRVE three-layer governance architecture plus regulatory gap analysis.
AITH provides a continuous delegation protocol with ML-DSA signatures, a six-check boundary engine, push revocation, and Tamarin-verified security theorems for human-AI trust.
Proposes a portable authorization model for autonomous agents featuring issuer-authored payloads, typed constraint algebra, delegation attenuation, and fail-closed semantics to enable consistent cross-boundary enforcement.
The paper proposes a bottom-up framework for safe agentic AI systems that treats each component as a dual-use interface where added capabilities also expand attack surfaces across single agents, multi-agent systems, and interoperable ecosystems.
LLM agents enable universal interoperability by serving as automatic translators and adapters between proprietary digital services.
The Redpanda Agentic Data Plane uses out-of-band metadata channels to enforce data scoping, action constraints, and tamper-proof auditing on autonomous AI agents.
citing papers explorer
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ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation
ClawNet digitizes human collaborative relationships into a network of identity-governed AI agents that collaborate on behalf of their owners through a central orchestrator enforcing binding and verification.
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Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration
Clarus is a four-layer collaboration infrastructure with a project-agent-resource model that reformulates research as an open, traceable, multi-participant process.
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Deontic Policies for Runtime Governance of Agentic AI Systems
AgenticRei applies the Rei deontic framework in OWL for runtime governance of agentic AI, handling obligations and waivers that standard policy engines lack.
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Overlaying Governance: A Compositional Authorization Framework for Delegation and Scope in Agentic AI
Introduces a compositional governance framework defining delegation types, resource scope attenuation, and an overlay operator for agentic AI authorization policies.
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The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane
The Redpanda Agentic Data Plane uses out-of-band metadata channels to enforce data scoping, action constraints, and tamper-proof auditing on autonomous AI agents.
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