arXiv AI
AID-Guard: Stateful Authorization for Delegated Agent Effects
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arXiv AI
Jul 14
Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents
arXiv:2607. 10487v1 Announce Type: cross Abstract: LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result.
arXiv AI
2d ago
Cross-Interface Authorization for AI Computer-Use Agents
The paper introduces a protocol for cross‑interface authorization of AI computer‑use agents, ensuring that policy decisions remain consistent across graphical interfaces, command lines, APIs, scripts, and Model Context Protocol servers. It defines a profile‑bounded system that enumerates all effect‑bearing routes, normalizes requests to canonical effects, issues prepare tickets, and re‑verifies all relevant state at commit time, requiring a sink receipt for a committed result. Extensive testing across two runtime mappings produced thousands of traces, with rigorous checks confirming no semantic regressions, mutant rejections, and correct outcome matching.
arXiv AI
Sep 21
Authorization Revocation for Long-Running AI Agents: Root-Scoped Quiescence under Delegation and Asynchronous Execution
arXiv:2609.21284v1 Announce Type: cross Abstract: Long-running AI agents outlive initiating processes through credentials, delegated tasks, queues, callbacks, reservations, and provider-side operatio...
arXiv AI
Sep 16
Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems
arXiv:2609.16313v1 Announce Type: cross Abstract: In agentic distributed systems, an agent may be authorized to mutate external infrastructure while lacking evidence that the mutation is ready to exe...
arXiv AI
Sep 21
Runtime Authorization for Resources Acquired by AI Agents
arXiv:2609.14744v2 Announce Type: replace Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, bud...
arXiv AI
Oct 16
Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization
The paper "Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization" examines how AI agents seek approval before performing actions such as sending money or deleting files. It identifies twelve attack vectors on the approval process and evaluates them against several protocols, finding that many agent runtimes lack verifiable approval records and exhibit binding weaknesses. The authors propose a defense called ASU (Agent Step-Up), which renders a canonical approval card, requires a phone passkey, and enforces single-use execution, demonstrating strong protection against substitution, replay, and grant-stretching attacks in extensive simulations.