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.
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: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: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: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...
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.
The paper introduces a fault‑tolerant budget conservation framework for distributed multi‑agent delegation, where budgets are represented as exclusive escrow credits that traverse a delegation DAG. It details how each branch converts credit into a reservation tied to lineage, epoch, and idempotency, persists a signed dispatch permit, and ensures that uncertain effects remain charged until settlement or retirement. The authors prove properties such as ownership partition, ledger conservation, and at‑most‑once settlement, and validate the mechanism through TLA+ checks, a JavaScript explorer, and crash‑injected SQLite experiments.
AcquireBound is a runtime authorization framework that ensures AI agents can safely acquire and activate resources such as compute, credentials, and services. It quarantines acquired outputs, resolves their capabilities through authenticated evidence, and activates them only after verifying a manifest, provenance, and relational constraints. The system demonstrates strong safety properties, passing extensive benign and unsafe trace tests across multiple resource classes.
The paper introduces "authorization succession," a framework that preserves authority across self‑modifying AI agent populations that can replace, fork, or roll back. It defines a protocol binding each generation to a manifest, root, unique parent, lineage, and population sequence, and establishes invariants that control root‑lifetime consumption and population exposure. The authors prove properties such as population‑safe succession, fork conservation, and rollback non‑reminting, and validate the approach with an executable evaluation covering 32 decisions and external adapters for two mutation systems.
The paper introduces the concept of Cognitive Serializability for autonomous AI agents, ensuring that mutations derived from dynamic inputs—such as database reads, evidence, policy, beliefs, and delegated authority—are committed in a serial, logically consistent order. It presents a framework called Trusted Cognitive Transaction (TCT) that combines immutable executable definitions, sealed envelopes, guard-first commits, and receipt-driven reconciliation to enforce serializability and prevent anomalies. Experimental results show that the prototype implementation incurs minimal overhead while eliminating injected anomalies.
arXiv:2606. 22504v1 Announce Type: cross Abstract: Coding agents often receive broad tool access for an entire task, even when a resource is needed only for one subgoal.
The paper investigates how AI agents can reliably recover from changes in authorization while executing tasks that involve external tools. It defines distinct obligations—checkpointing, permission to dispatch, evidence of external effects, and permission to reconcile—and evaluates them through a finite conformance study on 336 histories across multiple hosts, schedules, and provider contracts. The study demonstrates that all paired host cells produce identical outcomes and that various retry strategies (cached, revalidation, stable-key) successfully pass the majority of histories, while a component study of InfinityOS further isolates the impact of authority, contracts, and observations on recovery.