The paper presents a systematic security analysis of Google’s Agent Payments Protocol (AP2) version 2.0, focusing on its roles, transaction lifecycle, and deployment architectures. It identifies 48 threats across five attack families, scores them with the AIVSS, and demonstrates eight high‑risk threats with proof‑of‑concept attacks and mitigations. The study also introduces a deployment‑aware scanner to map threats to various checks, showing that signed mandates alone cannot guarantee user intent when pre‑authorization context is manipulated.
By Avital Aviv, Parth A. Gandh, Ron Bitton, Asaf Shabtai
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...
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)
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.
By Genliang Zhu
The paper surveys how decentralized agent economies combine independently designed protocols into a single task workflow, often leading to correct steps that still yield incorrect outcomes. It categorizes security and economic requirements into 17 property families across six stages, evaluates 12 systems and standards, and introduces the guarantee‑closure criterion to assess whether early guarantees persist to later stages. Empirical analysis of 840 executions and 11,648 checks reveals frequent failures between verification and settlement, highlighting gaps between recorded approvals and actual task conformance.
By Rui Sun, Xihan Xiong, Qin Wang, Fei Gao, Zelin Li, Zehua Cheng, Jiahao Sun, Zhipeng Wang
arXiv:2607. 21325v1 Announce Type: cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
arXiv:2607. 21325v2 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
arXiv:2608. 14074v1 Announce Type: new Abstract: AI agents increasingly act on external systems through standardized tool-calling protocols such as the Model Context Protocol (MCP), yet no infrastructure layer constrains their actions to what a principal has verifiably authorized: authorization logic lives in application code, is neither signed nor independently auditable, and the resulting logs lack evidentiary value.
By Giovanni Racioppi
arXiv:2606. 04104v1 Announce Type: cross Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways, and observer-only integrations.
By Zexun Wang
arXiv:2607. 21325v3 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
By M. Llamb\'i-Morillas, D. Fern\'andez-Fern\'andez
The paper introduces Verifiable Agent Insurance (VAI), a framework that lets AI agent providers commit to policy compliance through smart contracts and escrow funds on a blockchain. When a user detects a policy violation, a verifier reviews execution traces logged by the agent harness; if the violation is confirmed, the escrow is paid to the user. Experiments on three agent benchmarks show VAI detects 42.2% of violations, rising to 83.0% when agents emit explicit structured records.
By Cheng Hua, Taro Tsuchiya, Nicolas Christin, Giulia Fanti
The paper introduces CONTINUITY, a framework that ensures secure composition of large language model (LLM) agent controls by using assume‑guarantee contracts and authenticated security contexts. It models each component with signed root grants, provenance commitments, and other mechanisms to carry security context across transitions, formalizing end‑to‑end consequence integrity. A reference verifier and fault‑injection suite demonstrate that the full configuration prevents harmful effects while completing all benign tasks and correctly escalating ambiguous cases.
By Chris Zheng, Geng Yang
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.
By Genliang Zhu, Chu Wang