Same verdict, different culprit: which control decided? Attribution and enforcement points for AI agent payments

AI agents can now pay for things on their own, over settlement rails that do not govern the payer. Several systems published in 2026 put a deterministic check in front of an agent's action. We do not propose another one. We argue that this category, which already has independent implementations, is missing one property, and we work out what it costs to require it. When controls can only make a verdict stricter, the final verdict is the strictest one any control proposed, so the order the controls run in does not change it. The record of which control decided is a different matter. The last control to tighten the verdict is the first one in the order to reach the maximum, so attribution depends on order exactly when two or more controls reach that maximum. That happens all the time: any two outright refusals tie at the top. A specification that requires attribution therefore has to fix the evaluation order, and has to declare which controls win a tie. We make three small contributions. Attribution as a required output that a third party can score from machine-readable vectors, on approvals as well as refusals. A disclosure rule we call *bounded, not eliminated*, which obliges an implementation to state what its control leaves behind. And a benchmark scoring rule, `DEFENDED_BY_ACCIDENT`, which counts an attack refused by a control that was not aiming at it as a finding rather than a pass. The paper also maps where such a control can sit on an x402 payment (a pre-tool hook, the client's spend controls, a custodial signer, an agent gateway, the seller, the facilitator) and what each position can and cannot see. A pre-tool hook decides before the seller has named a price. A gateway sees the signed amount but cannot revoke it. No position holds an independent view of what the price should have been. The reference implementation scores 17 of 20 attacks defended, one by accident and two open by design. The instrument has no benign scenarios, so an implementation that refused everything would score perfectly; we found that defect by reading a competitor's design. Three prior-art surveys removed three larger claims while we wrote this, and Section 3 reports each removal.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-24
DOI
https://doi.org/10.5281/zenodo.23610076
Primary Topic
Access Control and Trust
Type
preprint
Controls
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preprint

Same verdict, different culprit: which control decided? Attribution and enforcement points for AI agent payments

Jayath de Silva
Zenodo (CERN European Organization for Nuclear Research)
Access Control and Trust
preprint

Same verdict, different culprit: which control decided? Attribution and enforcement points for AI agent payments

Jayath de Silva
preprint en

Abstract

AI agents can now pay for things on their own, over settlement rails that do not govern the payer. Several systems published in 2026 put a deterministic check in front of an agent's action. We do not propose another one. We argue that this category, which already has independent implementations, is missing one property, and we work out what it costs to require it. When controls can only make a verdict stricter, the final verdict is the strictest one any control proposed, so the order the controls run in does not change it. The record of which control decided is a different matter. The last control to tighten the verdict is the first one in the order to reach the maximum, so attribution depends on order exactly when two or more controls reach that maximum. That happens all the time: any two outright refusals tie at the top. A specification that requires attribution therefore has to fix the evaluation order, and has to declare which controls win a tie. We make three small contributions. Attribution as a required output that a third party can score from machine-readable vectors, on approvals as well as refusals. A disclosure rule we call *bounded, not eliminated*, which obliges an implementation to state what its control leaves behind. And a benchmark scoring rule, `DEFENDED_BY_ACCIDENT`, which counts an attack refused by a control that was not aiming at it as a finding rather than a pass. The paper also maps where such a control can sit on an x402 payment (a pre-tool hook, the client's spend controls, a custodial signer, an agent gateway, the seller, the facilitator) and what each position can and cannot see. A pre-tool hook decides before the seller has named a price. A gateway sees the signed amount but cannot revoke it. No position holds an independent view of what the price should have been. The reference implementation scores 17 of 20 attacks defended, one by accident and two open by design. The instrument has no benign scenarios, so an implementation that refused everything would score perfectly; we found that defect by reading a competitor's design. Three prior-art surveys removed three larger claims while we wrote this, and Section 3 reports each removal.

Zenodo (CERN European Organization for Nuclear Research)
Access Control and Trust
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Same verdict, different culprit: which control decided? Attribution and enforcement points for AI agent payments — Jayath de Silva · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS