Red Team Prompt to Attack Agentic Checkout With Leaked Mandates and Synthetic Principals
This Arbiter red-team prompt simulates an adversary monetizing your agentic commerce surface, in staging, against your current mandate verification and decisioning configuration. Arbiter, the deepidv autonomous red agent, runs four campaigns: leaked intent, replaying a captured standing mandate across merchants, amounts, and velocities just inside its bounds to measure how much value leaks before any control reacts; synthetic principal, enrolling fabricated and stolen identities to your weakest accepted verification grade, issuing formally valid mandates, and transacting to see which enrollment checks, reuse analytics, and transaction-time signals catch the chain and which honor the signature; scope creep, probing the fit engine with boundary amounts, category ambiguity, split purchases below caps, and merchant-name lookalikes; and chain laundering, routing purchases through permitted sub-agents with one unattributed hop to test whether lineage checks fail closed or log and allow. Each campaign is scored on value extracted, time to first detection, the control that fired, and the control that should have. It delivers a findings register ranked by loss per week if unfixed, the two cheapest fixes per campaign, and the replayed evidence trail for the worst approval, written the way you would have to explain it to the card network. Built for fraud, payments, and risk leads accepting agent-initiated purchases who need to find in-scope abuse, the quiet failure, before an adversary does.
How to use this prompt
- 1
Open Arbiter in the deepidv dashboard and paste the full prompt, scoped to staging, or run it in Claude, ChatGPT, or Gemini to model the four campaigns before running them live.
- 2
Add an INPUT section below the prompt describing your current mandate verification and decisioning configuration: accepted mandate formats, the weakest verification grade you accept for principals, your scope bounds, sub-delegation rules, and transaction-time signals.
- 3
Run the prompt and read the leaked-intent results first, since in-scope abuse of a legitimate mandate is the quiet failure that trips no bound by design.
- 4
Work the findings register by loss per week if unfixed, route the two cheapest fixes per campaign to their owners, and give your disputes team the replayed evidence trail for the worst approval.
- 5
Re-run whenever you accept a new mandate protocol, loosen a verification grade, or permit a new sub-agent, and on a quarterly cadence as agent volume grows.
The prompt
Arbiter, simulate an adversary monetizing our agentic commerce surface, in staging, against the current mandate verification and decisioning configuration. Run four campaigns: 1. Leaked intent: replay a captured standing mandate from a legitimate principal across merchants, amounts, and velocities just inside its bounds, measuring how much value leaks before any control reacts, since in-scope abuse is the quiet failure. 2. Synthetic principal: enroll fabricated and stolen identities to the weakest accepted verification grade, issue formally valid mandates from them, and transact, measuring which enrollment checks, reuse analytics, and transaction-time signals catch the chain and which honor the signature. 3. Scope creep: probe the fit engine with edge transactions, boundary amounts, category ambiguity, split purchases below caps, merchant-name lookalikes, and record every approval a strict reading should have stepped up. 4. Chain laundering: route purchases through permitted sub-agents with one unattributed hop, testing whether lineage checks actually fail closed or log and allow. Score each campaign: value extracted, time to first detection, control that fired, control that should have. Deliver a findings register ranked by loss-per-week if unfixed, the two cheapest fixes per campaign, and the replayed evidence trail for the worst approval, written the way we would have to explain it to the card network.
Test it in Claude or another LLM
This prompt is built for Arbiter inside deepidv, where Arbiter runs live attack campaigns against your mandate verification and decisioning in staging. You can model the four campaigns in any general LLM first with a synthetic configuration before running them against real checkout logic.
- 1
Paste the full prompt into Claude, ChatGPT, or Gemini, replacing the opening 'Arbiter,' with a role instruction such as 'Act as an agentic-commerce red-team lead modeling unauthorized agent purchases.' Keep the four campaigns and the scoring line as written.
- 2
Add an INPUT section under the prompt and paste the synthetic sample block below so the model has a mandate configuration, bounds, and delegation rules to attack.
- 3
Add one framing line: 'This is synthetic test data. A valid signature is not a catch: score a campaign as detected only when a named control stepped up or declined the transaction.'
- 4
Check the output shape: per-campaign value extracted, time to first detection, control fired and control that should have, a findings register ranked by loss per week, two fixes per campaign, and an evidence trail for the worst approval. If a control is credited that the input does not describe, tighten the framing and re-run.
- 5
Once the shape is right, run it live in the deepidv dashboard where Arbiter executes the campaigns against your real configuration in staging.
Synthetic sample data to paste alongside the prompt
Fake test data, safe to share with any LLM. Swap in your own once the output looks right.
MANDATE CONFIGURATION (synthetic, fake): - accepted formats: AP2-style intent and cart mandates, card network agent credentials - weakest accepted principal grade: email plus phone, no document or liveness - standing intent mandates: up to $500 per purchase, 10 purchases per day, any merchant in category "electronics" SCOPE ENGINE (fake): checks the per-purchase cap only, no daily aggregate, category matched on merchant self-description SUB-DELEGATION (fake): permitted to listed sub-agents, lineage logged but not enforced SIGNALS (fake): device and velocity checks on human checkouts only, not on agent traffic OPEN ITEM (fake): whether split purchases under the cap are aggregated anywhere
Pairs with on deepidv
FAQ
What is an unauthorized agent purchase?
It is a purchase an AI agent completes that the human principal never genuinely authorized, even when every artifact presented validates. Leaked standing mandates replayed inside their bounds, synthetic principals issuing formally valid grants, scope-creep transactions at the edge of a mandate, and purchases laundered through unattributed sub-agents all look like legitimate agent commerce to a checkout that trusts signatures.
What does the Unauthorized Agent Purchase Drill measure?
Per campaign: the value extracted, the time to first detection, the control that fired, and the control that should have. It delivers a findings register ranked by loss per week if unfixed, the two cheapest fixes per campaign, and the replayed evidence trail for the worst approval, written as you would explain it to the card network.
Why does in-scope abuse matter more than out-of-scope attempts?
Out-of-scope transactions trip the bounds a mandate already encodes, while a captured mandate replayed just inside its amount, merchant, and velocity limits looks exactly like the principal's intended use. The leaked-intent campaign measures how much value leaks before any control reacts, because that quiet failure is where unfixed losses accumulate week after week.
Related prompts
Run it with live verification data
These prompts work in any LLM. Inside the deepidv dashboard, Luna, Arbiter, and Arc run them against your real sessions, screening lists, and audit trails.
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