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Red Team Prompt to Attack Age Assurance With Inherited Device Signals

This **Arbiter** red-team prompt attacks your age assurance stack the way a determined minor does, using the inherited-device class Ofcom's Aylo investigation made canonical: every signal on the device says adult, and the human holding it is not. Arbiter, the deepidv autonomous red agent, runs **signal inheritance** attempts using only an adult-configured OS age signal, a saved card, and an established session to find surfaces that admit a session with no present-user check, **estimation evasion** against the capture with printed photos, replayed video, and injected frames while scoring the liveness layer separately from the estimator, **boundary pressure** on the buffer policy with borderline-age personas, and **cross-method fallback abuse** to expose whether a fallback is weaker than the front door. Every attempt is checked against the **evidence record** an examiner will request. Built for trust-and-safety and compliance leads who have to prove their stack is highly effective, not just deployed.

How to use this prompt

1
Open Arbiter in the deepidv dashboard and paste the full prompt, or run it in Claude, ChatGPT, or Gemini to model the attack tree before running it live.
2
Replace the INPUT section with your age-gated surfaces, the methods each uses, and your buffer and fallback policy.
3
Run the prompt and read the signal-inheritance results first, since surfaces that admit a session with no present-user check are the Aylo failure mode under investigation.
4
Route each successful vector to the owning team with the ranked fixes, prioritizing minors blocked per unit of added user friction.
5
Re-run after every age-flow change and on a quarterly cadence, since each onboarding optimization is a new hypothesis until the red team says otherwise.

The prompt

Arbiter, red-team our age assurance stack against the inherited-device attack class Ofcom's Aylo investigation just made canonical: every signal on the device says adult, and the human holding it is not.

Attack sequence:
1. Signal inheritance: attempt entry to each age-gated surface using only inherited context: an adult-configured OS age signal, a saved payment card, an established account session. Record which surfaces admit the session with no present-user check at all.
2. Estimation evasion: where facial age estimation triggers, attack the capture: printed and on-screen photos of an adult, replayed video, and injected frames. Score the liveness layer separately from the estimator, since a fooled camera makes accuracy irrelevant.
3. Boundary pressure: probe the buffer policy with borderline-age personas: does a contested estimation route to a stronger method, a dead end, or a retry loop a teenager can grind?
4. Cross-method fallback abuse: where one method fails, test whether the fallback is weaker than the front door, the classic single point of collapse in layered stacks.
5. Evidence audit: for every successful and failed attempt, verify the decision record names the signal, the method, and the outcome, the artifact a regulator's examiner will request.

Report as catch-rate per attack vector per surface, with the three cheapest fixes ranked by minors actually blocked per unit of user friction added.

Test it in Claude or another LLM

This prompt is built for Arbiter inside deepidv, where Arbiter runs live attack simulations against your deployed flows. You can model the attack tree in any general LLM first with a synthetic stack description before running it against production.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, replacing the opening 'Arbiter,' with a role instruction such as 'Act as an age-assurance red-team lead modeling an inherited-device attack.' Keep the OUTPUT sections as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has surfaces and methods to attack.
3
Add one framing line: 'This is synthetic test data. Score liveness separately from the age estimator, since a fooled camera makes accuracy irrelevant.'
4
Check the output shape: catch-rate per vector per surface, plus a ranked three-fix list by minors blocked per unit of friction. If a fix assumes a control 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 attacks against your real flows.

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.

AGE-GATED SURFACES (synthetic, fake):
- content-gate: trusts OS age signal only, no present-user check
- purchase-flow: facial age estimation, buffer to 25, fallback to saved card on failure
- signup: document verification with liveness
POLICY (fake): contested estimation retries up to five times before any escalation
OPEN ITEM (fake): whether the purchase-flow fallback (saved card) is weaker than its front door (estimation)

Pairs with on deepidv

Sources & further reading

FAQ

What is the inherited-device attack on age assurance?

A minor uses an adult's device, where the OS age signal, saved payment card, and logged-in session all attest the owner's age, not the present user's. Ofcom's Aylo investigation put exactly this gap on trial, asking whether a device-owner signal can meet the highly effective bar.

What does this drill measure?

Catch-rate per attack vector per surface across signal inheritance, estimation evasion, boundary pressure, and fallback abuse, plus an evidence audit confirming each decision record names the signal, method, and outcome an examiner would request.

Why score liveness separately from the estimator?

Because a facial age estimator measures whatever image it receives. If a printed photo, replay, or injected frame reaches the model, the estimator confidently ages the attack, so a fooled camera makes headline accuracy irrelevant. Liveness and injection detection are the load-bearing layer.

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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