FinTechTask Prompt

Fraud Prompt for iOS Camera Attestation Handshake Verification

This **Arbiter** task prompt wires a secure verification bridge to your iOS client endpoints using Apple's hardware camera-attestation parameters. Arbiter, the deepidv autonomous fraud agent, forces an immediate challenge handshake at the start of every capture, confirms the incoming video metadata carries the correct secure-enclave private signature, and drops any stream that arrives without an unforgeable hardware enclave token before facial geometry analysis ever runs. The output is an endpoint bridge spec, the challenge-handshake sequence, signature-validation rules, a drop-and-fallback policy for unsigned or emulated streams, and a monitoring layout that alerts on attestation-failure spikes. Built for mobile and fraud engineers who need to shut down camera-injection and virtual-driver attacks at the kernel line rather than auditing them after the fact.

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 if you are drafting the integration design outside the platform.
2
Replace the INPUT section with your iOS capture endpoints, current SDK verification order, the minimum iOS version you support, and any existing device-posture checks.
3
Run the prompt and read the endpoint bridge spec and handshake sequence first, confirming the attestation check runs before facial geometry analysis.
4
Hand the signature-validation rules and drop-and-fallback policy to your mobile engineer, and wire the monitoring layout to your fraud team's alert channel.
5
Re-run the prompt after each iOS SDK update and after Apple publishes new attestation parameters so the handshake stays current before injection toolkits adapt.

The prompt

Arbiter, establish a secure verification bridge with our iOS client endpoints using Apple's updated camera-attestation parameters. Force an immediate challenge handshake that confirms the incoming photo stream carries the correct secure-enclave private signature before any facial geometry analysis initializes.

INPUT, the user will paste:
- iOS capture endpoints and the app's current capture flow, in order (camera launch, selfie capture, liveness, facial geometry match, submit)
- The current SDK verification order and where, if anywhere, a hardware check runs today
- The minimum iOS version supported and any device-posture checks already in place (jailbreak, emulator, virtual-camera detection)
- The attestation parameters or references the firm is working from
- Any undecided fallback behavior for devices that cannot attest

OUTPUT, return the following structured response:

1. ENDPOINT BRIDGE SPEC
- The secure bridge between the app and the verification engine, and the request/response contract for an attestation exchange
- Where the attestation step inserts into the capture flow (it must precede facial geometry analysis)

2. CHALLENGE-HANDSHAKE SEQUENCE
- The randomized, unrepeatable challenge token issued to the client
- The secure-enclave signing step and the signed metadata packet returned
- The timing and replay-protection rules for the exchange

3. SIGNATURE-VALIDATION RULES
- How the engine verifies the enclave signature and the accepted key material
- The exact conditions that mark a stream authentic, suspect, or rejected

4. DROP-AND-FALLBACK POLICY
- The immediate drop rule for any stream missing a valid hardware enclave token
- The fallback path for supported devices that legitimately cannot attest, and the elevated checks that path requires
- The user-facing failure handling that avoids leaking the reason to an attacker

5. MONITORING LAYOUT
- The attestation-failure metrics to track and the thresholds that fire an alert
- How attestation-failure spikes route from Arbiter to the human fraud team
- The evidence bundle retained per rejected stream for later forensic review

Be specific about the ordering and the drop conditions. Where the firm's input is insufficient to specify a step, flag the question instead of guessing.

Test it in Claude or another LLM

This prompt is built for the Arbiter agent inside deepidv, where Arbiter establishes a live iOS camera-attestation handshake and validates the secure-enclave signature on every capture. You can dry-run the design in any general LLM first with synthetic endpoint data to see the bridge spec and handshake sequence before wiring it to real clients.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, but replace the opening 'Arbiter,' with a role instruction such as 'Act as a mobile fraud engineer designing an iOS secure-enclave camera-attestation handshake that runs before facial geometry analysis.' Keep the five OUTPUT sections exactly as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has capture endpoints, an SDK verification order, and a supported iOS version to design against.
3
Add one framing line: 'This is synthetic test data. Where a step cannot be specified from the input, flag it as an open question instead of guessing.'
4
Check the output shape: an endpoint bridge spec, a challenge-handshake sequence with the attestation check placed before facial geometry analysis, signature-validation rules, a drop-and-fallback policy for unsigned streams, and a monitoring layout. If any section reads vague, tighten the role line and re-run.
5
Once the output shape is right, run it live in the deepidv dashboard where Arbiter executes the handshake against your real iOS endpoints and alerts on attestation-failure spikes.

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.

iOS CAPTURE ENDPOINTS (synthetic, fake):
- App: ACME-IOS-TEST, min iOS 17, SDK v-test-4.2
- Capture flow: launch camera, capture selfie, run liveness, run facial geometry match, submit
- Current verification order (fake): liveness first, then facial geometry; no hardware attestation step
DEVICE-POSTURE CHECKS (fake): jailbreak flag only; no emulator or virtual-camera detection
ATTESTATION PARAMETERS (fake): secure-enclave signature ref APPLE-ATT-TEST-2026-02
OPEN ITEM (fake): fallback path for devices below min iOS undecided, fake ref FALLBACK-TEST-XX

Pairs with on deepidv

Sources & further reading

FAQ

What is iOS hardware camera attestation?

It is a security mechanism where an iOS device's secure enclave cryptographically signs camera-feed metadata to prove the image sequence emerged from an authentic physical camera lens. An application can request this attestation directly from the secure element, and reject any stream that does not carry the unforgeable enclave signature.

Why check attestation before facial geometry analysis?

Camera-injection attacks pipe a pre-rendered deepfake loop into the SDK through a modified driver or emulator, so the facial geometry stage sees a flawless synthetic face. Verifying the hardware signature first drops the injected stream before any biometric analysis runs, which is cheaper and far harder to defeat than post-capture visual inspection.

Can I use this prompt outside the deepidv dashboard?

Yes. The structure works in Claude, ChatGPT, or Gemini to draft the bridge spec, handshake sequence, and drop policy. Executing the live challenge handshake against your iOS endpoints and streaming attestation-failure alerts to your fraud team only works when it runs inside the deepidv dashboard.

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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