FinTechGenerator

Generator Prompt to Dry-Run an Accredited Injection Attack Detection Lab Battery

This **Arbiter** generator prompt takes your verification stack and the standardized attack families that accredited injection attack detection labs now certify against, then runs the **lab battery as a dry run** before you book the real audit. Arbiter, the deepidv autonomous red agent, exercises the **virtual camera family** presenting pre-rendered and real-time synthetic streams, the **hooking and tamper family** substituting frames below app visibility, the **emulator family** fabricating whole devices with perturbed fingerprints, the **stream substitution family** replacing media at the network boundary, and **signed-capture bypass attempts** riding stale attestation or downgrade paths. For each attempt it records the layer that caught it, detection latency, and whether the flow failed closed, treating any attempt that reached content judgment on an unverified channel as a finding even if liveness ultimately caught it. It returns a findings register ranked by exploitability, a per-OS-version coverage matrix mirroring accredited lab reporting, a weekly regression suite, and a readiness verdict, all in a sandbox that never touches production customer records.

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 scoping the campaign outside the platform.
2
Replace the INPUT section with your capture flows, your supported device and OS matrix, and your current injection-defense layers.
3
Run the prompt and read the readiness verdict first: book the lab audit now, or fix the named gaps first.
4
Route each surviving attack class to platform engineering with its per-OS coverage row and the layer that missed it.
5
Schedule the battery to re-run weekly and add an event-driven campaign whenever a new injection technique appears in the wild.

The prompt

Arbiter, run an accredited-lab-style injection attack detection battery against our verification stack, as a dry run before third-party lab testing.

Campaign scope, in the sandbox:
1. Virtual camera family: current desktop and mobile virtual camera drivers presenting pre-rendered and real-time synthetic streams into our browser and app capture flows.
2. Hooking and tamper family: capture-API interception and repackaged client builds substituting frames below app visibility, across our supported OS versions.
3. Emulator family: full-device fabrication profiles presenting as new customer hardware, including perturbed fingerprints per attempt.
4. Stream substitution family: network-level media replacement against any flow that trusts server-arriving streams.
5. Signed-capture bypass attempts: stale attestation reuse and downgrade-path riding where hardware capture signing is available.

For each attempt, record: the layer that caught it (driver provenance, capture signature, sensor forensics, structural liveness, telemetry), detection latency, and whether the flow failed closed. Any attempt that reached content judgment with an unverified channel is a finding even if liveness ultimately caught it.

Output: a findings register ranked by exploitability, per-OS-version coverage matrix formatted to mirror accredited lab reporting, the regression suite for weekly re-runs, and a readiness verdict: book the lab audit now, or fix the named gaps first.

Test it in Claude or another LLM

This prompt is built for the Arbiter agent inside deepidv, where Arbiter runs adversarial campaigns against a real stack under sandbox controls. You can dry-run the campaign structure in any general LLM first to see the findings-register shape before executing it live.

1
Paste the full prompt into Claude, ChatGPT, or Gemini, but replace the opening 'Arbiter,' with a role instruction such as 'Act as an injection attack detection lab auditor scoping a pre-audit battery.' Keep the OUTPUT sections exactly as written.
2
Under the INPUT section, paste the synthetic sample block below so the model has capture flows, an OS matrix, and defense layers to design against.
3
Add one framing line: 'This is a sandbox design exercise. Treat any unverified-channel pass as a finding even if a later layer catches it.'
4
Check the output shape: a findings register ranked by exploitability, a per-OS coverage matrix, a weekly regression suite, and a readiness verdict. If the design skips the fail-closed check on any channel, tighten the framing line and re-run.
5
Once the output shape is right, run it live in the deepidv dashboard where Arbiter executes the battery against your stack in the sandbox.

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.

CAPTURE FLOWS (synthetic, fake): browser web onboarding, iOS app, Android app
OS MATRIX (fake): iOS 18/19, Android 15/16, Windows and macOS desktop browsers
DEFENSE LAYERS (fake): driver provenance, capture signature (where available), sensor forensics, structural liveness, telemetry
ATTACK FAMILIES (fake): virtual camera, API hooking, emulator, network stream substitution, signed-capture bypass
OPEN ITEM (fake): downgrade path when a device cannot produce a capture signature

Pairs with on deepidv

Sources & further reading

FAQ

What is an injection attack detection lab battery?

Accredited laboratories test verification systems against standardized injection attack batteries, virtual cameras, hooking frameworks, emulators, and stream substitution, across OS versions, and certify the results. This prompt runs an equivalent battery as an internal dry run so you enter the real audit knowing what it will find.

Why dry-run before booking the lab?

Certification is a floor, and a failed lab visit is expensive in time and credibility. Running the battery internally first surfaces the gaps while they are still fixable, and produces the per-OS coverage matrix and regression suite the lab and your examiners both expect.

Is this safe to run against production?

The campaign runs in a sandbox with synthetic persona kits and never touches production customer records. Any attempt that reaches content judgment on an unverified channel is recorded as a finding even if a later layer caught it, so fail-open gaps surface before an attacker finds them.

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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