KYC Bypass Age Verification Using Generative Video Models

src/AI/AI-Risk-Frameworks.md

@@ -77,6 +77,44 @@ Google's [SAIF (Security AI Framework)](https://saif.google/secure-ai-framework/
 
 The [MITRE AI ATLAS Matrix](https://atlas.mitre.org/matrices/ATLAS) provides a comprehensive framework for understanding and mitigating risks associated with AI systems. It categorizes various attack techniques and tactics that adversaries may use against AI models and also how to use AI systems to perform different attacks.
 
+## Generative-media biometric / KYC bypasses
+
+A growing offensive use of generative models is **bypassing browser-based KYC, age-verification, and liveness workflows**. The weak point is often **not** the transport or the cloud liveness provider, but the **camera trust boundary**: a desktop browser usually trusts whatever device `getUserMedia()` exposes as a webcam.
+
+### Practical attack chain
+
+1. **Generate challenge-compliant media** with a video-to-video model from a source actor and a victim reference image.
+2. **Inject the forged stream before signing / upload**, e.g. through a Linux virtual camera created with `v4l2loopback` and fed by OBS or FFmpeg.
+3. Let the browser and vendor SDK (WebRTC / AWS / etc.) **capture, sign, and upload the attacker-controlled frames as if they came from a real webcam**.
+
+This is important during assessments because signed WebSocket chunks or proprietary SDK framing may make **network-layer tampering** impractical, while **camera-layer injection** still works.
+
+### High-value tester angles
+
+- **Virtual webcam acceptance**: if the flow works from a desktop browser, test whether OBS / `v4l2loopback` / vendor virtual cameras are accepted as normal peripherals.
+- **Camera-API redirection on mobile**: native mobile flows may still be vulnerable when Frida hooks camera APIs and replaces sensor buffers with frames from an MP4 or emulator-backed virtual camera.
+- **Constraint weakening**: pages that require exact `deviceId`, `frameRate`, `width`, `height`, or `facingMode` can sometimes be bypassed by monkeypatching `navigator.mediaDevices.getUserMedia` and replacing strict constraints with broader ranges.
+- **Low-quality generation + post-processing**: generate the cheapest video the model can render reliably, then use FFmpeg upscaling / frame interpolation to satisfy capture requirements.
+- **Predictable active challenges**: repeated head-movement or light-flash sequences are worth recording and replaying through a generative workflow.
+- **Weak replay detection**: simple scene perturbations (crop/position shifts, overlay changes, slight motion) can be enough when the anti-replay logic only checks superficial frame similarity.
+
+### Mobile vs desktop trust differences
+
+Native mobile apps can raise attacker cost with:
+- **sensor / Secure Element attestation** for camera buffers,
+- **execution-integrity** signals such as **Play Integrity** or **App Attest**,
+- **motion correlation** between video and accelerometer / gyroscope telemetry.
+
+Desktop web flows usually lack an equivalent camera chain of trust, so they are generally the path of least resistance.
+
+### Defensive review notes
+
+When reviewing a KYC or liveness integration, verify whether it:
+- allows a **desktop-browser fallback** for a workflow that was only threat-modeled for mobile capture;
+- relies mostly on **algorithmic liveness** without strong human escalation for suspicious sessions;
+- uses **stable or predictable challenges** that can be pre-recorded and fed into a generation pipeline;
+- detects **`getUserMedia` monkeypatching**, virtual cameras, inconsistent browser hardware telemetry, or missing device attestation.
+
 
 ## LLMJacking (Token Theft & Resale of Cloud-hosted LLM Access)
 
@@ -198,6 +236,10 @@ owner /srv/llm/** rw,
 ```
 
 ## References
+- [Synacktiv - KYC: Bypass age verification using generative video models](https://www.synacktiv.com/en/publications/kyc-bypass-age-verification-using-generative-video-models.html)
+- [Amazon Rekognition Face Liveness](https://docs.aws.amazon.com/rekognition/latest/dg/face-liveness.html)
+- [v4l2loopback](https://github.com/v4l2loopback/v4l2loopback)
+- [MDN - MediaDevices.getUserMedia()](https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia)
 - [Unit 42 – The Risks of Code Assistant LLMs: Harmful Content, Misuse and Deception](https://unit42.paloaltonetworks.com/code-assistant-llms/)
 - [LLMJacking scheme overview – The Hacker News](https://thehackernews.com/2024/05/researchers-uncover-llmjacking-scheme.html)
 - [oai-reverse-proxy (reselling stolen LLM access)](https://gitgud.io/khanon/oai-reverse-proxy)