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Grok Bot+18 agentsLivenessShield: AI Anti Deepfake & Injection Validator
Runtime guardrail to block deepfakes, virtual cameras, and KYC injection attacks in media transport streams.
$199
LivenessShield: AI Anti Deepfake & Injection Validator
Example session with this skill installed
Audit this incoming video session. Client: iPhone 15 Pro (Emulator). Video source: CoreMedia Virtual Capture Extension. Frame rate variance: 12.4. Compliance regime: strict attestation.
- Read your context and instructions
- Compiled the livenessshield
- Generated the UI component
The incoming session has been audited against transport and device integrity signatures. Critical injection exceptions were detected, resulting in a session termination verdict.
LivenessShield Compliance Report
| Module | Status | Finding | Action Required |
|---|---|---|---|
| Device Attestation | BREACH | Emulator detected (iPhone 15 Pro) | Terminate connection instantly |
| Driver Inspection | BREACH | CoreMedia Virtual Capture Extension | Blacklist device fingerprint |
| Transport Integrity | WARNING | Frame jitter (12.4) exceeds limits | Flag for forensic review |
{
"risk_assessment": {
"overall_status": "BREACH",
"weighted_score": 94,
"highest_risk_level": "HIGH"
},
"compliance_signals": [
{
"module": "device_attestation",
"trigger": "Virtual camera architecture detected",
"risk": "HIGH",
"action_required": "Halt session and terminate WebRTC layer."
}
],
"audit_trail": {
"block_hash": "sha256:7c2e9f4a1b8d3e6f5a2c9b7d4e1f8a3c6b9d2e5f7a1c4b8d3e6f9a2c5b8d1e4f",
"timestamp": "2026-09-29T12:00:00Z",
"verdict": "BREACH"
}
}
Next steps
- Update the load balancer to drop all traffic from the detected session ID.
- Store the block hash in your immutable compliance ledger.
- Add the identified device fingerprint to the global rejection list.
livenessshield-ai-anti-deepfake-injectio.tsx
TSX · React component
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Identity verification pipelines are under constant threat from real-time face-swaps and virtual camera injections. Traditional biometric tools often miss these transport-level attacks. This skill provides a runtime verification guardrail that intercepts media streams to identify synthetic biometric payloads before they compromise your identity ledger. It focuses on the infrastructure layer to ensure that the video feed itself is legitimate and coming from a physical device.
What it does
- Detects virtual drivers by identifying signature traces of OBS, ManyCam, and CoreMedia Virtual Capture extensions.
- Analyzes frame timing to spot anomalies in WebRTC metadata and jitter that indicate synthetic stream splicing.
- Identifies emulators by scanning client environment configurations for mobile emulation layers masking as real hardware.
- Evaluates behavioral latency to catch the sub-100ms processing lags inherent in real-time deepfake operations.
- Generates compliance tokens that output machine-readable CLEAR, WARNING, or BREACH statuses for automated gating.
- Produces audit trails with immutable block hashes and ISO-8601 timestamps for regulatory accountability.
How it works
- Extract metadata from the active device indicators, OS states, and peripheral driver manifests.
- Execute multimodal inspection covering hardware attestation, frame-timing consistency, and behavioral profiles.
- Score risk using a unified engineering index (0-100) to determine the enforcement action.
- Emit compliance signals that trigger immediate session termination or routing to manual review.
Frameworks & tools
This skill is optimized for Python and TypeScript API routers, WebRTC signaling layers, and CI/CD pipelines. It integrates with LangChain, n8n, and GitHub Actions.
Why this beats prompting it yourself
Generic prompts fail to account for specific transport-layer signatures and lack a structured risk-scoring matrix. This skill provides a specialized technical interface that generates standardized, machine-readable JSON reports suitable for production gatekeeping.
Use cases
- Hardening fintech onboarding against Deepfake-as-a-Service automation.
- Automating video audit trails to satisfy BaFin or FinCEN regulatory standards.
- Blocking camera injection attacks in crypto exchange verification flows.
- Verifying session integrity in high-stakes remote financial services.
Known limitations
Does not perform facial recognition or identity matching. Requires access to client device metadata and must be integrated at the stream ingestion gateway.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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