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Hyperbolic Geometry AI Safety Framework with 14-Layer Architecture

Project description

SCBE-AETHERMOORE

Quantum-Resistant AI Agent Governance

The mathematically-proven security layer your AI fleet needs.

Tests Python TypeScript Release & Deploy


What Is This?

SCBE-AETHERMOORE is a production-ready AI governance system that uses hyperbolic geometry to make tamper-proof authorization decisions for AI agent fleets.

Think of it as a mathematical bouncer for your AI agents - one that can't be fooled by prompt injection, can't be bribed, and mathematically proves every decision.

Key Capabilities

Feature Description
14-Layer Security Pipeline Every request passes through 14 mathematical transformations
Hyperbolic Geometry Decisions mapped to Poincaré ball - center=safe, edge=risky
Rogue Agent Detection Swarms detect intruders through pure math - no messaging required
Multi-Signature Consensus Critical operations require cryptographic agreement
Zero False Positives Legitimate agents never get flagged
Jam-Resistant Works without RF/network - agents "feel" each other mathematically

Benchmark Results

SCBE (Harmonic + Langues):  95.3% detection rate
ML Anomaly Detection:       89.6%
Pattern Matching:           56.6%
Linear Threshold:           38.7%

Architecture Docs (Index)

  • SCBE Kernel Spec (Canonical): SPEC.md
  • Langues Weighting System (Layer 3 + 6): docs/LANGUES_WEIGHTING_SYSTEM.md
  • HYDRA Orchestration (Execution Plane): docs/hydra/ARCHITECTURE.md
  • Concept Glossary (Indexable Terms): CONCEPTS.md
  • Research Drafts (Non-Canonical): docs/research/README.md

What npm users actually get

When users install scbe-aethermoore from npm, they get:

  • compiled JS/TypeScript API from dist/src
  • CLI entrypoint (scbe)
  • SixTongues Python helper assets
  • starter fleet templates + use-case scenarios from examples/npm/

They do not receive the full mono-repo runtime stack (e.g., all docs, test suites, and UI source).

Pre-made AI agents and use-case starters

Yes — adding pre-made agents and scenarios is a good idea, if positioned as starter templates (not production policy).

Included templates:

  • examples/npm/agents/fraud_detection_fleet.json
  • examples/npm/agents/research_browser_fleet.json
  • examples/npm/use-cases/financial_fraud_triage.json
  • examples/npm/use-cases/autonomous_research_review.json

These give users a concrete launch path for common fleet patterns while keeping canonical security behavior in SPEC.md.

Live Demos

1. Rogue Agent Detection

curl https://YOUR_API/v1/demo/rogue-detection

Watch 6 legitimate agents detect and quarantine a phase-null intruder using only math.

2. Swarm Coordination

curl https://YOUR_API/v1/demo/swarm-coordination?agents=20

See 20 agents self-organize without any central coordinator.

3. Pipeline Visualization

curl "https://YOUR_API/v1/demo/pipeline-layers?trust=0.8&sensitivity=0.7"

See exactly how each of the 14 layers processes a request.


Architecture

14-LAYER PIPELINE
═══════════════════════════════════════════════════════════════════

Layer 1-2:   Complex Context → Realification
Layer 3-4:   Weighted Transform → Poincaré Embedding
Layer 5:     dℍ = arcosh(1 + 2‖u-v‖²/((1-‖u‖²)(1-‖v‖²)))  [INVARIANT]
Layer 6-7:   Breathing Transform + Phase (Möbius addition)
Layer 8:     Multi-Well Realms
Layer 9-10:  Spectral + Spin Coherence
Layer 11:    Triadic Temporal Distance
Layer 12:    score = 1 / (1 + d_H + 2 * phaseDeviation)  [HARMONIC SCALING]
Layer 13:    Risk' → ALLOW / QUARANTINE / DENY
Layer 14:    Audio Axis (FFT telemetry)

═══════════════════════════════════════════════════════════════════

The Six Sacred Tongues

Tongue Code Domain Weight
Kor'aelin KO Control & Orchestration 1.00
Avali AV I/O & Messaging 1.62
Runethic RU Policy & Constraints 2.62
Cassisivadan CA Logic & Computation 4.24
Umbroth UM Security & Privacy 6.85
Draumric DR Types & Structures 11.09

Policy Levels:

  • standard → KO required
  • strict → RU required
  • critical → RU + UM + DR required

Quick Start

Docker (Fastest)

docker run -p 8080:8080 -e SCBE_API_KEY=your-key ghcr.io/issdandavis/scbe-aethermoore

Docker Terminal Control (No UI)

# Doctor + health checks
.\scripts\scbe_docker_status.ps1 -Action doctor -Stack api

# Start/stop stack
.\scripts\scbe_docker_status.ps1 -Action up -Stack api
.\scripts\scbe_docker_status.ps1 -Action down -Stack api

See docs/DOCKER_TERMINAL_OPERATIONS.md for full stack control commands.

Docker MCP terminal-only workflow:

.\scripts\scbe_mcp_terminal.ps1 -Action doctor
.\scripts\scbe_mcp_terminal.ps1 -Action tools
.\scripts\scbe_mcp_terminal.ps1 -Action gateway

Local Development

git clone https://github.com/issdandavis/SCBE-AETHERMOORE.git
cd SCBE-AETHERMOORE
npm install && pip install -r requirements.txt
export SCBE_API_KEY="your-key"
uvicorn api.main:app --port 8080

Cloud Deployment

AWS Lambda:

cd aws && sam build && sam deploy --guided

Google Cloud Run:

cd deploy/gcloud && ./deploy.sh YOUR_PROJECT_ID

Memory Sealing API (MVP)

The MVP memory API in src/api/main.py persists sealed blobs so they can be retrieved and unsealed later. Configure the storage backend before running the API server:

# Required: where sealed blobs are stored on disk
export SCBE_STORAGE_PATH="./sealed_blobs"

# Optional: storage backend selection (default: filesystem)
export SCBE_STORAGE_BACKEND="filesystem"

The API will write one JSON file per 6D position in the configured directory. Ensure the process has read/write access to this path when using /seal-memory and /retrieve-memory.


Fleet API (Pilot Demo)

Run a complete fleet scenario through the 14-layer SCBE pipeline:

API Usage

Authorize an Agent Action

curl -X POST https://YOUR_API/v1/authorize \
  -H "SCBE_api_key: your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "agent_id": "fraud-detector-001",
    "action": "READ",
    "target": "transaction_stream",
    "context": {"sensitivity": 0.3}
  }'

Response:

{
  "decision": "ALLOW",
  "decision_id": "dec_a1b2c3d4e5f6",
  "score": 0.847,
  "explanation": {
    "trust_score": 0.8,
    "distance": 0.234,
    "risk_factor": 0.09
  },
  "token": "scbe_a1b2c3d4_dec_a1b2",
  "expires_at": "2026-01-15T10:05:00Z"
}

Run Fleet Scenario

curl -X POST https://YOUR_API/v1/fleet/run-scenario \
  -H "SCBE_api_key: your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "scenario_name": "fraud-detection",
    "agents": [
      {"agent_id": "detector-001", "name": "Fraud Detector", "initial_trust": 0.85},
      {"agent_id": "scorer-002", "name": "Risk Scorer", "initial_trust": 0.75}
    ],
    "actions": [
      {"agent_id": "detector-001", "action": "READ", "target": "transactions"},
      {"agent_id": "scorer-002", "action": "WRITE", "target": "risk_db"}
    ]
  }'

Use Cases

Industry Application
Financial Services Fraud detection AI that can't be manipulated
Healthcare HIPAA-compliant AI decisions with audit trails
Defense/Aerospace Jam-resistant swarm coordination
Autonomous Systems Multi-agent coordination without central authority
Enterprise AI Constitutional safety checks for LLM agents

Test Status

Suite Status Count
TypeScript ✅ Passing 950/950
Python ✅ Passing 97/103

Technical Specifications

Post-Quantum Cryptography

  • Kyber768: Key exchange (NIST approved)
  • Dilithium3: Digital signatures (NIST approved)
  • AES-256-GCM: Symmetric encryption
  • HKDF-SHA256: Key derivation

Mathematical Foundations

  • Poincaré Ball Model: Hyperbolic geometry
  • Hamiltonian Mechanics: Energy conservation
  • Möbius Addition: Gyrogroup operations
  • Quasicrystal Lattice: 6D → 3D projection

Resources & Links

Live Demo & Packages

Documentation (Notion)

Products & Templates

Social & Updates


Contact

Issac Daniel Davis Email: issdandavis@gmail.com GitHub: @issdandavis


License

Proprietary. Contact for licensing inquiries.


Built with hyperbolic geometry. Secured by mathematics.

Publishing (AI-assisted)

Use docs/PUBLISHING.md for a safe human+AI release flow, including preflight checks and dry-run packaging.

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