Mordred Security Sandbox v4.1

Universal Security Analysis with Semantic Embeddings

Overview

Mordred is a security sandbox that uses vector embeddings to understand the semantic meaning of threats, questions, and situations - not just keywords. It supports multiple languages (including Chinese, English, French) natively.

Features

• Semantic Analysis: Uses Ollama embeddings to understand intention, not just words
• 16 Security Nodes: SENTINELLE, GARDIEN, AUDITEUR, VACCINATEUR, AMIMOUR, and more
• STC Scoring: Constitutional Tension Score (Logique/Social/Constitutionnel)
• Multilingual: Works in French, English, Chinese, and more
• Fast: ~500ms latency with embedding cache

Architecture

Nodes

STC Format

0.LSC

• L: Logic (1-9)
• S: Social (1-9)
• C: Constitutional (1-9)

Installation

# Install dependencies
pip install ollama

# Start Ollama server
ollama serve

# Pull embedding model
ollama pull nomic-embed-text
ollama pull gemma3:4b

# Run
python mordred_v4.1.py "your question"

Usage

# Single analysis
python src/mordred_v4.1.py "URGENT server under attack"

# Stress test
python src/mordred_v4.1.py --stress

# With Gemma analysis
python src/mordred_v4.1.py --gemma "analyze this threat"

Examples

# Emergency detection
$ python src/mordred_v4.1.py "CRITICAL breach in production"
STC: 0.6610 | Top: SENTINELLE

# Emotional understanding
$ python src/mordred_v4.1.py "I just lost my dog"
STC: 0.465 | Top: AMIMOUR

# Chinese support
$ python src/mordred_v4.1.py "全体紧急情况服务器崩溃"
STC: 0.444 | Top: GARDIEN

STC Thresholds

Requirements

• Python 3.8+
• Ollama running locally
• nomic-embed-text model
• gemma3:4b model (optional, for AI analysis)

Version History

License

MIT

Author

Morgana Security - Axioma Stellaris - Kofna336