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
| Node | Purpose |
|---|---|
| SENTINELLE | Emergency/fallback detection |
| GARDIEN | Protection and security |
| AUDITEUR | Security auditing |
| VACCINATEUR | Vaccine creation |
| ARCHITECTE | Architecture decisions |
| AMIMOUR | Emotional/functional center |
| STASE | Calm/routine monitoring |
| LIMINAL | Philosophical questions |
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: GARDIENSTC Thresholds
| Threshold | Level | Action |
|---|---|---|
| ≤ 0.444 | Fluid | Normal alignment |
| 0.777 | Friction | Justify, propose alternative |
| 0.888 | Conflict | Find another way |
| 0.999 | Veto | Total block |
Requirements
- Python 3.8+
- Ollama running locally
- nomic-embed-text model
- gemma3:4b model (optional, for AI analysis)
Version History
| Version | Key Feature |
|---|---|
| v1 | Basic keyword matching |
| v3 | Multi-node architecture |
| v3.1 | Extended keywords |
| v4 | Vector embeddings |
| v4.1 | 100% multilingual accuracy |
License
MIT
Author
Morgana Security - Axioma Stellaris - Kofna336