🛡️ Cyber-LLM: Advanced Cybersecurity AI Research Platform

⚡ Live Demo: https://huggingface.co/spaces/unit731/cyber_llm

🎯 Vision

Cyber-LLM empowers security professionals by synthesizing advanced adversarial tradecraft, OPSEC-aware reasoning, and automated attack-chain orchestration. From initial reconnaissance through post-exploitation and exfiltration, Cyber-LLM acts as a strategic partner in red-team simulations and adversarial research.

🚀 Key Innovations

Adversarial Fine-Tuning: Self-play loops generate adversarial prompts to harden model robustness.
Explainability & Safety Agents: Modules providing rationales for each decision and checking for OPSEC breaches.
Data Versioning & MLOps: Integrated DVC, MLflow, and Weights & Biases for reproducible pipelines.
Dynamic Memory Bank: Embedding-based persona memory for historical APT tactics retrieval.
Hybrid Reasoning: Combines neural LLM with symbolic rule-engine for exploit chain logic.

🏗️ Detailed Architecture

Base Model: Choice of LLaMA-3 / Phi-3 trunk with 7B–33B parameters.
LoRA Adapters: Specialized modules for Recon, C2, Post-Exploit, Explainability, Safety.
Memory Store: Vector DB (e.g., FAISS or Milvus) for persona & case retrieval.
Orchestrator: LangChain + YAML-defined workflows under src/orchestration/.
MLOps Stack: DVC-managed datasets, MLflow tracking, W&B dashboards, Grafana monitoring.

💻 Usage Examples

# Preprocess data
dvc repro src/data/preprocess.py
# Train adapters
python src/training/train.py --module ReconOps
# Run a red-team scenario
python src/deployment/cli/cyber_cli.py orchestrate recon,target=10.0.0.5

🚀 Packaging & Deployment

☁️ Live Hugging Face Space

Experience the platform instantly at unit731/cyber_llm

🌐 Web Dashboard: Interactive cybersecurity research interface
📊 Real-time Analysis: Live threat analysis and monitoring
🔍 API Access: RESTful API for integration
📚 Documentation: Complete API docs at /docs

🐳 Docker Deployment

Docker: docker-compose up --build for offline labs.
Kubernetes: kubectl apply -f src/deployment/k8s/ for scalable clusters.
CLI: cyber-llm agent recon --target 10.0.0.5

👨‍💻 Author: Muzan Sano

📧 Contact: [email protected] / [email protected]

🌟 PROJECT STATUS & CAPABILITIES

✅ Currently Implemented

🚀 Live Hugging Face Space with interactive web interface
🛡️ Advanced Threat Analysis using AI models
🤖 Multi-Agent Architecture for distributed security operations
🧠 Cognitive AI Systems with memory and learning capabilities
📊 Real-time Monitoring and alerting systems
🔍 Code Vulnerability Detection and security analysis
🐳 Enterprise Docker Deployment with Kubernetes support
🔐 Zero Trust Security Architecture and RBAC
📈 MLOps Pipeline with DVC, MLflow, and monitoring

🎯 Key Features Available

Interactive Web Dashboard: Research interface at /research endpoint
RESTful API: Complete API at /docs with real-time threat analysis
File Analysis: Upload and analyze security files for vulnerabilities
Multi-Model Support: Integration with Hugging Face transformer models
Real-time Processing: WebSocket support for live monitoring
Enterprise Architecture: Scalable, production-ready deployment

🚀 Try It Now

# Quick API test
curl -X POST "https://unit731-cyber-llm.hf.space/analyze_threat" \
  -H "Content-Type: application/json" \
  -d '{"threat_data": "suspicious network activity on port 443"}'

# Or visit the interactive dashboard
# https://unit731-cyber-llm.hf.space/research

🔧 Local Development

git clone https://github.com/734ai/cyber-llm.git
cd cyber-llm
cp .env.template .env  # Configure your API keys
docker-compose up -d   # Start full platform

🌐 Experience Live Demo: https://huggingface.co/spaces/unit731/cyber_llm