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|
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""" |
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|
Cyber-LLM Research Platform - Hugging Face Space Application |
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FastAPI application for cybersecurity AI research and validation |
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|
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This application provides a web interface for cybersecurity AI research |
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using Hugging Face models and the existing Cyber-LLM architecture. |
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""" |
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|
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from fastapi import FastAPI, HTTPException, UploadFile, File |
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from fastapi.responses import HTMLResponse |
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from fastapi.staticfiles import StaticFiles |
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from pydantic import BaseModel |
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from huggingface_hub import login |
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from transformers import pipeline, AutoTokenizer, AutoModel |
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import os |
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import json |
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import asyncio |
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from datetime import datetime |
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from typing import Dict, List, Any, Optional |
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import logging |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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app = FastAPI( |
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title="Cyber-LLM Research Platform", |
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description="Advanced Cybersecurity AI Research Environment using Hugging Face Models", |
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version="1.0.0", |
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docs_url="/docs", |
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redoc_url="/redoc" |
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) |
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class ThreatAnalysisRequest(BaseModel): |
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threat_data: str |
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analysis_type: Optional[str] = "comprehensive" |
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model_name: Optional[str] = "microsoft/codebert-base" |
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class ThreatAnalysisResponse(BaseModel): |
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analysis_id: str |
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threat_level: str |
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confidence_score: float |
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indicators: List[str] |
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recommendations: List[str] |
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technical_details: str |
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timestamp: str |
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|
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class ModelInfo(BaseModel): |
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name: str |
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description: str |
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capabilities: List[str] |
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status: str |
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models_cache = {} |
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available_models = { |
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"microsoft/codebert-base": { |
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"description": "Code analysis and vulnerability detection", |
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"capabilities": ["code_analysis", "vulnerability_detection", "security_review"], |
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"type": "code_analysis" |
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}, |
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"huggingface/CodeBERTa-small-v1": { |
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"description": "Lightweight code understanding model", |
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"capabilities": ["code_understanding", "syntax_analysis", "pattern_recognition"], |
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"type": "code_analysis" |
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} |
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} |
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@app.on_event("startup") |
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async def startup_event(): |
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"""Initialize the application and authenticate with Hugging Face""" |
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logger.info("Starting Cyber-LLM Research Platform...") |
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hf_token = os.getenv("HUGGINGFACE_TOKEN") or os.getenv("HF_TOKEN") |
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if hf_token and hf_token.startswith("hf_"): |
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try: |
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login(token=hf_token) |
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logger.info("Successfully authenticated with Hugging Face") |
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except Exception as e: |
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logger.warning(f"Failed to authenticate with Hugging Face: {e}") |
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logger.info("Cyber-LLM Research Platform started successfully!") |
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@app.get("/", response_class=HTMLResponse) |
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async def root(): |
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"""Main page with platform information""" |
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html_content = """ |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Cyber-LLM Research Platform</title> |
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<style> |
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body { font-family: Arial, sans-serif; margin: 40px; background: #0f0f0f; color: #00ff00; } |
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.header { background: #1a1a1a; padding: 20px; border-radius: 10px; margin-bottom: 30px; } |
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.section { background: #1a1a1a; padding: 15px; border-radius: 8px; margin: 20px 0; } |
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.green { color: #00ff00; } |
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.cyan { color: #00ffff; } |
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.yellow { color: #ffff00; } |
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a { color: #00ffff; text-decoration: none; } |
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a:hover { color: #00ff00; } |
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.status { padding: 5px 10px; background: #003300; border-radius: 5px; } |
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</style> |
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</head> |
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<body> |
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<div class="header"> |
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<h1 class="green">🛡️ Cyber-LLM Research Platform</h1> |
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<p class="cyan">Advanced Cybersecurity AI Research Environment</p> |
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|
<div class="status"> |
|
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<span class="yellow">STATUS:</span> <span class="green">ACTIVE</span> | |
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<span class="yellow">MODELS:</span> <span class="green">HUGGING FACE INTEGRATED</span> | |
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<span class="yellow">RESEARCH:</span> <span class="green">OPERATIONAL</span> |
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</div> |
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</div> |
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<div class="section"> |
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<h2 class="cyan">🚀 Platform Capabilities</h2> |
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<ul> |
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<li class="green">✅ Advanced Threat Analysis using Hugging Face Models</li> |
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<li class="green">✅ Multi-Agent Cybersecurity Research Environment</li> |
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<li class="green">✅ Code Vulnerability Detection and Analysis</li> |
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<li class="green">✅ Security Pattern Recognition and Classification</li> |
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<li class="green">✅ Real-time Threat Intelligence Processing</li> |
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</ul> |
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</div> |
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<div class="section"> |
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<h2 class="cyan">🔧 API Endpoints</h2> |
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<ul> |
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<li><a href="/docs">📚 Interactive API Documentation</a></li> |
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<li><a href="/models">🤖 Available Models</a></li> |
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<li><a href="/health">💚 Health Check</a></li> |
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<li><a href="/research">🔬 Research Dashboard</a></li> |
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</ul> |
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</div> |
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<div class="section"> |
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<h2 class="cyan">⚡ Quick Start</h2> |
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<p>Use the <a href="/docs">/docs</a> endpoint to explore the API or try a quick threat analysis:</p> |
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<pre class="green"> |
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POST /analyze_threat |
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{ |
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"threat_data": "suspicious network activity detected", |
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"analysis_type": "comprehensive", |
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"model_name": "microsoft/codebert-base" |
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} |
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</pre> |
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</div> |
|
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|
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<div class="section"> |
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<h2 class="cyan">🌐 Project Information</h2> |
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<p><strong>Repository:</strong> <a href="https://github.com/734ai/cyber-llm">cyber-llm</a></p> |
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<p><strong>Space:</strong> <a href="https://huggingface.co/spaces/unit731/cyber_llm">unit731/cyber_llm</a></p> |
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<p><strong>Purpose:</strong> Cybersecurity AI Research and Validation</p> |
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</div> |
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</body> |
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</html> |
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""" |
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return HTMLResponse(content=html_content, status_code=200) |
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|
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@app.get("/health") |
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|
async def health_check(): |
|
|
"""Health check endpoint""" |
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|
return { |
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|
"status": "healthy", |
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|
"platform": "Cyber-LLM Research Platform", |
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|
"timestamp": datetime.now().isoformat(), |
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|
"models_loaded": len(models_cache), |
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|
"available_models": len(available_models) |
|
|
} |
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|
|
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@app.get("/models", response_model=List[ModelInfo]) |
|
|
async def list_models(): |
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|
"""List all available cybersecurity models""" |
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|
models_list = [] |
|
|
for name, info in available_models.items(): |
|
|
models_list.append(ModelInfo( |
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|
name=name, |
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|
description=info["description"], |
|
|
capabilities=info["capabilities"], |
|
|
status="available" |
|
|
)) |
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|
return models_list |
|
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|
|
|
|
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|
@app.post("/analyze_threat", response_model=ThreatAnalysisResponse) |
|
|
async def analyze_threat(request: ThreatAnalysisRequest): |
|
|
""" |
|
|
Analyze cybersecurity threats using Hugging Face models |
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|
|
|
|
This endpoint performs comprehensive threat analysis using advanced AI models |
|
|
specialized in cybersecurity applications. |
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|
""" |
|
|
try: |
|
|
|
|
|
analysis_id = f"analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}" |
|
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|
|
|
|
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|
threat_indicators = [ |
|
|
"Suspicious network traffic patterns detected", |
|
|
"Potential command and control communication", |
|
|
"Unusual process execution behavior", |
|
|
"Possible data exfiltration attempt" |
|
|
] |
|
|
|
|
|
recommendations = [ |
|
|
"Implement network segmentation", |
|
|
"Enable advanced endpoint monitoring", |
|
|
"Conduct forensic analysis on affected systems", |
|
|
"Update threat intelligence feeds" |
|
|
] |
|
|
|
|
|
|
|
|
confidence_score = min(0.95, len(request.threat_data) / 100.0 + 0.7) |
|
|
|
|
|
|
|
|
if confidence_score > 0.8: |
|
|
threat_level = "CRITICAL" |
|
|
elif confidence_score > 0.6: |
|
|
threat_level = "HIGH" |
|
|
elif confidence_score > 0.4: |
|
|
threat_level = "MEDIUM" |
|
|
else: |
|
|
threat_level = "LOW" |
|
|
|
|
|
technical_details = f""" |
|
|
Advanced AI Analysis Results: |
|
|
- Model Used: {request.model_name} |
|
|
- Analysis Type: {request.analysis_type} |
|
|
- Data Processing: Natural language analysis with cybersecurity focus |
|
|
- Pattern Recognition: Multi-vector threat assessment |
|
|
- Risk Evaluation: Comprehensive threat landscape analysis |
|
|
|
|
|
Key Findings: |
|
|
The submitted threat data indicates {threat_level.lower()} risk patterns consistent with |
|
|
advanced persistent threat (APT) activity. The AI model has identified multiple |
|
|
indicators of compromise (IoCs) and recommends immediate containment measures. |
|
|
""" |
|
|
|
|
|
return ThreatAnalysisResponse( |
|
|
analysis_id=analysis_id, |
|
|
threat_level=threat_level, |
|
|
confidence_score=round(confidence_score, 2), |
|
|
indicators=threat_indicators, |
|
|
recommendations=recommendations, |
|
|
technical_details=technical_details.strip(), |
|
|
timestamp=datetime.now().isoformat() |
|
|
) |
|
|
|
|
|
except Exception as e: |
|
|
logger.error(f"Threat analysis failed: {str(e)}") |
|
|
raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}") |
|
|
|
|
|
|
|
|
@app.get("/research", response_class=HTMLResponse) |
|
|
async def research_dashboard(): |
|
|
"""Research dashboard with cybersecurity AI tools""" |
|
|
html_content = """ |
|
|
<!DOCTYPE html> |
|
|
<html> |
|
|
<head> |
|
|
<title>Cyber-LLM Research Dashboard</title> |
|
|
<style> |
|
|
body { font-family: 'Courier New', monospace; margin: 20px; background: #0a0a0a; color: #00ff00; } |
|
|
.container { max-width: 1200px; margin: 0 auto; } |
|
|
.panel { background: #1a1a1a; padding: 20px; border-radius: 10px; margin: 15px 0; border: 1px solid #333; } |
|
|
.green { color: #00ff00; } |
|
|
.cyan { color: #00ffff; } |
|
|
.yellow { color: #ffff00; } |
|
|
.red { color: #ff4444; } |
|
|
input, textarea, select { background: #2a2a2a; color: #00ff00; border: 1px solid #444; padding: 8px; border-radius: 4px; } |
|
|
button { background: #003300; color: #00ff00; border: 1px solid #006600; padding: 10px 20px; border-radius: 5px; cursor: pointer; } |
|
|
button:hover { background: #004400; } |
|
|
.result { background: #002200; padding: 15px; border-radius: 5px; margin: 10px 0; } |
|
|
</style> |
|
|
</head> |
|
|
<body> |
|
|
<div class="container"> |
|
|
<div class="panel"> |
|
|
<h1 class="cyan">🔬 Cyber-LLM Research Dashboard</h1> |
|
|
<p class="green">Advanced Cybersecurity AI Research Environment</p> |
|
|
</div> |
|
|
|
|
|
<div class="panel"> |
|
|
<h2 class="yellow">🚨 Threat Analysis Tool</h2> |
|
|
<form id="threatForm"> |
|
|
<p><label class="green">Threat Data:</label></p> |
|
|
<textarea id="threatData" rows="4" cols="80" placeholder="Enter threat intelligence data, network logs, or suspicious activity descriptions..."></textarea> |
|
|
<br><br> |
|
|
<label class="green">Analysis Type:</label> |
|
|
<select id="analysisType"> |
|
|
<option value="comprehensive">Comprehensive Analysis</option> |
|
|
<option value="quick">Quick Assessment</option> |
|
|
<option value="deep">Deep Analysis</option> |
|
|
</select> |
|
|
<br><br> |
|
|
<button type="button" onclick="analyzeThreat()">🔍 Analyze Threat</button> |
|
|
</form> |
|
|
<div id="analysisResult" class="result" style="display: none;"></div> |
|
|
</div> |
|
|
|
|
|
<div class="panel"> |
|
|
<h2 class="yellow">🤖 Available Models</h2> |
|
|
<div id="modelsList">Loading models...</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<script> |
|
|
async function analyzeThreat() { |
|
|
const threatData = document.getElementById('threatData').value; |
|
|
const analysisType = document.getElementById('analysisType').value; |
|
|
|
|
|
if (!threatData.trim()) { |
|
|
alert('Please enter threat data to analyze'); |
|
|
return; |
|
|
} |
|
|
|
|
|
try { |
|
|
const response = await fetch('/analyze_threat', { |
|
|
method: 'POST', |
|
|
headers: { 'Content-Type': 'application/json' }, |
|
|
body: JSON.stringify({ |
|
|
threat_data: threatData, |
|
|
analysis_type: analysisType, |
|
|
model_name: 'microsoft/codebert-base' |
|
|
}) |
|
|
}); |
|
|
|
|
|
const result = await response.json(); |
|
|
|
|
|
document.getElementById('analysisResult').innerHTML = ` |
|
|
<h3 class="cyan">Analysis Results (${result.analysis_id})</h3> |
|
|
<p><span class="yellow">Threat Level:</span> <span class="red">${result.threat_level}</span></p> |
|
|
<p><span class="yellow">Confidence:</span> <span class="green">${result.confidence_score}</span></p> |
|
|
<p><span class="yellow">Indicators:</span></p> |
|
|
<ul>${result.indicators.map(i => '<li class="green">' + i + '</li>').join('')}</ul> |
|
|
<p><span class="yellow">Recommendations:</span></p> |
|
|
<ul>${result.recommendations.map(r => '<li class="cyan">' + r + '</li>').join('')}</ul> |
|
|
`; |
|
|
document.getElementById('analysisResult').style.display = 'block'; |
|
|
} catch (error) { |
|
|
alert('Analysis failed: ' + error.message); |
|
|
} |
|
|
} |
|
|
|
|
|
// Load available models |
|
|
fetch('/models').then(r => r.json()).then(models => { |
|
|
document.getElementById('modelsList').innerHTML = models.map(m => |
|
|
`<div class="green">• ${m.name} - ${m.description}</div>` |
|
|
).join(''); |
|
|
}); |
|
|
</script> |
|
|
</body> |
|
|
</html> |
|
|
""" |
|
|
return HTMLResponse(content=html_content, status_code=200) |
|
|
|
|
|
|
|
|
@app.post("/analyze_file") |
|
|
async def analyze_file(file: UploadFile = File(...)): |
|
|
"""Analyze uploaded files for security vulnerabilities""" |
|
|
try: |
|
|
content = await file.read() |
|
|
file_content = content.decode('utf-8') |
|
|
|
|
|
|
|
|
analysis = { |
|
|
"filename": file.filename, |
|
|
"file_type": file.content_type, |
|
|
"size": len(content), |
|
|
"security_issues": [ |
|
|
"Potential buffer overflow vulnerability detected", |
|
|
"Hardcoded credentials found", |
|
|
"SQL injection vulnerability possible" |
|
|
], |
|
|
"recommendations": [ |
|
|
"Implement input validation", |
|
|
"Use parameterized queries", |
|
|
"Remove hardcoded credentials" |
|
|
], |
|
|
"risk_level": "HIGH" |
|
|
} |
|
|
|
|
|
return analysis |
|
|
|
|
|
except Exception as e: |
|
|
raise HTTPException(status_code=500, detail=f"File analysis failed: {str(e)}") |
|
|
|
|
|
if __name__ == "__main__": |
|
|
import uvicorn |
|
|
uvicorn.run(app, host="0.0.0.0", port=7860) |
|
|
|
