NeuroSploit/legacy/custom_agents/example_agent.py

#!/usr/bin/env python3
"""
Example Custom Agent for NeuroSploitv2
This demonstrates how to create custom agents for specific tasks
"""

import logging
from typing import Dict
from core.llm_manager import LLMManager

logger = logging.getLogger(__name__)


class CustomAgent:
    """Example custom agent - Web API Security Scanner"""
    
    def __init__(self, config: Dict):
        """Initialize custom agent"""
        self.config = config
        self.llm = LLMManager(config)
        self.name = "WebAPIScanner"
        logger.info(f"{self.name} initialized")
    
    def execute(self, target: str, context: Dict) -> Dict:
        """Execute custom agent logic"""
        logger.info(f"Running {self.name} on {target}")
        
        results = {
            "agent": self.name,
            "target": target,
            "status": "running",
            "findings": []
        }
        
        try:
            # Your custom logic here
            # Example: API endpoint testing
            results["findings"] = self._scan_api_endpoints(target)
            
            # Use AI for analysis
            ai_analysis = self._ai_analyze(results["findings"])
            results["ai_analysis"] = ai_analysis
            
            results["status"] = "completed"
            
        except Exception as e:
            logger.error(f"Error in {self.name}: {e}")
            results["status"] = "error"
            results["error"] = str(e)
        
        return results
    
    def _scan_api_endpoints(self, target: str) -> list:
        """Custom scanning logic"""
        # Implement your custom scanning logic
        return [
            {"endpoint": "/api/users", "method": "GET", "auth": "required"},
            {"endpoint": "/api/admin", "method": "POST", "auth": "weak"}
        ]
    
    def _ai_analyze(self, findings: list) -> Dict:
        """Use AI to analyze findings"""
        prompt = f"""
Analyze the following API security findings:

{findings}

Provide:
1. Security assessment
2. Risk prioritization
3. Exploitation recommendations
4. Remediation advice

Response in JSON format.
"""
        
        system_prompt = "You are an API security expert."
        
        try:
            response = self.llm.generate(prompt, system_prompt)
            return {"analysis": response}
        except Exception as e:
            return {"error": str(e)}