mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
synced 2026-03-02 07:43:24 +00:00
e0935793c5
116 modules | 100 vuln types | 18 API routes | 18 frontend pages Major features: - VulnEngine: 100 vuln types, 526+ payloads, 12 testers, anti-hallucination prompts - Autonomous Agent: 3-stream auto pentest, multi-session (5 concurrent), pause/resume/stop - CLI Agent: Claude Code / Gemini CLI / Codex CLI inside Kali containers - Validation Pipeline: negative controls, proof of execution, confidence scoring, judge - AI Reasoning: ReACT engine, token budget, endpoint classifier, CVE hunter, deep recon - Multi-Agent: 5 specialists + orchestrator + researcher AI + vuln type agents - RAG System: BM25/TF-IDF/ChromaDB vectorstore, few-shot, reasoning templates - Smart Router: 20 providers (8 CLI OAuth + 12 API), tier failover, token refresh - Kali Sandbox: container-per-scan, 56 tools, VPN support, on-demand install - Full IA Testing: methodology-driven comprehensive pentest sessions - Notifications: Discord, Telegram, WhatsApp/Twilio multi-channel alerts - Frontend: React/TypeScript with 18 pages, real-time WebSocket updates
84 lines
2.3 KiB
Python
Executable File
84 lines
2.3 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Example Custom Agent for NeuroSploitv2
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This demonstrates how to create custom agents for specific tasks
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"""
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import logging
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from typing import Dict
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from core.llm_manager import LLMManager
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logger = logging.getLogger(__name__)
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class CustomAgent:
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"""Example custom agent - Web API Security Scanner"""
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def __init__(self, config: Dict):
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"""Initialize custom agent"""
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self.config = config
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self.llm = LLMManager(config)
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self.name = "WebAPIScanner"
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logger.info(f"{self.name} initialized")
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def execute(self, target: str, context: Dict) -> Dict:
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"""Execute custom agent logic"""
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logger.info(f"Running {self.name} on {target}")
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results = {
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"agent": self.name,
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"target": target,
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"status": "running",
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"findings": []
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}
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try:
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# Your custom logic here
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# Example: API endpoint testing
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results["findings"] = self._scan_api_endpoints(target)
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# Use AI for analysis
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ai_analysis = self._ai_analyze(results["findings"])
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results["ai_analysis"] = ai_analysis
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results["status"] = "completed"
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except Exception as e:
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logger.error(f"Error in {self.name}: {e}")
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results["status"] = "error"
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results["error"] = str(e)
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return results
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def _scan_api_endpoints(self, target: str) -> list:
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"""Custom scanning logic"""
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# Implement your custom scanning logic
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return [
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{"endpoint": "/api/users", "method": "GET", "auth": "required"},
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{"endpoint": "/api/admin", "method": "POST", "auth": "weak"}
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]
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def _ai_analyze(self, findings: list) -> Dict:
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"""Use AI to analyze findings"""
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prompt = f"""
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Analyze the following API security findings:
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{findings}
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Provide:
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1. Security assessment
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2. Risk prioritization
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3. Exploitation recommendations
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4. Remediation advice
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Response in JSON format.
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"""
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system_prompt = "You are an API security expert."
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try:
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response = self.llm.generate(prompt, system_prompt)
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return {"analysis": response}
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except Exception as e:
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return {"error": str(e)}
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