NeuroSploit v3.3.0 — Autonomous MD-Agent Engine

Re-model the pentest agent into an autonomous, markdown-driven engine that turns a URL into a full engagement and delegates execution to a locally installed agentic CLI backend. Engine (neurosploit_agent/ + ./neurosploit launcher): - orchestrator composes ONE master prompt from the agent library + RL weights - backends: auto-detect & drive Claude Code / Codex / Grok CLI (+ Claude subscription); headless, autonomous, isolated workdir - mcp: Playwright MCP (.mcp.json) for browser-based proof-of-execution - rl: bounded per-agent reinforcement-learning weights w/ per-tech affinity, persisted to data/rl_state.json - models: latest registry incl. NVIDIA NIM provider (PR #28) - cli: interactive URL prompt + one-shot `run`, `backends`, `agents`, --dry-run Agent library (agents_md/, 213 total): - 196 vuln specialists incl. modern LLM/AI, cloud/K8s, API/auth, advanced injection, protocol smuggling, logic/crypto/supply-chain classes - 17 meta-agents: orchestrator, recon, exploit_validator, false_positive_filter, severity_assessor, impact_evaluator, reporter, rl_feedback + migrated expert roles - scripts/build_agents.py data-driven builder; REGISTRY.md index Docs: rewritten README.md, v3.3.0 RELEASE.md, .env.example (NVIDIA NIM, xAI, engine vars). Retire legacy Python orchestration (neurosploit.py + agent classes) to legacy/. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-30 07:15:30 +02:00 · 2026-06-14 20:57:38 -03:00
parent 59f8f42d80
commit 55af0d4634
248 changed files with 18707 additions and 577 deletions
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""
+Network Reconnaissance Agent - Network-focused information gathering and enumeration
+"""
+
+import os
+import json
+import subprocess
+from typing import Dict, List
+import logging
+from core.llm_manager import LLMManager
+from tools.recon import (
+    NetworkScanner, 
+    OSINTCollector,
+    DNSEnumerator,
+    SubdomainFinder
+)
+from urllib.parse import urlparse # Added import
+
+logger = logging.getLogger(__name__)
+
+
+class NetworkReconAgent:
+    """Agent responsible for network-focused reconnaissance and information gathering"""
+    
+    def __init__(self, config: Dict):
+        """Initialize network reconnaissance agent"""
+        self.config = config
+        self.llm = LLMManager(config)
+        self.network_scanner = NetworkScanner(config)
+        self.osint = OSINTCollector(config)
+        self.dns_enum = DNSEnumerator(config)
+        self.subdomain_finder = SubdomainFinder(config)
+        
+        logger.info("NetworkReconAgent initialized")
+    
+    def execute(self, target: str, context: Dict) -> Dict:
+        """Execute network reconnaissance phase"""
+        logger.info(f"Starting network reconnaissance on {target}")
+        
+        results = {
+            "target": target,
+            "status": "running",
+            "findings": [],
+            "network_scan": {},
+            "osint": {},
+            "dns": {},
+            "subdomains": [],
+            "ai_analysis": {}
+        }
+        
+        # Parse target to extract hostname if it's a URL
+        parsed_target = urlparse(target)
+        target_host = parsed_target.hostname or target # Use hostname if exists, otherwise original target
+        logger.info(f"Target for network tools: {target_host}")
+
+        try:
+            # Phase 1: Network Scanning
+            logger.info("Phase 1: Network scanning")
+            results["network_scan"] = self.network_scanner.scan(target_host) # Use target_host
+            
+            # Phase 2: DNS Enumeration
+            logger.info("Phase 2: DNS enumeration")
+            results["dns"] = self.dns_enum.enumerate(target_host) # Use target_host
+            
+            # Phase 3: Subdomain Discovery
+            logger.info("Phase 3: Subdomain discovery")
+            results["subdomains"] = self.subdomain_finder.find(target_host) # Use target_host
+            
+            # Phase 4: OSINT Collection
+            logger.info("Phase 4: OSINT collection")
+            results["osint"] = self.osint.collect(target_host) # Use target_host
+            
+            # Phase 5: AI Analysis
+            logger.info("Phase 5: AI-powered analysis")
+            results["ai_analysis"] = self._ai_analysis(results)
+            
+            results["status"] = "completed"
+            logger.info("Network reconnaissance phase completed")
+            
+        except Exception as e:
+            logger.error(f"Error during network reconnaissance: {e}")
+            results["status"] = "error"
+            results["error"] = str(e)
+        
+        return results
+    
+    def _ai_analysis(self, recon_data: Dict) -> Dict:
+        """Use AI to analyze reconnaissance data"""
+        prompt = self.llm.get_prompt(
+            "network_recon",
+            "ai_analysis_user",
+            default=f"""
+Analyze the following network reconnaissance data and provide insights:
+
+{json.dumps(recon_data, indent=2)}
+
+Provide:
+1. Attack surface summary
+2. Prioritized network target list
+3. Identified network vulnerabilities or misconfigurations
+4. Recommended next steps for network exploitation
+5. Network risk assessment
+6. Stealth considerations for network activities
+
+Response in JSON format with actionable recommendations.
+"""
+        )
+        
+        system_prompt = self.llm.get_prompt(
+            "network_recon",
+            "ai_analysis_system",
+            default="""You are an expert network penetration tester analyzing reconnaissance data.
+Identify network security weaknesses, network attack vectors, and provide strategic recommendations.
+Consider both technical and operational security aspects."""
+        )
+
+        try:
+            # Format the user prompt with recon_data
+            formatted_prompt = prompt.format(recon_data_json=json.dumps(recon_data, indent=2))
+            response = self.llm.generate(formatted_prompt, system_prompt)
+            return json.loads(response)
+        except Exception as e:
+            logger.error(f"AI analysis error: {e}")
+            return {"error": str(e), "raw_response": response if 'response' in locals() else None}
+    
+    def passive_recon(self, target: str) -> Dict:
+        """Perform passive reconnaissance only"""
+        # Parse target to extract hostname if it's a URL
+        parsed_target = urlparse(target)
+        target_host = parsed_target.hostname or target
+        
+        return {
+            "osint": self.osint.collect(target_host), # Use target_host
+            "dns": self.dns_enum.enumerate(target_host), # Use target_host
+            "subdomains": self.subdomain_finder.find(target_host) # Use target_host
+        }
+    
+    def active_recon(self, target: str) -> Dict:
+        """Perform active reconnaissance"""
+        # Parse target to extract hostname if it's a URL
+        parsed_target = urlparse(target)
+        target_host = parsed_target.hostname or target
+
+        return {
+            "network_scan": self.network_scanner.scan(target_host) # Use target_host
+        }
+