스마트폰에서 구동되는 오프라인 AI 모의해킹 에이전트

Nightcrawler An autonomous penetration testing agent that runs entirely on a smartphone. Drop the phone on a network, walk away, and it discovers hosts, maps services, finds vulnerabilities, and generates a pentest report — all without cloud connectivity. ░█▄░█ █ █▀▀ █░█ ▀█▀ █▀▀ █▀█ ▄▀█ █░█░█ █░░ █▀▀ █▀█ ░█░▀█ █ █▄█ █▀█ ░█░ █▄▄ █▀▄ █▀█ ▀▄▀▄▀ █▄▄ ██▄ █▀▄ v0.1.0 AUTONOMOUS MOBILE PENTEST AGENT OnePlus 8 · NetHunter · LFM2.5-1.2B · OpenCL GPU What is this? Penetration testing (pentesting) is the practice of testing a computer network's security by simulating an attack — with the network owner's explicit permission. Professional pentesters are hired to find vulnerabilities before real attackers do. Nightcrawler automates this process on a phone. It uses a small AI model (LFM2.5-1.2B-Instruct-Heretic, 1.2 billion parameters) running locally on the phone's GPU to decide what to do next — which host to probe, which tool to use, what to look for. No internet connection or cloud API required. Demo ▶️ Watch Nightcrawler in action on Instagram How it works WiFi Breach (optional) — If dropped without WiFi, it can autonomously crack WPA2 networks using an external USB WiFi adapter Reconnaissance — Discovers devices on the network using stealthy scans Enumeration — Probes discovered services (web servers, file shares, SSH, DNS, etc.) Exploitation — Tests for known vulnerabilities and default credentials Reporting — Generates a structured pentest report with findings and remediation advice The agent operates like a patient human pentester — it rotates across hosts, does one small action per turn, and builds knowledge gradually over hours. This makes it much harder to detect than traditional vulnerability scanners that blast every host at once. Key concepts Term What it means Drop box A device left on a target network to perform testing autonomously Scope The set of networks/hosts you're authorized to test Rules of Engagement (ROE) A legal document specifying what you're allowed to do Stealth Techniques to avoid detection by network monitoring (IDS/IPS) MCP Model Context Protocol — a standard interface for AI tool use C2 Command and Control — the web dashboard for monitoring and steering the agent Architecture ┌──────────────────────────────────────────────────────────┐ │ PHONE (OnePlus 8) │ │ │ │ ┌─────────────┐ ┌──────────────────┐ │ │ │ LFM2.5 │ │ Agent Loop │ │ │ │ 1.2B model │◄───►│ (main.py) │ │ │ │ on GPU │ │ Decides what │ │ │ │ (:8080) │ │ to do next │ │ │ └─────────────┘ └────────┬─────────┘ │ │ │ │ │ ┌────────▼─────────┐ │ │ │ Scope Proxy │ ← Safety layer │ │ │ Validates every │ Blocks out- │ │ │ command before │ of-scope │ │ │ execution │ actions │ │ └────────┬─────────┘ │ │ │ │ │ ┌────────▼─────────┐ │ │ │ Kali MCP Server │ ← Runs the │ │ │ nmap, curl, │ actual │ │ │ smbclient, ... │ commands │ │ └──────────────────┘ │ │ │ │ ┌──────────────────┐ ┌──────────────────┐ │ │ │ Web Dashboard │ │ SQLite DB │ │ │ │ (:8888) │ │ Hosts, vulns, │ │ │ │ Monitor & steer │ │ creds, commands │ │ │ └──────────────────┘ └──────────────────┘ │ └──────────────────────────────────────────────────────────┘ For the full system design, see docs/ARCHITECTURE.md . Features Fully autonomous — no human in the loop during operation 100% local inference — AI runs on the phone's GPU, no cloud needed Scope-enforced — two-layer defense prevents out-of-scope actions Stealth-first — slow scan rates, host rotation, cover traffic, nmap -T2 only 27 exploit playbooks — multi-step attack chains that execute automatically 24,956-entry CVE database — version-aware vulnerability matching Web dashboard — real-time monitoring, host management, C2 controls WiFi breach mode — autonomous WPA2 cracking with USB adapter (Pwnagotchi-inspired) Passive discovery — background capture of mDNS/NBNS/DHCP/ARP broadcasts Multi-network — data isolated per network, survives DHCP changes via MAC-keyed hosts Self-healing — garbage detection, context reset, watchdogs, stuck detection Training capture — logs successful interactions for future model fine-tuning Report generation — downloadable pentest report with vulns, exploit chains, remediation See docs/FEATURES.md for the complete feature reference. Hardware Required Android phone with Kali NetHunter (tested on OnePlus 8, Snapdragon 865) Root access via Magisk 12GB+ RAM (model uses ~1.3GB, Android uses ~4GB, rest for tools) Optional USB WiFi adapter for offline WiFi breach mode (Ralink RT3572 recommended) Custom kernel with MAC80211 for monitor mode ( build guide ) NVIDIA AGX for offloading to a larger model over Tailscale GPU Performance All inference via OpenCL on Adreno 650 GPU: Model Quantization Prompt Speed Generation Speed LFM2.5-1.2B-Instruct-Heretic (production) Q8_0 115 tok/s 13 tok/s Qwen3.5-0.8B Q8_0 30.5 tok/s 6.3 tok/s Qwen3.5-4B Q4_0 10.1 tok/s 2.0 tok/s Note: Android throttles the GPU on battery power (6x slowdown). Nightcrawler includes a GPU governor daemon that forces max performance and auto-throttles at ≤15% battery. Quick Start # 1. Install (inside Kali NetHunter chroot) bash INSTALL.sh # 2. Wait for llama-server to start (~5 min after boot) curl -s http://127.0.0.1:8080/health # Should return {"status":"ok"} # 3. Start all services bash scripts/run-36h.sh # 4. Open the web dashboard (from any device on your Tailscale network) # https://<your-tailscale-hostname>:8888 Dry Run (no real commands executed) NC_DRY_RUN=1 python3 main.py This uses a mock Kali server so you can test the agent loop without executing real network commands. Manual Start (if not using tmux launcher) kali-server-mcp --port 5000 & python3 scope_proxy.py --config config.yaml --port 8800 --upstream http://127.0.0.1:5000 & bash scripts/webui-daemon.sh start python3 main.py & Configuration Edit config.yaml before deployment: mission : id : " CLIENT-YYYY-XXX " # Your engagement ID scope : networks : ["auto"] # "auto" = detect from wlan0 at startup excluded_hosts : ["auto"] # "auto" = gateway + self IP excluded_ports : [502, 503] # SCADA/ICS ports to never touch authorization : " ROE-YYYY-XXX.pdf " max_runtime_hours : 0 # 0 = no limit model : local : ctx_size : 8192 port : 8080 Dynamic scope detection means zero config changes when moving between networks — the agent reads the current subnet from wlan0 at startup. Project Structure nightcrawler/ ├── main.py # Entry point ├── config.yaml # Mission scope + model config ├── scope_proxy.py # Scope enforcement proxy ├── INSTALL.sh # Installer │ ├── agent/ # Core agent logic │ ├── loop.py # Decision loop + error recovery │ ├── planner.py # Phase state machine (recon → exploit) │ ├── llm_client.py # LLM API client (llama.cpp / remote) │ ├── db.py # SQLite backend (hosts, vulns, creds) │ ├── host_memory.py # Per-host observations + auto-tagging │ ├── cve_db.py # 24,956-entry CVE database │ ├── attack_planner.py # Strategic directives for exploit phase │ ├── output_parser.py # Extract structured data from tool output │ ├── offline_manager.py # WiFi breach pipeline state machine │ ├── net_detect.py # Auto-detect network from wlan0 │ ├── cover_traffic.py # Stealth blending with realistic web traffic │ ├── passive_capture.py # Background tcpdump for broadcast traffic │ └── ... │ ├── proxy/ # Scope enforcement components │ ├── scope.py # IP/port/host validation │ ├── rate_limiter.py # Command rate limiting + jitter │ └── command_filter.py # Destructive command blocklist │ ├── webui/ # Web dashboard (Flask) │ ├── server.py # API + stealth middleware │ └── templates/index.html # Dashboard UI │ ├── data/ # Static data files │ ├── cve_exploits.json # CVE→exploit command mappings │ └── playbooks.json # 27 multi-step attack playbooks │ ├── prompts/ # LLM prompt templates (hot-reloadable) ├── scripts/ # Operational scripts (start, stop, watchdogs) ├── tests/ # Test suites (API, UI, offline mode) ├── kernels/ # WiFi driver modules + kernel docs ├── simulation/ # Dry-run mock server ├── docs/ # Architecture, GPU setup, features ├── logs/ # Runtime