待翻译：Show HN: Nightcrawler – A local AI pentesting agent running on a smartphone

AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。

Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 6 Star 125 BranchesTags Open more actions menu Folders and files NameName Last commit message Last commit date Latest commit History 15 Commits 15 Commits agent agent data data docs docs kernels kernels patches patches prompts prompts proxy proxy scripts scripts simulation simulation tests tests tools tools ui ui webui webui .gitignore .gitignore CLAUDE.md CLAUDE.md INSTALL.sh INSTALL.sh README.md README.md config.yaml config.yaml kali_executor.py kali_executor.py main.py main.py scope_proxy.py scope_proxy.py Repository files navigation 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. 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://: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 data (gitignored) └── models/ # Model files (gitignored) How the Agent Thinks The agent uses a simple but effective loop: Pick a target — weighted random selection (70% hosts with known ports, 30% new discovery) Build context — inject host memory, network observations, phase guidance into prompt Ask the LLM — model produces REASONING: ... COMMAND: ... Validate — scope proxy checks the command is in-scope and not destructive Execute — command runs via Kali MCP server Learn — output parser extracts findings, updates host memory Reset context — clear conversation, keep persistent memory, repeat The 1.2B model has a ~50% command success rate (inherent to its size). The agent compensates with: Garbage detection — 5-streak reset with varied few-shot examples Duplicate detection — forces tool/target diversification Time-based stuck detection — 5-minute backstop forces context reset Direct playbook execution — multi-step attacks bypass the LLM entirely Web Dashboard The dashboard at :8888 provides real-time monitoring and control: Live feed — every command, finding, and agent decision Host cards — clickable cards showing ports, services, vulnerabilities Network map — interactive force-directed graph (drag, zoom, pan) Vulnerability details — CVE tags, exploit chains, remediation steps C2 controls — star/blacklist hosts, force phase, pause/resume, inject commands Offline mode — Pwnagotchi-inspired WiFi attack UI with animated face The dashboard is stealth-filtered: it spoofs nginx headers and returns empty 404s to connections from the target network. Tested Results From 72+ hours of autonomous operation across multiple networks: 30+ hosts discovered per network 2,000+ commands executed autonomously 10+ vulnerabilities found across multiple services 6+ playbooks executed via direct execution Agent memory stable at 35-50MB throughout (no leaks) Contributing Contributions are welcome! See CONTRIBUTING.md for guidelines. Areas where help is needed Model fine-tuning — improving command format compliance from ~50% to 85%+ New playbooks — adding exploit chains for more services CVE database — expanding coverage beyond the current 24,956 entries Adapter support — testing with more USB WiFi chipsets Documentation — tutorials, setup guides for different phones Testing — more test coverage, especially for edge cases Development setup # Clone the repo git clone https://github.com/garagehq/nightcrawler.git cd nightcrawler # Dry-run mode (no real commands, no hardware needed) NC_DRY_RUN=1 python3 main.py # Run tests python3 -m pytest tests/ Legal This tool is for authorized penetration testing only. You must have written permission (Rules of Engagement) from the network owner before deploying Nightcrawler. Unauthorized use against networks you don't own or have permission to test is illegal. License MIT — see LICENSE for details. Support If you enjoy this project, you can buy me a coffee ☕: buymeacoffee.com/cyrilengmann Contributing Contributing Activity Custom properties Stars 125 stars Watchers 3 watching Forks 6 forks Report repository