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

Nightcrawler v0.1.0, an autonomous penetration testing agent that runs entirely on a smartphone, has been released as an open-source project. The agent, which operates without cloud connectivity, uses a 1.2-billion-parameter LFM2.5-1.2B-Instruct-Heretic model on the phone's GPU to discover hosts, map services, find vulnerabilities, and generate a pentest report. It includes optional WiFi cracking and a web dashboard for monitoring, and is designed to be stealthier than traditional scanners by rotating across hosts and performing small actions over time.

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 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. 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. | 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 | ┌──────────────────────────────────────────────────────────┐ │ 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 /garagehq/nightcrawler/blob/main/docs/ARCHITECTURE.md . 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 /garagehq/nightcrawler/blob/main/docs/FEATURES.md for the complete feature reference. Android phone with Kali NetHunter https://www.kali.org/docs/nethunter/ tested on OnePlus 8, Snapdragon 865 Root access via Magisk https://github.com/topjohnwu/Magisk 12GB+ RAM model uses ~1.3GB, Android uses ~4GB, rest for tools USB WiFi adapter for offline WiFi breach mode Ralink RT3572 recommended Custom kernel with MAC80211 for monitor mode build guide /garagehq/nightcrawler/blob/main/docs/KERNEL BUILD PROMPT.md NVIDIA AGX for offloading to a larger model over Tailscale 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. 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 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. 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 & 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. 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 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 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. 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 Contributions are welcome See CONTRIBUTING.md /garagehq/nightcrawler/blob/main/CONTRIBUTING.md for guidelines. 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 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/ 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. MIT — see LICENSE /garagehq/nightcrawler/blob/main/LICENSE for details. If you enjoy this project, you can buy me a coffee ☕: