iot-camera-recon

iot-camera-recon is a skill for Claude Code, Codex from uphiago/recon-skills. It costs 22 tokens per session (2,951 once invoked), scanned A, original, MIT.

A security assessment for Flask applications using the Werkzeug debugger, a development error tool that can expose detailed failures when left enabled in production.

In plain words
What is it for?
Checking whether a Flask service exposes debugger pages, tracebacks, or related configuration after an error.
Why use it?
A public debugger may reveal application details and, depending on its settings, sensitive configuration or an interactive console.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Checking whether a Flask service exposes debugger pages, tracebacks, or related configuration after an error.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uphiago/recon-skills/iot-camera-recon
About the project

Recon Skills is a pack of security-testing skills covering reconnaissance, web applications, APIs, authentication, vulnerability validation, cloud infrastructure, and reporting. Security professionals use it for authorized assessments of systems they own or have written permission to test. The catalogue entries are individual skills from the pack.

uphiago/recon-skills · 1,251 stars · on GitHub · hiago.sh

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add uphiago/recon-skills --skill iot-camera-recon
Clone the repo
git clone --depth 1 https://github.com/uphiago/recon-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for iot-camera-recon

README.md
[![agentmods](https://agentmods.dev/badge/skills/uphiago/recon-skills/iot-camera-recon/github.svg)](https://agentmods.dev/skills/uphiago/recon-skills/iot-camera-recon)
Your own site
<a href="https://agentmods.dev/skills/uphiago/recon-skills/iot-camera-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/iot-camera-recon/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for iot-camera-recon

Your own site · 80×15
<a href="https://agentmods.dev/skills/uphiago/recon-skills/iot-camera-recon"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/iot-camera-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,951 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium YARA Match · line 43
    YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).
    Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
  • medium Data Exfiltration · line 107
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.02951
Opus 5 $0.00011 $0.01476
Sonnet 5 $0.00004 $0.00590
Haiku 4.5 $0.00002 $0.00295

Measured 10d ago against content hash 8c1230e7ea43, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

iot-camera-recon scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

compatibility: Requires curl, nmap, python3, masscan, subfinder, httpx, nuclei
recon/iot-camera-recon/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

# 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 Brand Default HTTP Port Snapshot URL Config URL Default Creds
Axis 80, 8010 /axis-cgi/jpg/image.cgi /axis-cgi/admin/param.cgi?action=list root:pass, root:admin
Hikvision 80, 554 /ISAPI/Streaming/channels/101/picture /System/configurationFile?auth=... admin:12345, admin:admin
Dahua 80, 554 /cgi-bin/snapshot.cgi /cgi-bin/configManager.cgi?action=getConfig admin:admin, admin:password
Intelbras 80 /cgi-bin/snapshot.cgi /web/cgi-bin/hi3510/param.cgi admin:admin, admin:123456
ONVIF 80, 8899 N/A (SOAP) /onvif/device_service admin:admin

Procedure

Phase 1 — Mass Camera Discovery

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

Read the full file on GitHub · 256 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 256 lines · 22 tokens per session scan A 8c1230e7ea43

Subscribe to this mod's changes

iot-camera-recon is a skill published in the GitHub repository uphiago/recon-skills (1,251 stars, last pushed 8d ago), licensed MIT. It adds 22 tokens to every session and 2,951 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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