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Wp Mass Recon

CommunityPopular
uphiago
wp-mass-recon

Batch WP recon: users, CORS, XMLRPC, leaks across domains.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namewp-mass-recon
Stars
1.3K
Forks
213
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Wp Mass 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/wp-mass-recon .claude/skills/wp-mass-recon
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wp Mass 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 Wp Mass 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 Wp Mass 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.

WP Mass Recon Skill

Batch WordPress vulnerability detection pipeline for scanning dozens to hundreds of domains in parallel. Detects WordPress presence, REST API user enumeration, CORS credential reflection, XMLRPC exposure, open registration, and sensitive file leaks in a single pass. Proven on 600+ US company domains across 28 sectors.

When to Use

  • You have an authorized target list from a bug bounty program, pentest engagement, or red team with signed RoE.
  • Sector-wide recon within authorized scope.
  • After subfinder/crt.sh produces a target list and you need to triage.
  • You want maximum findings per minute with a parallelizable pipeline.

Prerequisites

  • curl, httpx, python3, and jq.
  • A target list in domain|company|sector format, one target per line.
  • A writable OUTPUT_DIR; examples default to ./output.
  • The bundled scanner or the inline commands below.

How to Run

bash
# Phase 1: Live host discovery + tech detection
httpx -silent -l targets.txt -threads 50 -tech-detect -status-code -title -o $OUTDIR/alive.txt

# Phase 2: WP detection, user enum, CORS, XMLRPC (20 workers)
python3 parallel_batch.py "${OUTPUT_DIR:-./output}/targets.txt" 20

Or run the 4-phase pipeline manually using the commands in Procedure.

Quick Reference

CheckCommandPositive Signal
WP detectioncurl -skI "https://TARGET/wp-login.php"HTTP 200/301/302
User enumcurl --max-time 30 --connect-timeout 10 -sk "https://TARGET/wp-json/wp/v2/users"JSON with id, name, slug
CORScurl -skI "https://TARGET/wp-json/wp/v2/users" -H "Origin: https://evil.com"Access-Control-Allow-Credentials: true
XMLRPCcurl --max-time 30 --connect-timeout 10 -sk -X POST "https://TARGET/xmlrpc.php" -d '<methodCall><methodName>demo.sayHello</methodName></methodCall>'Hello! in body
Open regcurl --max-time 30 --connect-timeout 10 -sk "https://TARGET/wp-login.php?action=register"Form with user_login field
Source leaksParallel curl for .env, wp-config.php.bak, .git/config, debug.log, backup.sqlReal content (not SPA catch-all)

Procedure

Phase 1 — Target Preparation

bash
# Generate target list from crt.sh sector keywords
for sector in "landscaping" "roofing" "hvac" "pools" "plumbing"; do
  curl --max-time 30 --connect-timeout 10 -sk "https://crt.sh/?q=%25.${sector}%25&output=json" | jq -r '.[].name_value' | sed 's/\*\.//g' | sort -u >> $OUTDIR/discovered.txt
done

# Filter to unique domains, remove www prefix
cat $OUTDIR/discovered.txt | sed 's/^www\.//' | sort -u > $OUTDIR/unique_domains.txt

Phase 2 — Live Host Discovery

bash
# httpx with tech detection, 50 threads
httpx -silent -l $OUTDIR/unique_domains.txt -threads 50 -tech-detect -status-code -title \
  -o $OUTDIR/alive.txt

# Parse to URL list
awk '{print $1}' $OUTDIR/alive.txt | grep -E '^https?://' > $OUTDIR/urls.txt

Phase 3 — Parallel Vulnerability Scan

For each live target, run in parallel (20 workers):

bash
while read -r url; do
  domain=$(echo "$url" | sed 's|https\?://||')
  (
    echo "# $domain Findings" > "$OUTDIR/findings/${domain}_findings.md"

    # WP detection
    wp_code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 10 --connect-timeout 10 "$url/wp-login.php")
    [[ "$wp_code" =~ ^(200|301|302|403)$ ]] && echo "- WordPress: YES (wp-login: $wp_code)" >> "$OUTDIR/findings/${domain}_findings.md"

    # User enumeration
    users=$(curl -sk --max-time 10 --connect-timeout 10 "$url/wp-json/wp/v2/users" | python3 -c "import sys,json; d=json.load(sys.stdin); print(len(d) if isinstance(d,list) else 0)" 2>/dev/null)
    [[ "$users" -gt 0 ]] && echo "- Users exposed: $users" >> "$OUTDIR/findings/${domain}_findings.md"

    # CORS credential reflection
    cors=$(curl -skI --max-time 10 --connect-timeout 10 "$url/wp-json/wp/v2/users" -H "Origin: https://evil.com" 2>/dev/null | grep -i "access-control-allow-credentials: true")
    [[ -n "$cors" ]] && echo "- CORS: CREDENTIAL REFLECTION CONFIRMED" >> "$OUTDIR/findings/${domain}_findings.md"

    # XMLRPC
    xmlrpc=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 10 --connect-timeout 10 -X POST "$url/xmlrpc.php" \
      -d '<?xml version="1.0"?><methodCall><methodName>demo.sayHello</methodName></methodCall>')
    [[ "$xmlrpc" == "200" ]] && echo "- XMLRPC: OPEN" >> "$OUTDIR/findings/${domain}_findings.md"

    # Open registration
    reg=$(curl -sk --max-time 10 --connect-timeout 10 "$url/wp-login.php?action=register" | grep -o 'user_login')
    [[ -n "$reg" ]] && echo "- Open Registration: YES" >> "$OUTDIR/findings/${domain}_findings.md"

    # Source leaks (parallel)
    for path in ".env" "wp-config.php.bak" ".git/config" "debug.log" "backup.sql" "info.php" "phpinfo.php" "wp-config.php~" ".env.backup" ".env.local" "docker-compose.yml" "Dockerfile"; do
      leak_code=$(curl -sk -o /dev/null -w "%{http_code}" --max-time 5 --connect-timeout 5 "$url/$path")
      if [[ "$leak_code" == "200" ]]; then
        content=$(curl -sk --max-time 5 --connect-timeout 5 "$url/$path" | head -c 500)
        # False positive filter: skip SPA catch-alls
        if echo "$content" | grep -qiE 'DB_|APP_|_KEY|_SECRET|password|mysql|\[core\]|PHP Version|CREATE TABLE'; then
          echo "- Source leak: /$path (VERIFIED)" >> "$OUTDIR/findings/${domain}_findings.md"
        fi
      fi
    done
  ) &
  # Limit to 20 parallel workers
  while [[ $(jobs -r | wc -l) -ge 20 ]]; do sleep 0.5; done
done < $OUTDIR/urls.txt
wait

Phase 4 — Consolidation

bash
# Generate summary
echo "## Mass Recon Summary" > $OUTDIR/mass_summary.md
echo "" >> $OUTDIR/mass_summary.md
for f in $OUTDIR/findings/*_findings.md; do
  domain=$(basename "$f" _findings.md)
  criticals=$(grep -c "CRITICAL\|CREDENTIAL REFLECTION\|XMLRPC: OPEN\|Source leak: VERIFIED" "$f" || true)
  [[ "$criticals" -gt 0 ]] && echo "- **$domain**: $criticals findings" >> $OUTDIR/mass_summary.md
done

# Rank targets by finding count
grep "^\- \*\*" $OUTDIR/mass_summary.md | sort -t: -k2 -rn | head -20

Production Scanner (Python — parallel_batch.py pattern)

The production-proven approach uses concurrent.futures.ThreadPoolExecutor with 20 workers. Each worker calls curl via subprocess.run. This is 10x faster than bash while loops.

python
import concurrent.futures, subprocess, json

def curl_code(url, timeout=8):
    cmd = ["curl", "-sk", "-m", str(timeout), "-o", "/dev/null", "-w", "%{http_code}", url]
    r = subprocess.run(cmd, capture_output=True, timeout=timeout+5)
    return r.stdout.decode().strip()

def test_target(domain):
    # Determine protocol
    proto = None
    for p in ["https", "http"]:
        code = curl_code(f"{p}://{domain}/")
        if code not in ["000", ""]: proto = p; break
    if not proto: return None

    # WP detection (v2 strict: check login OR json)
    login_code = curl_code(f"{proto}://{domain}/wp-login.php")
    json_code = curl_code(f"{proto}://{domain}/wp-json/")
    is_wp = login_code not in ["000","404",""] or json_code not in ["000","404",""]

    if not is_wp: return {"domain":domain, "is_wp":False}

    score = 1  # WordPress detected
    findings = ["wordpress"]

    # Users (v2 pattern: parse JSON, check list length)
    body, _ = curl_raw(f"{proto}://{domain}/wp-json/wp/v2/users")
    try:
        data = json.loads(body.decode())
        if isinstance(data, list) and len(data) > 0:
            findings.append(f"wp_users_{len(data)}")
            score += 2
    except: pass

    # CORS (v2 pattern: explicit -I header check)
    cmd = ["curl","-sk","-m","8","-I","-H","Origin: https://evil.com",
           f"{proto}://{domain}/wp-json/wp/v2/users"]
    r = subprocess.run(cmd, capture_output=True, timeout=10)
    hdrs = r.stdout.decode().lower()
    acao = [l.split(":",1)[1].strip() for l in hdrs.split('\n') if 'access-control-allow-origin:' in l]
    acac = [l.split(":",1)[1].strip() for l in hdrs.split('\n') if 'access-control-allow-credentials:' in l]
    if acao and "evil.com" in acao[0] and acac and acac[0] == "true":
        findings.append("cors_credentialed")
        score += 3

    # XMLRPC (v2 pattern: system.listMethods, check for multicall string)
    xml = '<?xml version="1.0"?><methodCall><methodName>system.listMethods</methodName></methodCall>'
    body, _ = curl_raw(f"{proto}://{domain}/xmlrpc.php", method="POST", data=xml)
    txt = body.decode()
    if "system.multicall" in txt:
        findings.append("xmlrpc_multicall")
        score += 3
    elif "methodName" in txt:
        findings.append("xmlrpc_active")

    # Open registration (v2 pattern: three-string check avoids false positives)
    body, _ = curl_raw(f"{proto}://{domain}/wp-login.php?action=register")
    rt = body.decode().lower()
    if "register" in rt and "user_login" in rt and "wp-submit" in rt:
        findings.append("registration_open")
        score += 2

    # Severity (v2 thresholds: >=8 CRITICAL, >=5 HIGH, >=3 MEDIUM, >=1 LOW)
    if score >= 8: severity = "CRITICAL"
    elif score >= 5: severity = "HIGH"
    elif score >= 3: severity = "MEDIUM"
    elif score >= 1: severity = "LOW"
    else: severity = "NONE"

    return {"domain":domain, "severity":severity, "score":score, "findings":findings}

# Run with ThreadPoolExecutor
targets = [(d.strip(), s.strip()) for line in open("targets.txt") if (p := line.split("|")) and (d:=p[0]) and (s:=p[-1] if len(p)>2 else "unknown")]
with concurrent.futures.ThreadPoolExecutor(max_workers=20) as ex:
    futures = {ex.submit(test_target, t[0]): t for t in targets}
    for f in concurrent.futures.as_completed(futures):
        r = f.result()
        if r and r.get("score",0) > 0:
            print(f"[{r['severity']:>8}] {r['domain']:40s} | {r['score']:2d} | {', '.join(r['findings'])}")

Pitfalls

  • SPA catch-all false positives: Single-page apps return 200 for every path. Always verify .env has DB_/APP_/_KEY/_SECRET patterns; .git/config has [core]; SQL files have CREATE TABLE/INSERT INTO. Skip bodies with <html or <script in first 100 chars.
  • Cloudflare/WAF blocking: httpx may show tech as "Cloudflare" but WP is behind it. Try HTTP/1.0 for WP Engine-hosted sites: curl --max-time 30 --connect-timeout 10 -sk --http1.0 "https://TARGET/wp-json/..."
  • Rate limiting: WP Engine and Hostinger throttle after ~50 requests. Use 2-4s jitter between requests. Chrome/125 UA has 0% block rate; curl/8.4 UA has 5% block rate; Python urllib has 15%.
  • WordPress on subpaths: Check justified candidates such as /blog/ and /wp/ in addition to the root; secondary installations can have different versions and controls.
  • Non-standard XMLRPC paths: Some hosts rename xmlrpc.php. Verify with system.listMethods (not just HTTP 200) — look for <string> tags in response XML.
  • Registration form false positives: Many sites show login form on ?action=register without actually allowing registration. The v2 check requires ALL THREE strings: register + user_login + wp-submit.

Real-World Results (from 600+ US targets)

FindingFrequencyBest Sector
WP user enumeration~9% (55/600)Landscaping, Law Firms
Sensitive files (3+)~7% (41 sites)Auto Body, Window Cleaning
CORS credential reflection~3.3% (20+ sites)Law Firms, Real Estate
XMLRPC system.multicall~1.7% (10+ sites)HVAC, Landscaping
PHPInfo/info.php exposed~1.7% (~10 sites)Dental, Gyms
MySQL 3306 exposed0.17% (1 site)Healthcare SaaS

WordPress = 36.5% of all US SMB targets. All CORS/XMLRPC vulns occur EXCLUSIVELY on WordPress.

Verification

  • Every CORS finding must show Access-Control-Allow-Credentials: true in curl -I response headers.
  • Every source leak must pass content verification (not just HTTP 200). Skip HTML/SPA responses.
  • Every XMLRPC finding must have system.listMethods response containing <string> method names.
  • Score targets with v2 thresholds: WP=+1, users (+2), CORS=+3, XMLRPC multicall=+3, open reg=+2. Score >=6 = deep-dive candidate (Phase 3).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wp Mass Recon AI skill do?

Batch WP recon: users, CORS, XMLRPC, leaks across domains.

Why use Wp Mass Recon on TypingMind?

Because you install it once and use it with any model. Wp Mass 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 Wp Mass Recon in TypingMind?

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

Which AI models can use Wp Mass 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 Wp Mass Recon?

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

Is the Wp Mass 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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