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Insecure Defaults

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waybarrios
insecure-defaults

Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.

Overview

Publisherwaybarrios
Repositoryopencode-power-pack
Skill nameinsecure-defaults
Stars
535
Forks
43
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 waybarrios on GitHub. Read the source before you install it.

Installation

Install the Insecure Defaults 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.
    https://github.com/waybarrios/opencode-power-pack/tree/main/skills/insecure-defaults
  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/waybarrios/opencode-power-pack.git /tmp/opencode-power-pack
mkdir -p .claude/skills
cp -r /tmp/opencode-power-pack/skills/insecure-defaults .claude/skills/insecure-defaults
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Insecure Defaults 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 Insecure Defaults 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 Insecure Defaults 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.

Insecure Defaults Detection

Finds fail-open vulnerabilities where apps run insecurely with missing configuration. Distinguishes exploitable defaults from fail-secure patterns that crash safely.

  • Fail-open (CRITICAL): SECRET = env.get('KEY') or 'default' → App runs with weak secret
  • Fail-secure (SAFE): SECRET = env['KEY'] → App crashes if missing

When to Use

  • Security audits of production applications (auth, crypto, API security)
  • Configuration review of deployment files, IaC templates, Docker configs
  • Code review of environment variable handling and secrets management
  • Pre-deployment checks for hardcoded credentials or weak defaults

When NOT to Use

Do not use this skill for:

  • Test fixtures explicitly scoped to test environments (files in test/, spec/, __tests__/)
  • Example/template files (.example, .template, .sample suffixes)
  • Development-only tools (local Docker Compose for dev, debug scripts)
  • Documentation examples in README.md or docs/ directories
  • Build-time configuration that gets replaced during deployment
  • Crash-on-missing behavior where app won't start without proper config (fail-secure)

When in doubt: trace the code path to determine if the app runs with the default or crashes.

Rationalizations to Reject

  • "It's just a development default" → If it reaches production code, it's a finding
  • "The production config overrides it" → Verify prod config exists; code-level vulnerability remains if not
  • "This would never run without proper config" → Prove it with code trace; many apps fail silently
  • "It's behind authentication" → Defense in depth; compromised session still exploits weak defaults
  • "We'll fix it before release" → Document now; "later" rarely comes

Workflow

Follow this workflow for every potential finding:

1. SEARCH: Perform Project Discovery and Find Insecure Defaults

Determine language, framework, and project conventions. Use this information to further discover things like secret storage locations, secret usage patterns, credentialed third-party integrations, cryptography, and any other relevant configuration. Further use information to analyze insecure default configurations.

Example Search for patterns in **/config/, **/auth/, **/database/, and env files:

  • Fallback secrets: getenv.*\) or ['"], process\.env\.[A-Z_]+ \|\| ['"], ENV\.fetch.*default:
  • Hardcoded credentials: password.*=.*['"][^'"]{8,}['"], api[_-]?key.*=.*['"][^'"]+['"]
  • Weak defaults: DEBUG.*=.*true, AUTH.*=.*false, CORS.*=.*\*
  • Crypto algorithms: MD5|SHA1|DES|RC4|ECB in security contexts

Tailor search approach based on discovery results.

Focus on production-reachable code, not test fixtures or example files.

2. VERIFY: Actual Behavior

For each match, trace the code path to understand runtime behavior.

Questions to answer:

  • When is this code executed? (Startup vs. runtime)
  • What happens if a configuration variable is missing?
  • Is there validation that enforces secure configuration?

3. CONFIRM: Production Impact

Determine if this issue reaches production:

If production config provides the variable → Lower severity (but still a code-level vulnerability) If production config missing or uses default → CRITICAL

4. REPORT: with Evidence

Example report:

Finding: Hardcoded JWT Secret Fallback
Location: src/auth/jwt.ts:15
Pattern: const secret = process.env.JWT_SECRET || 'default';

Verification: App starts without JWT_SECRET; secret used in jwt.sign() at line 42
Production Impact: Dockerfile missing JWT_SECRET
Exploitation: Attacker forges JWTs using 'default', gains unauthorized access

Quick Verification Checklist

Fallback Secrets: SECRET = env.get(X) or Y → Verify: App starts without env var? Secret used in crypto/auth? → Skip: Test fixtures, example files

Default Credentials: Hardcoded username/password pairs → Verify: Active in deployed config? No runtime override? → Skip: Disabled accounts, documentation examples

Fail-Open Security: AUTH_REQUIRED = env.get(X, 'false') → Verify: Default is insecure (false/disabled/permissive)? → Safe: App crashes or default is secure (true/enabled/restricted)

Weak Crypto: MD5/SHA1/DES/RC4/ECB in security contexts → Verify: Used for passwords, encryption, or tokens? → Skip: Checksums, non-security hashing

Permissive Access: CORS *, permissions 0777, public-by-default → Verify: Default allows unauthorized access? → Skip: Explicitly configured permissiveness with justification

Debug Features: Stack traces, introspection, verbose errors → Verify: Enabled by default? Exposed in responses? → Skip: Logging-only, not user-facing

For detailed examples and counter-examples, see examples.md.

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 Insecure Defaults AI skill do?

Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.

Why use Insecure Defaults on TypingMind?

Because you install it once and use it with any model. Insecure Defaults 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 Insecure Defaults in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/waybarrios/opencode-power-pack/tree/main/skills/insecure-defaults. 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 Insecure Defaults?

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 Insecure Defaults?

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

Is the Insecure Defaults AI skill free?

Yes. It is published on GitHub by waybarrios 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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