Credential Exposure Audit logo

Credential Exposure Audit

Organization
zero-lab-ai
credential-exposure-audit

Audits supplied evidence when passwords, hashes, private keys, browser or vault entries, cloud CLI credentials, application secrets, tokens, or credential-bearing artifacts may be exposed. Use for defensive review that must distinguish indicators from material and validity from privilege without decrypting, cracking, replaying, or authenticating.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namecredential-exposure-audit
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

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

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Credential Exposure Audit 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/zero-lab-ai/skills/tree/main/plugins/identity-security/skills/credential-exposure-audit
  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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/identity-security/skills/credential-exposure-audit .claude/skills/credential-exposure-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Credential Exposure Audit 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 Credential Exposure Audit 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 Credential Exposure Audit 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.

Credential exposure audit

A credential-shaped string, record, file, or log entry does not by itself prove usable material. Keep material presence separate from current validity, identity, privilege, and reachable systems. Preserve raw values in restricted evidence; the audit contains redacted references only.

This skill reviews supplied evidence and protection controls. It does not collect new credential material, decrypt stores, inspect unsupplied memory, crack hashes, authenticate, replay tokens, test destinations, or plan lateral movement.

When to Use

  • Audit credential exposure for authorized security work.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record authorization, exact assets, business context, the supplied-evidence boundary, restricted evidence location, retention, and disposition owner. Use unknown or null when a handling fact was not supplied; do not invent a vault path, owner, retention period, or asset purpose.
  2. Inventory each evidence item with its asset, locator, class, access state, protection, integrity hash for copied artifacts, and exact time or honest bounded collection window. Never invent acquisition precision or a digest. If content is described but no copied artifact and hash were supplied, use the matching metadata or configuration evidence kind and state the integrity gap. Never use repeated hexadecimal characters as a stand-in digest. Do not assign evidence to a host because its content names that host or a URL. When the source asset is not supplied, use an other asset named Unattributed supplied evidence and record the provenance gap.
  3. Keep raw secrets out of the audit. Use a restricted redacted reference and a disposition record for every observed or redacted secret-bearing item.
  4. Classify what the evidence establishes: indicator, credential reference, protected material, redacted material, direct observation, potential container, or expected credential mechanism.
  5. Record exposure separately: protected, owner-accessible, broadly accessible, historical broad access, unknown, or not an exposure. A variable name, masked value, or uninspected dump is not confirmed material exposure.
  6. Record freshness, longevity, identity binding, privilege, and reachability as separate facts. Mark them verified only from administrative, runtime, or configuration evidence; never infer them from a filename, account label, or credential format.
  7. Bind each protection control to evidence and state its limitation. Encryption, file permissions, masking, short lifetime, process protection, and storage ACLs reduce different risks and cannot clear unrelated surfaces.
  8. Cover memory, local stores, browser and vault data, private keys, cloud caches, application configuration, histories and logs, process environments, workload identity, retained artifacts, authorization, and handling. Blocked work stays visible and receives a reason.
  9. Link every medium-or-higher observation to an owned action and verification method. Administrative inventory can close identity, validity, privilege, and last-use gaps without authenticating with the credential.

Interpretation rules

  • Encrypted material proves a protected object exists. It does not prove decryptability, plaintext knowledge, current validity, or successful use.
  • Owner-only access limits local readers. It does not establish that the owner is uncompromised or that a remote credential is inactive.
  • A private-key header proves key material is present only when the supplied artifact supports that claim. It does not establish passphrase state, trust destinations, or validity.
  • A masked CI value or environment-variable name is an indicator. It does not prove the underlying value appeared in the evidence.
  • An intended short-lived workload token is a mechanism, not a finding. Audience, RBAC, runtime issuance, and pod access remain separate questions.
  • A crash dump or memory image is a potential container until inspected under separate authority. Restricting it does not prove it contains credentials.
  • Rotation can be precautionary when validity is unknown. Describe it that way; do not rewrite precaution as confirmed compromise.

Boundary with adjacent skills

  • Use password-audit-plan for authorized password-strength or cracking policy.
  • Use kerberos-exposure-audit for directory Kerberos configuration and ticket exposure controls.
  • Use cloud-attack-path after administrative evidence binds a credential to an identity and permissions.
  • Use protocol-traffic-analysis for authentication material observed in packet captures.
  • Use findings-report to turn verified audit observations into final findings.

Rationalizations to reject

  • "It looks like a credential, so it is valid." Format establishes neither issuance nor current administrative state.
  • "The command names that server, so the history came from it." A destination in evidence is not the source of that evidence. Keep the source unattributed until collection provenance binds it.
  • "The username says admin, so the privilege is high." Names are inferred identity and privilege until authoritative records bind them.
  • "The value was masked, so the secret definitely existed." Masking output can be static text; record an indicator until the source is established.
  • "Mode 0600 means there is no exposure." It limits filesystem readers while leaving owner compromise, lifetime, remote permissions, and retention open.
  • "The token expires in an hour, so the workload is safe." Lifetime does not prove audience, RBAC, pod isolation, or absence of theft during that hour.
  • "Rotate everything immediately." Rotation can disrupt production and destroy evidence. Give it an owner, authorization state, reason, and verification method.
  • "The artifact was supplied, so I can add a placeholder hash." Artifact availability and digest availability are separate facts. Record the integrity gap unless a digest was supplied or actually computed from the artifact.
  • "We can test it once to learn the scope." Authentication is a separate action with detection, lockout, state-change, and authorization consequences.

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 Credential Exposure Audit AI skill do?

Audits supplied evidence when passwords, hashes, private keys, browser or vault entries, cloud CLI credentials, application secrets, tokens, or credential-bearing artifacts may be exposed. Use for defensive review that must distinguish indicators from material and validity from privilege without decrypting, cracking, replaying, or authenticating.

Why use Credential Exposure Audit on TypingMind?

Because you install it once and use it with any model. Credential Exposure Audit 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 Credential Exposure Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/identity-security/skills/credential-exposure-audit. 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 Credential Exposure Audit?

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 Credential Exposure Audit?

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

Is the Credential Exposure Audit AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 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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