credential-exposure-audit

credential-exposure-audit is a skill for Claude Code, Codex from zero-lab-ai/skills. It costs 67 tokens per session (1,392 once invoked), scanned A, original, Apache-2.0.

A passive audit guide for evidence that may contain exposed passwords, hashes, private keys, browser or vault entries, cloud credentials, secrets, or tokens.

In plain words
What is it for?
Use it to review supplied evidence, handling controls, protection measures, and the difference between an indicator and usable credential material.
Why use it?
It helps distinguish the presence of credential-like material from whether it is valid, privileged, or usable, without decrypting, cracking, or using it.

Skill for Claude CodeCodex

Written for Claude Code and Codex: allowed-tools in frontmatter, but also agents/openai.yaml present.

Part of the identity-security plugin — 8 skills shipped together

Good fit Use it to review supplied evidence, handling controls, protection measures, and the difference between an indicator and usable credential material.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zero-lab-ai/skills/credential-exposure-audit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add zero-lab-ai/skills --skill credential-exposure-audit
Clone the repo
git clone --depth 1 https://github.com/zero-lab-ai/skills

Made for: Claude Code, Codex.

Or install identity-security, the plugin that ships this one along with the rest of its 8 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for credential-exposure-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/zero-lab-ai/skills/credential-exposure-audit/github.svg)](https://agentmods.dev/skills/zero-lab-ai/skills/credential-exposure-audit)
Your own site
<a href="https://agentmods.dev/skills/zero-lab-ai/skills/credential-exposure-audit"><img src="https://agentmods.dev/badge/skills/zero-lab-ai/skills/credential-exposure-audit/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for credential-exposure-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/zero-lab-ai/skills/credential-exposure-audit"><img src="https://agentmods.dev/badge/skills/zero-lab-ai/skills/credential-exposure-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,392 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.01392
Opus 5.5 $0.00027 $0.00557
Sonnet 5.5 $0.00013 $0.00278
Haiku 4.5 $0.00007 $0.00139

Measured yesterday against content hash 8b8d3d4ed176, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

credential-exposure-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/identity-security/skills/credential-exposure-audit/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 117 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 117 lines · 67 tokens per session scan A 8b8d3d4ed176

Subscribe to this mod's changes

credential-exposure-audit is a skill published in the GitHub repository zero-lab-ai/skills (283 stars, last pushed 12d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,392 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-26.

Related

Other skills, from other repositories

performing-ai-assisted-vulnerability-discovery

Using LLMs to accelerate vulnerability research and pentest workflows — generating syntax-valid fuzzing seeds and evolving grammars, fine-tuned mutation dictionaries, parallel agent-based proof-of-vulnerability generation, and evidence-driven passive analysis of real HTTP traffic via the Burp MCP server. Covers…

xalgorix/xalgorix · 91 tokens

testing-llm-prompt-injection-and-jailbreaks

Testing LLM-backed applications, chatbots, and AI agents for direct and indirect prompt injection, jailbreaks, system-prompt leakage, and tool/agent abuse during authorized penetration tests, using structured payload families and reliable confirmation signals.

xalgorix/xalgorix · 60 tokens

bypassing-authentication-with-forced-browsing

Discovering and accessing unprotected pages, APIs, and administrative interfaces by enumerating URLs and bypassing authentication controls during authorized security assessments.

26zl/cybersec-toolkit · 39 tokens

analyzing-ios-app-security-with-objection

Performs runtime mobile security exploration of iOS applications using Objection, a Frida-powered toolkit that enables security testers to interact with app internals without jailbreaking. Use when assessing iOS app security posture, bypassing client-side protections, dumping keychain items, inspecting filesystem…

26zl/cybersec-toolkit · 99 tokens

conducting-full-scope-red-team-engagement

Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities.

26zl/cybersec-toolkit · 44 tokens

exploiting-ai-model-file-rce

Testing machine-learning model files and model-loading services for remote code execution caused by insecure deserialization (pickle/PyTorch), unsafe config instantiation (Hydra), archive path traversal, and dangerous layer types during authorized penetration tests of AI/ML pipelines.

xalgorix/xalgorix · 59 tokens