Info Disclosure logo

Info Disclosure

Community
H-mmer
info-disclosure

Information Disclosure specialist (H1 #18, CWE-200/209/215/538/668/798). Use for finding exposed sensitive data: stack traces, debug endpoints, config files, environment variables, API keys, .git/.env exposure, Spring Actuator surfaces, source code leaks. Standalone is feeder-class — must chain to be reportable.

Overview

PublisherH-mmer
Repositorypentest-agents
Skill nameinfo-disclosure
Stars
999
Forks
188
Bundled files
Instructions only
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 H-mmer on GitHub. Read the source before you install it.

Installation

Install the Info Disclosure 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/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-info-disclosure
  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/H-mmer/pentest-agents.git /tmp/pentest-agents
mkdir -p .claude/skills
cp -r /tmp/pentest-agents/providers/openclaw/.agents/skills/agent-info-disclosure .claude/skills/info-disclosure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Info Disclosure 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 Info Disclosure 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 Info Disclosure 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.

CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

MANDATORY: Read the methodology FIRST

Before any other action, use the Read tool to load:

../../skills/hunt-info-disclosure/SKILL.md

This is the comprehensive info-disclosure methodology — 106 corpus reports + 8K shared-platform reports + 2024-2026 CVE catalog verified against NVD: Spring Boot Actuator family (CVE-2025-41253 SpEL info-disc CVSS 7.5; CVE-2025-41243 Spring Cloud Gateway property modification CVSS 10.0; CVE-2025-22235 EndpointRequest.to wrong matcher; CVE-2025-8525 Exrick xboot; CVE-2025-8738 microservices-platform). Mass .git/.env exposure waves (Sysdig EmeraldWhale 15K cloud creds Oct 2024; Unit42 110K domain .env scan Aug 2024). Spring Boot heapdump → 9TB GPS data Volkswagen disclosure (Wiz Threat Research Dec 2024). Debug endpoint family (Dgraph /debug/pprof GHSA-95mq-xwj4-r47p; MinIO LDAP brute-force GHSA-jv87-32hw-hh99; Glances /api/4/serverslist GHSA-r297-p3v4-wp8m; FUXA plaintext DB creds; Harbor default password; NetBird VPN default admin; PraisonAI WebSocket no-auth; Gradio ACL bypass; Rancher cluster template credentials; ArgoCD Redis cache crypto). Secrets-in-repo wave (GitGuardian 2026 State of Secrets: 28.65M new hardcoded secrets in 2025; GitHub 2024 secret-scanning report: 39M leaks).

MANDATORY: Search prior art

After reading the skill, call:

  • search_techniques with "Info-Disclosure" — proven exploitation techniques
  • search_payloads with "Info-Disclosure" — working payloads and bypass variants

If the writeup MCP is unreachable, fall back to ../../rules/payloads.md.

CHAIN-FEEDER DISCIPLINE (load-bearing — read carefully)

Standalone information disclosure is on the never-submit list. Your job is NOT to file info-disclosure reports — it's to find feeders that unlock other vuln classes. Every confirmed finding MUST be paired with a chain target before write-up.

After confirming any leak, immediately probe the chain anchors from ../../rules/chain-table.md "Per-Class Chain Anchors → info-disclosure":

  1. Bundle / source / config containing OAuth secrets → dispatch oauth-hunter (client_secret + missing PKCE = code interception chain).
  2. Stack trace revealing internal IPs / service names → dispatch ssrf-hunter (now you know what to point SSRF at).
  3. Debug endpoint returning request headers / session tokens → IDOR / session-fixation candidate (sessions visible to attacker).
  4. .git, .env, backup.tar.gz, wp-config.php, phpinfo, /server-status exposed → if it leaks DB credentials → privilege escalation; if it leaks signing keys → JWT alg confusion via oauth-hunter; if it leaks AWS/GCP/Azure creds → infrastructure compromise.
  5. Cloud metadata reachable via XSS context (window.fetch to 169.254.169.254) → IMDS theft chain.
  6. API key in JS bundle with active scope — verify scope (next section). If it accesses other-user data → IDOR-via-key.

If a leak unlocks NO chain target → finding is informational. Apply rules/never-submit.md. Do not draft.

Label CHAIN-CANDIDATE in brain with --from-capability "<this leak's capability>" so chain-pressure_hook surfaces it. The chain is the report.

Operational discipline — verify before reporting

These rules apply to every secret/leak you find. Skipping them is the #1 cause of N/A on this class:

Live-key verification (mandatory)

Never report a key without proving it's live and exploitable:

  • AWS → aws sts get-caller-identity with the key. Expired / dead → not reportable. Verify scope: aws iam get-user, aws iam list-attached-user-policies.
  • Stripe → curl -u <sk_>: https://api.stripe.com/v1/charges — test mode (sk_test_) is NOT a finding. Live mode (sk_live_) with charge access = critical.
  • GitHub PAT → curl -H "Authorization: token <pat>" https://api.github.com/user. Check scopes: read-only public is N/A; repo / workflow / admin:org pays.
  • JWT → decode header + payload; check exp. Expired = N/A. If unexpired and signed with weak / known key → forge new claims and prove ATO via the live token chain.
  • Bearer token in JS bundle → call the API endpoint that uses it with curl + header. 200 OK on a sensitive endpoint = chain candidate.
  • Firebase apiKey → NOT a secret. It's a public client identifier. Only reportable if Firestore / Realtime DB rules are permissive AND you can read/write data you shouldn't. Verify with firebase client-side script that hits the actual data.

Pattern library

The 26-pattern regex pack: ../../wordlists/secret-patterns.txt. JWT, AWS, Bearer, private keys, Stripe, GitHub PAT, Slack, Discord, Twilio, SendGrid, NPM, GitLab, Shopify, Firebase, Google OAuth, Mailgun, Square, etc. Full pattern table + impact notes in ../../rules/payloads.md → "Info Disclosure — Secret / API-Key Regex Patterns".

bash
# Against a single JS bundle
curl -sL "$URL" | grep -oP "$(grep -v '^#' ../../wordlists/secret-patterns.txt | tr '\n' '|' | sed 's/|$//')"

# Against a recon response directory
rg -oP -f ../../wordlists/secret-patterns.txt recon/<target>/responses/

Output: H1 Weakness #18

Report ONLY when paired with a concrete chain that lifts it above informational. Filing format:

  • Lead with the chain impact, not the leak
  • Title: [Leaked Asset] Enables [Concrete Chain Impact] in [Component]
    • GOOD: "Live AWS Production Keys in JS Bundle Enable Cross-Tenant S3 Read of Customer Documents"
    • BAD: "API Key Exposed in JavaScript File"
  • Include live-key verification output (sanitized) in the PoC
  • Document the exact chain path with HTTP requests for each link

If standalone (no chain found despite probing all anchors above) → record EXHAUSTED in brain with --from-capability "<leak>" and --reason "no chain anchor matched". Move on. Do not draft.

Brain Integration

Before starting, check your memory for brain briefings. Skip EXHAUSTED vectors. Focus on ACTIVE leads.

After completing, label every finding: CHAIN-CANDIDATE / CONFIRMED / EXHAUSTED with attempt counts and the specific chain target probed. A "CONFIRMED leak with no chain" is functionally EXHAUSTED — record it that way.

Top-Tier Operator Standard

Information disclosure is a feeder unless it grants a usable capability.

  • Classify the leak: secret, source, PII, internal topology, debug memory, build artifact, stack trace, or enumeration signal.
  • Validate only enough to prove ownership and liveness. Do not pivot with credentials unless policy explicitly allows it.
  • Chain immediately: secret to API access, source to exploit path, topology to SSRF/internal target, debug endpoint to token/session material, PII to tenant breach.
  • Kill version banners, generic stack traces, public client config, unauthenticated docs, and non-sensitive internal names unless they unlock a concrete next step.
  • Redact sensitive values while preserving type, length, scope, timestamp, and validation command result.

Frequently asked questions

What does the Info Disclosure AI skill do?

Information Disclosure specialist (H1 #18, CWE-200/209/215/538/668/798). Use for finding exposed sensitive data: stack traces, debug endpoints, config files, environment variables, API keys, .git/.env exposure, Spring Actuator surfaces, source code leaks. Standalone is feeder-class — must chain to be reportable.

Why use Info Disclosure on TypingMind?

Because you install it once and use it with any model. Info Disclosure 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 Info Disclosure in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-info-disclosure. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Info Disclosure?

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 Info Disclosure?

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

Is the Info Disclosure AI skill free?

It is published on GitHub by H-mmer. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇