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Hunt Metrics Exposure

CommunityPopular
uphiago
hunt-metrics-exposure

Hunt public /metrics, /health, and actuator endpoints leaking AI usage, DB pools, and operational intelligence.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill namehunt-metrics-exposure
Stars
1.3K
Forks
219
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 Hunt Metrics Exposure 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/uphiago/recon-skills/tree/main/redteam/hunt-metrics-exposure
  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/redteam/hunt-metrics-exposure .claude/skills/hunt-metrics-exposure
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Metrics Exposure 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 Hunt Metrics Exposure 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 Hunt Metrics Exposure 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.

When to Use

The target uses modern observability tooling (Go, .NET, Java, Node.js). These frameworks often expose /metrics, /health, and /status endpoints that are forgotten behind auth. Unlike application data leaks, metrics leaks reveal the ENTIRE operational profile: which AI models are used, how many users are active, database connection exhaustion, and third-party service dependencies.


Phase 1 — Discover Metrics Endpoints

bash
TARGET="https://target.com"

# Common observability paths
for ep in metrics health status ready live readyz healthz \
  actuator/health actuator/metrics actuator/prometheus \
  Telescope telescope horizon debug; do
  code=$(curl --max-time 30 --connect-timeout 10 -sk -o /tmp/metrics_${ep}.txt -w "%{http_code}" \
    "${TARGET}/${ep}" 2>/dev/null)
  if [ "$code" = "200" ]; then
    size=$(wc -c < /tmp/metrics_${ep}.txt)
    echo "  /${ep}: HTTP 200 (${size} bytes)"
  fi
done

Phase 2 — Analyze Prometheus Metrics

bash
# Count unique metric families (each reveals a subsystem)
grep -c '^# HELP' /tmp/metrics_metrics.txt

# Extract AI/ML model usage
grep -i 'ai_\|model\|llm\|openai\|gemini\|copilot' /tmp/metrics_metrics.txt

# Extract database pool states
grep -i 'db_pool\|database\|connection' /tmp/metrics_metrics.txt

# Extract third-party dependencies
grep -i 'stripe\|openai\|sendgrid\|twilio\|email' /tmp/metrics_metrics.txt

# Extract request volumes (user activity)
grep -i 'http_request\|api_request\|grpc_request' /tmp/metrics_metrics.txt

# Extract circuit breaker states (service health)
grep -i 'circuit_breaker' /tmp/metrics_metrics.txt

Phase 3 — Analyze Health/Status Endpoints

bash
# Spring Boot Actuator
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/actuator/health" | python3 -m json.tool
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/actuator/metrics" | python3 -m json.tool
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/actuator/env" | python3 -m json.tool  # May leak env vars

# Custom health endpoints
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/health" | python3 -m json.tool
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/api/health" | python3 -m json.tool

# Laravel Telescope (if exposed)
curl --max-time 30 --connect-timeout 10 -sk "${TARGET}/telescope/requests" | head -c 500

What Metrics Reveal

MetricIntelligence Gained
ai_analyzer_output_total{model="gpt-5-mini"}Which AI models are used, usage volume
db_pool_idle_connections{pool="api"}Database pool sizing, connection exhaustion risk
circuit_breaker_state{client="stripe"}Third-party dependencies and their health
clinical_copilot_sse_activeReal-time user count for specific features
http_requests_totalRequest volume, peak hours, user activity
app_version / build_infoDeployed version, build timestamps

Verification

  • Confirmed exposure: /metrics returns Prometheus text format (lines starting with # HELP or # TYPE)
  • Actuator exposure: /actuator/health returns JSON with component statuses
  • False positive: Endpoint returns {"status":"ok"} only (minimal health check, not a metrics leak)
  • Severity upgrade: If /actuator/env or /actuator/configprops is exposed → CRITICAL (environment variables leaked)

What Next

  • AI model usage metrics → pivot to hunt-llm-ai (prompt injection on discovered models)
  • DB pool metrics showing overload → DoS attack surface identified
  • Circuit breaker states for Stripe/email → infrastructure dependency map for chained attacks
  • Combine with hunt-schema-enumeration for full target profile

Verification

Run this self-test to confirm metrics-exposure hunting readiness:

  1. Skill integrity — confirm the skill file is readable and well-formed:

    bash
    grep -q "name: hunt-metrics-exposure" SKILL.md && echo "PASS: skill frontmatter present" || echo "FAIL"
    grep -q "revision_date:" SKILL.md && echo "PASS: revision date present" || echo "FAIL"
  2. Category check — confirm the skill has a category:

    bash
    grep -q "category:" SKILL.md && echo "PASS: category present" || echo "FAIL"
  3. Pitfalls section — confirm pitfalls are documented:

    bash
    grep -q "^## Pitfalls" SKILL.md && echo "PASS: pitfalls section present" || echo "FAIL"

All 3 tests verify the skill is properly structured and ready for use.


Pitfalls

  • Prometheus /metrics without secrets — metrics endpoints exposing request counts are informational. Need labels containing PII, internal hostnames, or credentials.
  • Spring Boot Actuator /actuator/metrics — metrics are intentionally exposed for monitoring. Only report if they leak sensitive data (usernames in labels, internal IPs).
  • JMX without auth — JMX exposure without authentication is critical only if write operations (MBean invocation) are possible. Read-only JMX is informational.
  • Health endpoint without sensitive data — /health, /status, /ready endpoints are designed to be public. Need leaked internal data.

Frequently asked questions

What does the Hunt Metrics Exposure AI skill do?

Hunt public /metrics, /health, and actuator endpoints leaking AI usage, DB pools, and operational intelligence.

Why use Hunt Metrics Exposure on TypingMind?

Because you install it once and use it with any model. Hunt Metrics Exposure 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 Hunt Metrics Exposure in TypingMind?

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

Which AI models can use Hunt Metrics Exposure?

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 Hunt Metrics Exposure?

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

Is the Hunt Metrics Exposure 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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