hunt-metrics-exposure

name	hunt-metrics-exposure
description	Hunt public /metrics, /health, and actuator endpoints leaking AI usage, DB pools, and operational intelligence.
version	1.1.0
revision_date	"2026-07-25T00:00:00.000Z"
license	MIT
category	redteam
tags	["metrics","exposure","hunt","redteam"]

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

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

# 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

# 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

Metric	Intelligence 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_active	Real-time user count for specific features
http_requests_total	Request volume, peak hours, user activity
app_version / build_info	Deployed 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:

   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:

   grep -q "category:" SKILL.md && echo "PASS: category present" || echo "FAIL"
   

3. Pitfalls section — confirm pitfalls are documented:

   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.