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:
/metricsreturns Prometheus text format (lines starting with# HELPor# TYPE) - Actuator exposure:
/actuator/healthreturns JSON with component statuses - False positive: Endpoint returns
{"status":"ok"}only (minimal health check, not a metrics leak) - Severity upgrade: If
/actuator/envor/actuator/configpropsis 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-enumerationfor full target profile
Verification
Run this self-test to confirm metrics-exposure hunting readiness:
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"Category check — confirm the skill has a category:
grep -q "category:" SKILL.md && echo "PASS: category present" || echo "FAIL"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,/readyendpoints are designed to be public. Need leaked internal data.