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Skillshunt-metrics-exposure

hunt-metrics-exposure

Security & auditing

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Updated 10-16 03:04

About this skill

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

```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

# 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.