Definition
Take all scanner findings ingested in a window. Count the ones whose correlated_threat was predicted at design review. Of those, count the subset where no Layer 2 guardrail blocked the write. Divide. That's the escape rate.
escape_rate = ( findings_with_predicted_threat_AND_no_blocking_guardrail )
÷
( findings_with_predicted_threat )
example: 14 escapes ÷ 102 findings linked to a prediction → 13.7%Why it's the metric
Scan pass rates conflate "we don't have a problem" with "we don't see a problem." Total finding count rewards noisy scanners. Escape rate isolates the failure mode that actually matters: the prediction was right, the threat was real, and the guardrail still missed the write. Lower escape rate = your guardrails are doing their job. Rising escape rate = a new attack pattern is sneaking past you.
Category 30d escape rate Δ vs prior 30d injection 8.2% −1.4 pp secret_exposure 0.4% −0.2 pp scope_escalation 21.3% +6.1 pp ⚠ dependency_supply_chain 2.1% flat cryptographic_weakness 4.8% −0.6 pp
What you do with it
When a category climbs, the Triage & Feedback agent has already drafted candidate guardrail tightenings against the escape findings; the dashboard surfaces them with replay-test results from the last 30 days of writes. You decide whether to merge, modify, or defer. The metric closes the loop between findings, guardrails, and outcomes.