Academic research shows LLM-assisted vulnerability discovery works—IRIS achieves 2.5x improvement over CodeQL, Google's Big Sleep found a critical SQLite zero-day. But can it work at production scale? FENRIR has discovered 100+ vulnerabilities across AI infrastructure since mid-2025, with 21 CVEs patched including multiple CVSS 9.8 RCEs. This talk presents FENRIR's multi-stage verification pipeline: static analysis pre-triage, two-layer LLM validation (L1 prune → L2 deep-verify), and confidence-based human routing. We'll cover what worked (research-backed context generation, CWE-specific agents, pattern recognition for bypass detection), what failed (pure automation's false positives, generic prompts, insufficient context), and the hybrid model that emerged. Live demo: FENRIR analyzing AI framework code and surfacing candidates for human triage.