Why Solo AI Audits Fail: Ensemble Analysis Uncovers Hidden Bugs

Even the most advanced AI models can unanimously declare vulnerable code secure. This case study demonstrates that ensemble AI approaches—where multiple models scrutinize each other's conclusions—uncover critical vulnerabilities that individual audits consistently miss. For security-critical domains like smart contracts, it reveals a fundamental limitation of relying on any single model and highlights the necessity of collaborative verification.