The AI Safety Mirage: When Model Breaches Become a Data Problem

The data shows a disconnect. Over the past three quarters, major AI labs have reported a 47% increase in successful adversarial attacks against their flagship models. Yet, the public narrative remains focused on capability benchmarks and parameter counts. This is the same mistake we made in DeFi in 2020. We celebrated total value locked while ignoring the fact that 15% of yield was being extracted by bots exploiting front-running vulnerabilities. The current AI security crisis is not a software bug. It is a structural failure of testing methodology, and the industry is only beginning to admit it.