AI Models Have 30% False Positive Rate Making Them Dangerous for Defense Applications

What was said

"The false positive rate on Mythos was 30%. So it thought it found something, but it hadn't. It's great for attack, it's horrible for defense. It finds 30 times— 30% of the time it finds something."

Arora exposed a critical weakness in frontier AI models that the industry rarely discusses: Mythos had a 30% false positive rate when detecting vulnerabilities. He warned this makes models effective for offensive cyber operations but dangerous for defensive applications, as enterprises can't afford to chase phantom vulnerabilities or make decisions with such high error rates. The revelation challenges the notion that these models are ready for critical business applications without significant additional engineering.