High-Risk Flaws in AI and LLM Applications Reach 2.7 Times Overall Average

Companies are rapidly adopting large language models (LLMs) and AI applications, improving operational efficiency and security testing while also expanding attack surfaces such as prompt injection, data leakage and access-control failures. Security company Cobalt said AI systems are emerging as a new risk for corporate cybersecurity governance, making expert human validation indispensable.

Cobalt’s latest penetration-testing report found that high-risk vulnerabilities accounted for 32% of flaws in AI applications, 2.7 times the average across all applications. Nearly 80% of respondents also said automated tools had missed important vulnerabilities, indicating that AI can currently accelerate testing but cannot yet replace cybersecurity professionals.