This article details how the Escape research team identified over 2,000 vulnerabilities in more than 5,600 applications built with vibe coding platforms. It explains their methodology, which included data gathering, attack surface scanning, and the introduction of the Visage Surface Scanner to analyze frontend code for security weaknesses.

This article analyzes the security of over 20,000 web applications generated by large language models (LLMs). It identifies common vulnerabilities, such as hardcoded secrets and predictable credentials, while highlighting improvements in security compared to earlier AI-generated code.