RESEARCHER TESTS IF LLMS CAN HACK VULNERABLE APPS

The researcher created a deliberately flawed app and deployed multiple LLMs against it to measure their hacking effectiveness. The $1,500 budget covered API costs and testing infrastructure across different AI models. Results showed varying levels of success depending on the model used and vulnerability type. Some LLMs demonstrated ability to identify and exploit common security flaws, while others struggled with more complex attack chains. The experiment highlights both the potential and limitations of AI in security testing. It suggests LLMs could serve as tools for identifying vulnerabilities, though they don't yet match skilled human hackers. The findings also underscore the importance of defense mechanisms against AI-assisted attacks. The research generated significant discussion in developer communities about AI security implications and practical applications for vulnerability assessment.