Improving Software Fuzzing with AI and ML

What if we could achieve completely ‘contactless’ software security scanning? As the lines between physical and digital security become blurrier and blurrier, software quality standards and testing methodologies must continue to keep pace. Software fuzzing has long been a trusted method for finding vulnerabilities that are difficult to discover using traditional methods.

The application of AI and ML to this field has already begun to bear very promising results. By leveraging deep learning techniques to improve our input corpus and better understand our program's states, we can shine areas on the code logic that would be hidden by approaches like vulnerability scanning and static code analysis, and even traditional software fuzzing.