FrameShift: Learning to Resize Fuzzer Inputs without Breaking Them

Coverage-guided fuzzers are powerful automated bug-finding tools. They mutate program inputs, observe coverage, and save any input that hits an unexplored path for future mutation. Unfortunately, without knowledge of input formats--for example, the relationship between formats' data fields and...

A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection

Various deep learning DL methods have recently been utilized to detect software vulnerabilities. Real-world software vulnerability datasets are rare and hard to acquire, as there is no simple metric for classifying vulnerability. Such datasets are heavily imbalanced, and none of the current...