Binary Vulnerability Similarity Detection Based on Function Parameter Dependency

Abstract

Many existing works compute the binary vulnerability similarity based on binary procedure, which has coarse detection granularity and cannot locate the vulnerability trigger position accurately, and have a higher false positive rate, so a new binary vulnerability similarity detection method based on function parameter dependency in hazard API is proposed. First, convert the instructions of different architectures into an intermediate language, and use the compiler with a back-end optimizer to optimize and normalize the binary procedure. Then, locate the hazard API that appears in the binary procedure, and perform the function parameters dependency analysis to generate a set of parameter slices on the hazard API. Experiments show that the method has a higher recall rate (up to 14.3% better than the baseline model) in real-world scenarios, and not only locates the triggering position of the vulnerability but also identifies the fixed vulnerability.