Abstract: Similarity detection of binary files aims to judge whether the two binary files from different platforms, compilers, optimized configurations, and even different software versions are highly similar. Binary vulnerability search is one of its applications in the field of information security. The emergence of binary vulnerabilities has brought many problems to modern software applications, such as the vulnerability of operating systems to attacks, and the vulnerability of private information to theft. The main reason that codes are reused in the software development process without strict supervision. Based on this, a binary vulnerability search method Taurus based on multi-granular semantic analysis is proposed. This method uses three granular semantic features to search for potential cross-platform binary vulnerabilities. Given a binary file to be detected and a vulnerability database, it is necessary to search each binary vulnerability in the vulnerability database one by one. Firstly, semantic extraction is performed on two binary files respectively to obtain the semantic features of the two at three granularities of basic block, function and module, and similarity calculation is conducted. Secondly, the similarities of semantic features at the three granularities are integrated to calculate the overall similarity scores of the three files. Finally, the similarity score results of the binary files to be detected and all the vulnerabilities in the vulnerability database are sorted in descending order, and the search result report of the binary file is obtained. Comparative experiments under reasonable configuration show that the proposed Taurus method is better than the baseline method in terms of accuracy. 

Key words: vulnerability search, multi-granularity semantic feature, cross-platform