MBA-Blast: Unveiling and Simplifying Mixed Boolean-Arithmetic Obfuscation

Mixed Boolean-Arithmetic (MBA) obfuscation is a method to perform a semantics-preserving transformation from a simple expression to a representation that is hard to understand and analyze. More specifically, this obfuscation technique consists of the mixture usage of arithmetic operations (e.g., ADD and IMUL) and Boolean operations (e.g., AND, OR, and NOT). Binary code with MBA obfuscation can effectively hide the secret data/algorithm from both static and dynamic reverse engineering, including advanced analyses utilizing SMT solvers. Unfortunately, deobfuscation research against MBA is still in its infancy: state-of-the-art solutions such as pattern matching, bit-blasting, and program synthesis either suffer from severe performance penalties, are designed for specific MBA patterns, or generate too many false simplification results in practice.

In this paper, we first demystify the underlying mechanism of MBA obfuscation. Our in-depth study reveals a hidden two-way feature regarding MBA transformation between 1-bit and n-bit variables. We exploit this feature and propose a viable solution to efficiently deobfuscate code with MBA obfuscation. Our key insight is that MBA transformations behave in the same way on 1-bit and n-bit variables. We provide a mathematical proof to guarantee the correctness of this finding. We further develop a novel technique to simplify MBA expressions to a normal simple form by arithmetic reduction in 1-bit space. We have implemented this idea as an open-source prototype, named MBA-Blast, and evaluated it on a comprehensive dataset with about 10,000 MBA expressions. We also tested our method in real-world, binary code deobfuscation scenarios, which demonstrate that MBA-Blast can assist human analysts to harness the full strength of SMT solvers. Compared with existing work, MBA-Blast is the most generic and efficient MBA deobfuscation technique; it has a solid theoretical underpinning, as well as, the highest success rate with negligible overhead.