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The binary code similarity detection (BCSD) method measures the similarity oftwo binary executable codes. Recently, the learning-based BCSD methods haveachieved great success, outperforming traditional BCSD in detection accuracyand efficiency. However, the existing studies are rather sparse on theadversarial vulnerability of the learning-based BCSD methods, which causehazards in security-related applications. To evaluate the adversarialrobustness, this paper designs an efficient and black-box adversarial codegeneration algorithm, namely, FuncFooler. FuncFooler constrains the adversarialcodes 1) to keep unchanged the program's control flow graph (CFG), and 2) topreserve the same semantic meaning. Specifically, FuncFooler consecutively 1)determines vulnerable candidates in the malicious code, 2) chooses and insertsthe adversarial instructions from the benign code, and 3) corrects the semanticside effect of the adversarial code to meet the constraints. Empirically, ourFuncFooler can successfully attack the three learning-based BCSD models,including SAFE, Asm2Vec, and jTrans, which calls into question whether thelearning-based BCSD is desirable.

FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods