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Binary rewriters are tools that are used to modify the functionality of binaries lacking source code. Binary rewriters can be used to rewrite binaries for a variety of purposes including optimization, hardening, and extraction of executable components. To rewrite a binary based on semantic criteria, an essential primitive to have is a machine-code synthesizer—a tool that synthesizes an instruction sequence from a specification of the desired behavior, often given as a formula in quantifier-free bit-vector logic (QFBV). However, state-of-the-art machine-code synthesizers such as McSynth++ employ naïve search strategies for synthesis: McSynth++ merely enumerates candidates of increasing length without performing any form of prioritization. This inefficient search strategy is compounded by the huge number of unique instruction schemas in instruction sets (e.g., around 43,000 in Intel’s IA-32) and the exponential cost inherent in enumeration. The effect is slow synthesis: even for relatively small specifications, McSynth++ might take several minutes or a few hours to find an implementation.

In this paper, we describe how we use machine learning to make the search in McSynth++ smarter and potentially faster. We converted the linear search in McSynth++ into a best-first search over the space of instruction sequences. The cost heuristic for the best-first search comes from two models—used together— built from a corpus of ⟨QFBV-formula, instruction-sequence⟩ pairs: (i) a language model that favors useful instruction sequences, and (ii) a regression model that correlates features of instruction sequences with features of QFBV formulas, and favors instruction sequences that are more likely to implement the input formula. Our experiments for IA-32 showed that our model-assisted synthesizer enables synthesis of code for 6 out of 50 formulas on which McSynth++ times out, speeding up the synthesis time by at least 526×, and for the remaining formulas, speeds up synthesis by 4.55×.