Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. We therefore propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP.
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