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Although existing techniques have proposed automated approaches to alleviatethe path explosion problem of symbolic execution, users still need to optimizesymbolic execution by applying various searching strategies carefully. Asexisting approaches mainly support only coarse-grained global searchingstrategies, they cannot efficiently traverse through complex code structures.In this paper, we propose Eunomia, a symbolic execution technique that allowsusers to specify local domain knowledge to enable fine-grained search. InEunomia, we design an expressive DSL, Aes, that lets users precisely pinpointlocal searching strategies to different parts of the target program. To furtheroptimize local searching strategies, we design an interval-based algorithm thatautomatically isolates the context of variables for different local searchingstrategies, avoiding conflicts between local searching strategies for the samevariable. We implement Eunomia as a symbolic execution platform targetingWebAssembly, which enables us to analyze applications written in variouslanguages (like C and Go) but can be compiled into WebAssembly. To the best ofour knowledge, Eunomia is the first symbolic execution engine that supports thefull features of the WebAssembly runtime. We evaluate Eunomia with a dedicatedmicrobenchmark suite for symbolic execution and six real-world applications.Our evaluation shows that Eunomia accelerates bug detection in real-worldapplications by up to three orders of magnitude. According to the results of acomprehensive user study, users can significantly improve the efficiency andeffectiveness of symbolic execution by writing a simple and intuitive Aesscript. Besides verifying six known real-world bugs, Eunomia also detected twonew zero-day bugs in a popular open-source project, Collections-C.

Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries