Finding the Dwarf: Recovering Precise Types from WebAssembly Binaries

Daniel Lehmann, Michael Pradel

Proceedings of the 43rd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), pp. 1–16, 2022.

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Abstract

The increasing popularity of WebAssembly creates a demand for understanding and reverse engineering WebAssembly binaries. Recovering high-level function types is an important part of this process. One method to recover types is data-flow analysis, but it is complex to implement and may require manual heuristics when logical constraints fall short. In contrast, this paper presents SnowWhite, a learning-based approach for recovering precise, high-level parameter and return types for WebAssembly functions. It improves over prior work on learning-based type recovery by representing the types-to-predict in an expressive type language, which can describe a large number of complex types, instead of the fixed, and usually small type vocabulary used previously. Thus, recovery of a single type is no longer a classification task but sequence prediction, for which we build on the success of neural sequence-to-sequence models. We evaluate SnowWhite on a new, large-scale dataset of 6.3 million type samples extracted from 300,905 WebAssembly object files. The results show the type language is expressive, precisely describing 1,225 types instead the 7 to 35 types considered in previous learning-based approaches. Despite this expressiveness, our type recovery has high accuracy, exactly matching 44.5% (75.2%) of all parameter types and 57.7% (80.5%) of all return types within the top-1 (top-5) predictions.

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