# StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation

arXiv: https://arxiv.org/abs/2406.04568 · HTML: https://mlsanity.com/paper/2406.04568/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 7 of 7)

- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Augmenting Math Word Problems via Iterative Question Composing](https://mlsanity.com/paper/2401.09003/) (2024)
- [Refining Decompiled C Code with Large Language Models](https://mlsanity.com/paper/2310.06530/) (2023)
- [Towards Neural Decompilation](https://mlsanity.com/paper/1905.08325/) (2019)
- [Pop Quiz! Can a Large Language Model Help With Reverse Engineering?](https://mlsanity.com/paper/2202.01142/) (2022)
- [Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary](https://mlsanity.com/paper/2306.02546/) (2023)
- [Semantics-Recovering Decompilation through Neural Machine Translation](https://mlsanity.com/paper/2112.15491/) (2021)

## Built on by (most cited first, 1 of 1)

- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)

## Abstract (arXiv)

WebAssembly enables near-native execution in web applications and is increasingly adopted for tasks that demand high performance and robust security. However, its assembly-like syntax, implicit stack machine, and low-level data types make it extremely difficult for human developers to understand, spurring the need for effective WebAssembly reverse engineering techniques. In this paper, we propose StackSight, a novel neurosymbolic approach that combines Large Language Models (LLMs) with advanced program analysis to decompile complex WebAssembly code into readable C++ snippets. StackSight visualizes and tracks virtual stack alterations via a static analysis algorithm and then applies chain-of-thought prompting to harness LLM's complex reasoning capabilities. Evaluation results show that StackSight significantly improves WebAssembly decompilation. Our user study also demonstrates that code snippets generated by StackSight have significantly higher win rates and enable a better grasp of code semantics.