USENIX Security2022Top-tier venue
Decomperson: How Humans Decompile and What We Can Learn From It
Kevin Burk, Fabio Pagani, Christopher Kruegel, Giovanni Vigna
Abstract
Human analysts must reverse engineer binary programs as a prerequisite for a number of security tasks, such as vulnerability analysis, malware detection, and firmware re-hosting. Existing studies of human reversers and the processes they follow are limited in size and often use qualitative metrics that require subjective evaluation.
In this paper, we reframe the problem of reverse engineering binaries as the problem of perfect decompilation, which is the process of recovering, from a binary program, source code that, when compiled, produces binary code that is identical to the original binary. This gives us a quantitative measure of understanding, and lets us examine the reversing process programmatically.
We developed a tool, called DECOMPERSON, that supported a group of reverse engineers during a large-scale security competition designed to collect information about the participants' reverse engineering process, with the well-defined goal of achieving perfect decompilation. Over 150 people participated, and we collected more than 35,000 code submissions, the largest manual reverse engineering dataset to date. This includes snapshots of over 300 successful perfect decompilation attempts. In this paper, we show how perfect decompilation allows programmatic analysis of such large datasets, providing new insights into the reverse engineering process.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers16
- Ahoy SAILR! There is No Need to DREAM of C: A Compiler-Aware Structuring Algorithm for Binary DecompilationZion Leonahenahe Basque, Ati Priya Bajaj, Wil Gibbs, Jude O'Kain et al.USENIX Security 2024 · 32 citations
- A Taxonomy of C Decompiler Fidelity IssuesLuke Dramko, Jeremy Lacomis, Edward J. Schwartz, Bogdan Vasilescu et al.USENIX Security 2024 · 23 citations
- Source Code Foundation Models are Transferable Binary Analysis Knowledge BasesZian Su, Xiangzhe Xu, Ziyang Huang, Kaiyuan Zhang et al.NeurIPS 2024 · 17 citations
- D-Helix: A Generic Decompiler Testing Framework Using Symbolic DifferentiationMuqi Zou, Arslan Khan, Ruoyu Wu, Han Gao et al.USENIX Security 2024 · 14 citations
- DecLLM: LLM-Augmented Recompilable Decompilation for Enabling Programmatic Use of Decompiled CodeWai Kin Wong, Daoyuan Wu, Huaijin Wang, Zongjie Li et al.ISSTA 2025 · 8 citations
Builds on5
- Understanding Linux MalwareEmanuele Cozzi, Mariano Graziano, Yanick Fratantonio, Davide BalzarottiS&P 2018 · 203 citations
- Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human AssistanceYan Shoshitaishvili, Michael Weissbacher, Lukas Dresel, Christopher Salls et al.CCS 2017 · 57 citations
- Augmenting Decompiler Output with Learned Variable Names and TypesQibin Chen, Jeremy Lacomis, Edward J. Schwartz, Claire Le Goues et al.USENIX Security 2022
- An Observational Investigation of Reverse Engineers' ProcessesDaniel Votipka, Seth M. Rabin, Kristopher K. Micinski, Jeffrey S. Foster et al.USENIX Security 2020
- RE-Mind: a First Look Inside the Mind of a Reverse EngineerAlessandro Mantovani, Simone Aonzo, Yanick Fratantonio, Davide BalzarottiUSENIX Security 2022
Related papers
- Walking The Last Mile: Studying Decompiler Output Correction in PracticeJoshua Wiedemeier, Simon Klancher, Joel Flores, Max Zheng et al.CCS 2025
- Evaluating the Effectiveness of DecompilersYing Cao, Runze Zhang, Ruigang Liang, Kai ChenISSTA 2024 · 10 citations
- Helping Johnny to Analyze Malware: A Usability-Optimized Decompiler and Malware Analysis User StudyKhaled Yakdan, Sergej Dechand, Elmar Gerhards-Padilla, Matthew SmithS&P 2016 · 128 citations
- How far we have come: testing decompilation correctness of C decompilersZhibo Liu, Shuai WangISSTA 2020 · 53 citations
- BinStruct: Binary Structure Recovery Combining Static Analysis and SemanticsYiran Zhang, Zhengzi Xu, Zhe Lang, Chengyue Liu et al.ASE 2025
