Lune

OOPSLA2024顶会

Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis

Benjamin Mariano, Ziteng Wang, Shankara Pailoor, Christian S. Collberg, Isil Dillig

2024年份
5被引次数
1顶会引用

摘要

Code deobfuscation, which attempts to simplify code that has been intentionally obfuscated to prevent understanding, is a critical technique for downstream security analysis tasks like malware detection. While there has been significant prior work on code deobfuscation, most techniques either do not handle control flow obfuscations that modify control flow or they target specific classes of control flow obfuscations, making them unsuitable for handling new types of obfuscations or combinations of existing ones. In this paper, we study a new deobfuscation technique that is based on program synthesis and that can handle a broad class of control flow obfuscations. Given an obfuscated program 𝑃, our approach aims to synthesize a smallest program that is a control-flow reduction of 𝑃 and that is semantically equivalent. Since our method does not assume knowledge about the types of obfuscations that have been applied to the original program, the underlying synthesis problem ends up being very challenging. To address this challenge, we propose a novel trace-informed compositional synthesis algorithm that leverages hints present in dynamic traces of the obfuscated program to decompose the synthesis problem into a set of simpler subproblems. In particular, we show how dynamic traces can be useful for inferring a suitable control-flow skeleton of the deobfuscated program and performing independent synthesis of each basic block. We have implemented this approach in a tool called Chisel and evaluate it on 546 benchmarks that have been obfuscated using combinations of six different obfuscation techniques. Our evaluation shows that our approach is effective and that it produces code that is almost identical (modulo variable renaming) to the original (non-obfuscated) program in 86% of cases. Our evaluation also shows that Chisel significantly outperforms existing techniques.

CCS Concepts: • Security and privacy → Malware and its mitigation; • Software and its engineering → Search-based software engineering.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

引用它的顶会 Paper1

问问它们各自怎么用它

它引用的顶会 Paper13

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖