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P2 Defeating Program Analysis Techniques via Ambiguous Translation

通过歧义翻译击败程序分析技术
ASE 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)2021匹配度 3/10 · 该论文属于程序分析领域,且以“指出已有分析技术在特定代码形态下失效并解释原因”为切入,与用户偏好问题驱动的风格有一定共鸣;但其核心是反分析的代码混淆/歧义翻译(对抗分析),既非将静态分析用于 ML/Agentic 程序,也非用 LLM 改进分析,与浮点分析、贝叶斯程序分析亦无交集,方向上是用户研究目标的“反面”。引用 2 inbox
Chijung Jung, Doowon Kim, Weihang Wang, Yunhui Zheng, Kyu Hyung Lee, Yonghwi Kwon
crossref: 2026-09-12

AI 速览

一句话:提出一种歧义翻译反分析技术,将代码片段编码,并用状态机产生多个难以区分的解码结果以对抗程序分析。

本文研究一种新的反分析技术,专门针对现有程序分析方法的薄弱点。该方法将待隐藏的程序代码片段编码,解码过程由复杂状态机实现,能根据输入产生多个输出;其核心是歧义化解码,使同一编码产生多个彼此难以区分的解码代码片段,从而干扰分析。与已有类似方法不同,它在正确解码与错误解码时执行行为没有明显差异,因此更隐蔽。论文还分析现有技术的弱点并讨论潜在改进。作者实现概念验证并评估,初步结果表明该技术给程序分析带来多种新挑战。

问题
如何设计比现有反分析或混淆方法更隐蔽的技术,利用程序分析方法的弱点,使分析无法可靠区分正确与错误解码结果。
方法
将待隐藏代码片段编码,并用依输入产生多输出的复杂状态机进行歧义化解码,同时分析现有程序分析技术的弱点。
评估
实现概念验证原型并进行初步评估,展示该技术对程序分析技术构成多种新挑战。
原文摘要(Abstract)

This research explores the possibility of a new anti-analysis technique, carefully designed to attack weaknesses of the existing program analysis approaches. It encodes a program code snippet to hide, and its decoding process is implemented by a sophisticated state machine that produces multiple outputs depending on inputs. The key idea of the proposed technique is to ambiguously decode the program code, resulting in multiple decoded code snippets that are challenging to distinguish from each other. Our approach is stealthier than previous similar approaches as its execution does not exhibit different behaviors between when it decodes correctly or incorrectly. This paper also presents analyses of weaknesses of existing techniques and discusses potential improvements. We implement and evaluate the proof of concept approach, and our preliminary results show that the proposed technique imposes various new unique challenges to the program analysis technique.

链接与引用

DOI 原文 ·

BibTeX
@inproceedings{paperbot6524,
  title = {Defeating Program Analysis Techniques via Ambiguous Translation},
  author = {Chijung Jung and Doowon Kim and Weihang Wang and Yunhui Zheng and Kyu Hyung Lee and Yonghwi Kwon},
  booktitle = {ASE},
  year = {2021},
  doi = {10.1109/ase51524.2021.9678912}
}