CRAIFeb 9, 2021

AI-based Blackbox Code Deobfuscation: Understand, Improve and Mitigate

arXiv:2102.04805v17 citations
Originality Incremental advance
AI Analysis

This work is significant for software developers and security researchers, offering improved blackbox code deobfuscation techniques and countermeasures against them, representing an incremental advancement in the field.

This paper explores AI-based blackbox code deobfuscation, identifying the instability of search spaces for simulation-based methods and proposing robust S-metaheuristics. They introduce Xyntia, a new deobfuscator that significantly outperforms prior work in success rate, especially with limited time budgets, and is immune to recent anti-analysis obfuscation. The paper also proposes two novel protections against AI-based deobfuscation.

Code obfuscation aims at protecting Intellectual Property and other secrets embedded into software from being retrieved. Recent works leverage advances in artificial intelligence with the hope of getting blackbox deobfuscators completely immune to standard (whitebox) protection mechanisms. While promising, this new field of AI-based blackbox deobfuscation is still in its infancy. In this article we deepen the state of AI-based blackbox deobfuscation in three key directions: understand the current state-of-the-art, improve over it and design dedicated protection mechanisms. In particular, we define a novel generic framework for AI-based blackbox deobfuscation encompassing prior work and highlighting key components; we are the first to point out that the search space underlying code deobfuscation is too unstable for simulation-based methods (e.g., Monte Carlo Tres Search used in prior work) and advocate the use of robust methods such as S-metaheuritics; we propose the new optimized AI-based blackbox deobfuscator Xyntia which significantly outperforms prior work in terms of success rate (especially with small time budget) while being completely immune to the most recent anti-analysis code obfuscation methods; and finally we propose two novel protections against AI-based blackbox deobfuscation, allowing to counter Xyntia's powerful attacks.

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