Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities

TL;DR

This paper evaluates the capabilities of large language models in assembly code deobfuscation, revealing significant performance variability and fundamental limitations, and proposing a four-dimensional framework to understand these variations.

Contribution

It introduces the first comprehensive evaluation of commercial LLMs for assembly deobfuscation and proposes a theoretical framework explaining their performance variations.

Findings

LLMs show varying success in deobfuscation tasks.

Certain obfuscation techniques remain resistant to LLMs.

Fundamental error patterns limit LLM effectiveness.

Abstract

Large language models (LLMs) have shown promise in software engineering, yet their effectiveness for binary analysis remains unexplored. We present the first comprehensive evaluation of commercial LLMs for assembly code deobfuscation. Testing seven state-of-the-art models against four obfuscation scenarios (bogus control flow, instruction substitution, control flow flattening, and their combination), we found striking performance variations--from autonomous deobfuscation to complete failure. We propose a theoretical framework based on four dimensions: Reasoning Depth, Pattern Recognition, Noise Filtering, and Context Integration, explaining these variations. Our analysis identifies five error patterns: predicate misinterpretation, structural mapping errors, control flow misinterpretation, arithmetic transformation errors, and constant propagation errors, revealing fundamental limitations in LLM code processing.We establish a three-tier resistance model: bogus control flow (low resistance), control flow flattening (moderate resistance), and instruction substitution/combined techniques (high resistance). Universal failure against combined techniques demonstrates that sophisticated obfuscation remains effective against advanced LLMs. Our findings suggest a human-AI collaboration paradigm where LLMs reduce expertise barriers for certain reverse engineering tasks while requiring human guidance for complex deobfuscation. This work provides a foundation for evaluating emerging capabilities and developing resistant obfuscation techniques.x deobfuscation. This work provides a foundation for evaluating emerging capabilities and developing resistant obfuscation techniques.