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Sangjun An

Publications and source records attributed to Sangjun An.

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Towards LLM-Based Analysis of Virtualization-Obfuscated Code through Automated Data Generation

Virtualization-based obfuscation produces extremely large and structurally complex binaries, posing challenges for LLM-based analysis due to input size limits and the need for large-scale labeled data. We address this by focusing on structural rather than full semantic analysis. Obfuscated binaries are decomposed into the largest semantically coherent units that fit within LLM constraints and are labeled according to their structural roles. We implement a static analysis framework to automate labeling and enable large-scale dataset generation. Our prototype shows strong performance on real-world virtualization obfuscators.

cs.CR

Static Detection of Core Structures in Tigress Virtualization-Based Obfuscation Using an LLVM Pass

Malware often uses obfuscation to hinder security analysis. Among these techniques, virtualization-based obfuscation is particularly strong because it protects programs by translating original instructions into attacker-defined virtual machine (VM) bytecode, producing long and complex code that is difficult to analyze and deobfuscate. This paper aims to identify the structural components of virtualization-based obfuscation through static analysis. By examining the execution model of obfuscated code, we define and detect the key elements required for deobfuscation-namely the dispatch routine, handler blocks, and the VM region-using LLVM IR. Experimental results show that, in the absence of compiler optimizations, the proposed LLVM Pass successfully detects all core structures across major virtualization options, including switch, direct, and indirect modes.

cs.CR