LLVM Pass Detects Core Structures in Tigress Virtualization Obfuscation

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

Researchers Sangjun An, Seoksu Lee, and Eun‑Sun Cho announced on arXiv that they have developed a static analysis technique to identify the fundamental components of Tigress virtualization‑based obfuscation. The work was first submitted on 19 January 2026 (v1, 233 KB) and revised on 23 January 2026 (v2, 1,472 KB). Their approach targets the dispatch routine, handler blocks, and the virtual‑machine region of obfuscated binaries, aiming to simplify deobfuscation efforts for security analysts.

Background on Virtualization‑Based Obfuscation

Virtualization‑based obfuscation translates original program instructions into custom bytecode executed by a software virtual machine. This transformation produces long, complex code sequences that hinder conventional static and dynamic analysis, making it a popular choice for sophisticated malware authors.

Methodology: LLVM Pass Design

The authors implemented an LLVM pass that operates on LLVM intermediate representation (IR) to locate the three core structures required for deobfuscation. By modeling the execution flow of the obfuscated program, the pass identifies the dispatch routine that selects handler blocks, the individual handler blocks that implement bytecode semantics, and the contiguous region containing the virtual‑machine bytecode.

Experimental Setup and Results

Experiments were conducted on Tigress‑generated binaries without compiler optimizations. The LLVM pass successfully detected all core structures across the three primary virtualization modes supported by Tigress: switch‑based dispatch, direct‑call dispatch, and indirect‑call dispatch. The authors report a 100 % detection rate for each mode under the test conditions.

Implications for Malware Analysis

Accurate identification of these structures can streamline reverse‑engineering workflows, allowing analysts to isolate the virtual machine logic from the surrounding code. Consequently, the technique may reduce the time required to develop deobfuscation tools and improve the reliability of static malware assessments.

Limitations and Future Work

The current implementation assumes the absence of compiler optimizations, which can alter the layout of the virtual‑machine region and obscure dispatch patterns. The authors suggest extending the pass to handle optimized builds and to integrate dynamic analysis cues for more resilient detection.

Conclusion

Overall, the study demonstrates that a targeted LLVM pass can reliably uncover the essential components of Tigress virtualization‑based obfuscation, offering a practical asset for cybersecurity professionals confronting heavily obfuscated malware.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.