Jonathan Crussell (Sandia National Laboratories)

Malware analysis relies on evolving tools that undergo continuous improvement and refinement. One such tool is Ghidra, released as open-source in 2019, which has seen 39 public releases and 13,000 commits as of October 2024. In this paper, we examine the impact of these updates on code similarity analysis for the same set of input files. Additionally, we measure how the underlying version of Ghidra affects simple metrics such as analysis time, error counts, and the number of functions identified. Our case studies reveal that Ghidra’s effectiveness varies depending on the specific file analyzed, highlighting the importance of context in evaluating tool performance.
We do not yet have an answer to the question posed in the title of this paper. In general, Ghidra has certainly improved in the years since it was released. Developers have fixed countless bugs, added substantial new features, and supported several new program formats. However, we observe that better is highly nuanced. We encourage the community to approach version upgrades with caution, as the latest release may not always provide superior results for every use case. By fostering a nuanced understanding of Ghidra’s advancements, we aim to contribute to more informed decision-making regarding tool adoption and usage in malware analysis and other binary analysis domains.

View More Papers

Retrofitting XoM for Stripped Binaries without Embedded Data Relocation

Chenke Luo (Wuhan University), Jiang Ming (Tulane University), Mengfei Xie (Wuhan University), Guojun Peng (Wuhan University), Jianming Fu (Wuhan University)

Read More

Towards Better CFG Layouts

Jack Royer (CentraleSupélec), Frédéric TRONEL (CentraleSupélec, Inria, CNRS, University of Rennes), Yaëlle Vinçont (Univ Rennes, Inria, CNRS, IRISA)

Read More

Vision: Retiring Scenarios — Enabling Ecologically Valid Measurement in...

Oliver D. Reithmaier (Leibniz University Hannover), Thorsten Thiel (Atmina Solutions), Anne Vonderheide (Leibniz University Hannover), Markus Dürmuth (Leibniz University Hannover)

Read More

Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A...

Ningfei Wang (University of California, Irvine), Shaoyuan Xie (University of California, Irvine), Takami Sato (University of California, Irvine), Yunpeng Luo (University of California, Irvine), Kaidi Xu (Drexel University), Qi Alfred Chen (University of California, Irvine)

Read More