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

RCABench: Open Benchmarking Platform for Root Cause Analysis

Keisuke Nishimura, Yuichi Sugiyama, Yuki Koike, Masaya Motoda, Tomoya Kitagawa, Toshiki Takatera, Yuma Kurogome (Ricerca Security, Inc.)

Read More

Trim My View: An LLM-Based Code Query System for...

Sima Arasteh (University of Southern California), Pegah Jandaghi, Nicolaas Weideman (University of Southern California/Information Sciences Institute), Dennis Perepech, Mukund Raghothaman (University of Southern California), Christophe Hauser (Dartmouth College), Luis Garcia (University of Utah Kahlert School of Computing)

Read More

Truman: Constructing Device Behavior Models from OS Drivers to...

Zheyu Ma (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University; EPFL; JCSS, Tsinghua University (INSC) - Science City (Guangzhou) Digital Technology Group Co., Ltd.), Qiang Liu (EPFL), Zheming Li (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University; JCSS, Tsinghua University (INSC) - Science City (Guangzhou) Digital Technology Group Co., Ltd.), Tingting Yin (Zhongguancun…

Read More

Defending Against Membership Inference Attacks on Iteratively Pruned Deep...

Jing Shang (Beijing Jiaotong University), Jian Wang (Beijing Jiaotong University), Kailun Wang (Beijing Jiaotong University), Jiqiang Liu (Beijing Jiaotong University), Nan Jiang (Beijing University of Technology), Md Armanuzzaman (Northeastern University), Ziming Zhao (Northeastern University)

Read More