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

CASPR: Context-Aware Security Policy Recommendation

Lifang Xiao (Institute of Information Engineering, Chinese Academy of Sciences), Hanyu Wang (Institute of Information Engineering, Chinese Academy of Sciences), Aimin Yu (Institute of Information Engineering, Chinese Academy of Sciences), Lixin Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Dan Meng (Institute of Information Engineering, Chinese Academy of Sciences)

Read More

VeriBin: Adaptive Verification of Patches at the Binary Level

Hongwei Wu (Purdue University), Jianliang Wu (Simon Fraser University), Ruoyu Wu (Purdue University), Ayushi Sharma (Purdue University), Aravind Machiry (Purdue University), Antonio Bianchi (Purdue University)

Read More

Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces...

Yanzuo Chen (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

Read More