René Helmke (Fraunhofer FKIE), Elmar Padilla (Fraunhofer FKIE, Germany), Nils Aschenbruck (University of Osnabrück)

Firmware corpora for vulnerability research should be textit{scientifically sound}. Yet, several practical challenges complicate the creation of sound corpora: Sample acquisition, e.g., is hard and one must overcome the barrier of proprietary or encrypted data. As image contents are unknown prior analysis, it is hard to select textit{high-quality} samples that can satisfy scientific demands.
Ideally, we help each other out by sharing data. But here, sharing is problematic due to copyright laws. Instead, papers must carefully document each step of corpus creation: If a step is unclear, replicability is jeopardized. This has cascading effects on result verifiability, representativeness, and, thus, soundness.

Despite all challenges, how can we maintain the soundness of firmware corpora? This paper thoroughly analyzes the problem space and investigates its impact on research: We distill practical binary analysis challenges that significantly influence corpus creation. We use these insights to derive guidelines that help researchers to nurture corpus replicability and representativeness. We apply them to 44 top tier papers and systematically analyze scientific corpus creation practices. Our comprehensive analysis confirms that there is currently no common ground in related work. It shows the added value of our guidelines, as they discover methodical issues in corpus creation and unveil miniscule step stones in documentation. These blur visions on representativeness, hinder replicability, and, thus, negatively impact the soundness of otherwise excellent work.

Finally, we show the feasibility of our guidelines and build a new corpus for large-scale analyses on Linux firmware: LFwC. We share rich meta data for good (and proven) replicability. We verify unpacking, deduplicate, identify contents, provide ground truth, and demonstrate LFwC's utility for research.

View More Papers

Rethink Custom Transformers for Binary Analysis

Heng Yin, Professor, Department of Computer Science and Engineering, University of California, Riverside

Read More

PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented...

Ye Liu (Singapore Management University), Yue Xue (MetaTrust Labs), Daoyuan Wu (The Hong Kong University of Science and Technology), Yuqiang Sun (Nanyang Technological University), Yi Li (Nanyang Technological University), Miaolei Shi (MetaTrust Labs), Yang Liu (Nanyang Technological University)

Read More

DeFiIntel: A Dataset Bridging On-Chain and Off-Chain Data for...

Iori Suzuki (Graduate School of Environment and Information Sciences, Yokohama National University), Yin Minn Pa Pa (Institute of Advanced Sciences, Yokohama National University), Nguyen Thi Van Anh (Institute of Advanced Sciences, Yokohama National University), Katsunari Yoshioka (Graduate School of Environment and Information Sciences, Yokohama National University)

Read More