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

Secure Data Analytics in Apache Spark with Fine-grained Policy...

Byeongwook Kim (Seoul National University), Jaewon Hur (Seoul National University), Adil Ahmad (Arizona State University), Byoungyoung Lee (Seoul National University)

Read More

LeakLess: Selective Data Protection against Memory Leakage Attacks for...

Maryam Rostamipoor (Stony Brook University), Seyedhamed Ghavamnia (University of Connecticut), Michalis Polychronakis (Stony Brook University)

Read More

TME-Box: Scalable In-Process Isolation through Intel TME-MK Memory Encryption

Martin Unterguggenberger (Graz University of Technology), Lukas Lamster (Graz University of Technology), David Schrammel (Graz University of Technology), Martin Schwarzl (Cloudflare, Inc.), Stefan Mangard (Graz University of Technology)

Read More

Towards LLM-Assisted Vulnerability Detection and Repair for Open-Source 5G...

Rupam Patir (University at Buffalo), Qiqing Huang (University at Buffalo), Keyan Guo (University at Buffalo), Wanda Guo (Texas A&M University), Guofei Gu (Texas A&M University), Haipeng Cai (University at Buffalo), Hongxin Hu (University at Buffalo)

Read More