Binary reverse engineering is foundational to various tasks such as malware analysis and vulnerability detection. Traditional binary analysis tools mainly operate at the function level. However, modern software has grown significantly in size, with binaries often containing thousands of functions. Without understanding how these functions are organized into higher-level structures, it becomes difficult to effectively support downstream analysis tasks. Analysts must examine thousands of functions separately, making the process time-consuming and error-prone. Despite these challenges, current research on recovering the higher-level structure of binaries remains limited.To bridge this gap, we propose BinStruct, a novel binary structure recovery framework that recovers both file and module structures from binaries. BinStruct first identifies the file structure by combining data reference patterns, function calls, and semantic understanding from Large Language Models. Then, inspired by software architecture recovery in source code analysis, BinStruct identifies modules by clustering the recovered files using consensus between structural dependency and semantic similarity. Evaluation on 121 real-world stripped binaries demonstrates that BinStruct outperforms state-of-the-art techniques in both file and module recovery accuracy, while requiring only 7.42s and 34.46s on average to recover file and module structures, respectively. Case studies on Libxml2 and PredatorTheStealer demonstrate BinStruct’s effectiveness on security tasks like attack surface analysis and malware investigation.

Third-party libraries (TPLs) are frequently used in software to boost efficiency by avoiding repeated developments. However, the massive using TPLs also brings security threats since TPLs may introduce bugs and vulnerabilities. Therefore, software composition analysis (SCA) tools have been proposed to detect and manage TPL usage. Unfortunately, due to the presence of common and trivial functions in the bloated feature dataset, existing tools fail to precisely and rapidly identify TPLs in C/C++ real-world projects. To this end, we propose OSSFP, a novel SCA framework for effective and efficient TPL detection in large-scale real-world projects via generating unique fingerprints for open source software. By removing common and trivial functions and keeping only the core functions to build the fingerprint index for each TPL project, OSSFP significantly reduces the database size and accelerates the detection process. It also improves TPL detection accuracy since noises are excluded from the fingerprints. We applied OSSFP on a large data set containing 23,427 C/C++ repositories, which included 585,683 versions and 90 billion lines of code. The result showed that it could achieve 90.84% of recall and 90.34% of precision, which outperformed the state-of-the-art tool by 35.31% and 3.71%, respectively. OSSFP took only 0.12 seconds on average to identify all TPLs per project, which was 22 times faster than the other tool. OSSFP has proven to be highly scalable on large-scale datasets.