Binary Software Composition Analysis (BSCA) is a technique for identifying the versions of third-party libraries (TPLs) used in compiled binaries, thereby tracing the dependencies and vulnerabilities of software components without access to their source code. However, existing BSCA techniques struggle with cross-language invoked C/C++ binaries in polyglot projects due to two key challenges: (1) interference from heterogeneous Foreign Function Interface (FFI) bindings that obscure distinctive TPL features and generate false positives during matching processes, and (2) the inherent complexity of composite binaries (fused binaries), particularly prevalent in polyglot development where multiple TPLs are frequently compiled into single executable units, resulting in blurred boundaries between libraries and substantially compromising version identification precision.We propose DeeperBin, a BSCA technique that addresses these challenges through a high-quality, large-scale feature database with four key advantages: (1) high scalability that is capable of analyzing 74,647 C/C++ TPL versions, (2) efficient noise filtering to remove FFI bindings and common functions, (3) automated extraction of version string regexes for 31,855 TPL versions, and (4) generation of distinctive version features using the Minimum Description Length (MDL) principle. Evaluated on 418 cross-language binaries, DeeperBin achieves 81.2% precision and 84.6% recall for TPL detection, outperforming state-of-the-art (SOTA) techniques by 14.1% and 23.2%, respectively. For version identification, it achieves 70.3% precision, a 12.6% improvement over state-of-the-art techniques. Ablation studies confirm the usefulness of FFI filtering and MDL-based features, boosting precision and recall by 17.1% and 18.8%. DeeperBin also maintains competitive efficiency, processing binaries in 364.3 seconds while supporting the largest feature database.

Vulnerabilities, referred to as CLV issues, are induced by cross-language invocations of vulnerable libraries. Such issues greatly increase the attack surface of Python/Java projects due to their pervasive use of C libraries. Existing Python/Java build tools in PyPI and Maven ecosystems fail to report the dependency on vulnerable libraries written in other languages such as C. CLV issues are easily missed by developers. In this paper, we conduct the first empirical study on the status quo of CLV issues in PyPI and Maven ecosystems. It is found that 82,951 projects in these ecosystems are directly or indirectly dependent on libraries compiled from the C project versions that are identified to be vulnerable in CVE reports. Our study arouses the awareness of CLV issues in popular ecosystems and presents related analysis results.