LibPass: An Entropy-Guided Black-Box Adversarial Attack Against Third-Party Library Detection Tools in the Wild

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Abstract
To mitigate the security and compliance risks posed by Android third-party libraries (TPLs), researchers have proposed many automated detection tools aimed at accurately identifying TPLs within Android applications (apps). The detection results serve as the foundation for practical downstream tasks such as software bill of materials (SBOM) generation, n-day vulnerability identification, compliance auditing, and software supply chain risk tracing, among others. However, despite the high levels of detection accuracy and efficiency achieved by state-of-the-art TPL detection tools, existing studies lack a systematic evaluation of these tools' robustness against potential malicious attacks. As a result, the risk of detection failure in real-world scenarios remains unmanageable. Tools with poor robustness may be rendered ineffective under attack, allowing unsafe and non-compliant TPLs within apps to evade scrutiny and analysis, thereby compromising user interests. To bridge this gap, we propose the first adversarial attack against TPL detection tools, LibPass. The core idea of LibPass is to generate adversarial apps by crafting perturbations that go beyond the code transformations introduced by obfuscation techniques. These adversarial perturbations hinder the generalization capability of existing detection tools, which are primarily designed to counter code obfuscation, thereby enabling the evasion of TPL detection. To minimize attack overhead and enhance stealthiness, LibPass employs an improved firefly algorithm to search for optimal adversarial apps. This work evaluates the effectiveness of LibPass against five state-of-the-art TPL detection tools on three datasets of different types and benchmarks its performance against three baseline attack methods. Experimental results demonstrate that LibPass achieves an average attack success rate of 61.33%, with a peak of 99.46%, underscoring the insufficient robustness of current TPL detection tools.
Funding Information
  • National Key R&D Program of China (2024YFB4506200)
  • National Natural Science Foundation of China (62202457)
  • YuanTu Large Research Infrastructure

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