Navigating Developers' Quagmire: LLM-Enabled Privacy Compliance Analysis for SDK Integrations

The use of third-party SDKs has become a major source of privacy noncompliance in mobile apps, creating an urgent need for app developers to ensure privacy compliance during SDK integrations. However, existing methods for identifying privacy-noncompliant SDK integrations (PINs) largely rely on predefined noncompliance patterns based on externally observable app behaviors. These methods are limited in their ability to systematically detect PINs due to the lack of generic detection, and in providing concrete development guidance for app developers on SDK integrations due to limited visibility into the SDK integration context. To overcome these limitations, we introduce a new PIN detection paradigm using privacy-contextual consistency analysis, based on a key observation: PINs often manifest as inconsistencies between the privacy implications of SDK APIs and the context of their integrations, which allow for generic, PIN-independent checks. This study takes the first step in validating the feasibility of the new detection paradigm. We first establish the knowledge foundation for this paradigm by defining models that capture API privacy implications, privacy context, and generic consistency analysis rules that describe the proper use and implementation of SDK APIs. Based on the models, we develop an automated framework, PINFINDER, to detect PINs arising from SDK integrations in Android apps – software known for its extensive use of SDKs. The framework combines app analysis techniques for extracting SDK APIs and privacy context, along with large language models (LLMs) for understanding and analyzing privacy-contextual inconsistencies, and targeted enhancements to increase the feasibility of LLMenabled consistency analysis. Our evaluation confirms the effectiveness and coverage of PINFINDER in detecting PINs. Running PINFINDER on 4,683 real-world apps further sheds light on the prevalence and magnitude of PINs, revealing lesser-known manifestations and their underlying causes. These causes highlight the need for greater standardization in SDK integration API design and improved transparency regarding their privacy implications.