Engineer reverse-engineers Apple Find My location sharing for custom geofence automation

An engineer accessed the raw location data stream from Apple’s Find My service, which is normally consumed through Apple’s own apps. By intercepting and parsing this stream, they bypassed the official interface, gaining direct access to coordinates shared by a contact. This approach does not require jailbreaking or exploiting a vulnerability but instead relies on reverse-engineering the protocol used by Apple’s ecosystem to sync location data between devices.

The extracted data was then fed into a custom automation pipeline running on Linux. The pipeline defined geofences, virtual boundaries around specific geographic areas, and monitored the contact’s location in real time. When the contact entered or exited a geofenced area, the system triggered Discord messages to announce the event. This demonstrates how location data, once extracted, can be repurposed for use cases beyond Apple’s built-in features, such as custom alerts or integrations with third-party platforms.

While the project was conducted with the explicit consent of the shared contact, it underscores the broader implications of repurposing closed systems. Location-sharing features are designed with specific user interfaces and limitations, but once data leaves those interfaces, it can be used in ways that may not align with the original intent. This raises questions about data ownership: does the person sharing their location retain control over how it is used, or does the recipient gain implicit permission to process it in any way they choose?

The technical approach here is not novel, reverse-engineering protocols to extend functionality is a common practice in engineering, but the context is notable. Location data is inherently sensitive, and its use in automation introduces risks around unintended exposure or misuse. Engineers adopting similar methods should consider not only the technical feasibility but also the ethical and privacy implications, particularly when handling data shared under assumptions of limited use. The project serves as a reminder that shared data, even within trusted ecosystems, can be extracted and repurposed with relative ease.