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Up Close and Personal: Keeping an Eye On Mobile

Mikhail Sasonkin

Mobile-focused OSINT techniques for tracking individuals and organizations through mobile devices and location data.

Summary

### Mobile Application Reconnaissance for Security Assessments At 00:00, Mikhail Sosonkin, Director of Research and Data at Synack, introduces methodologies for conducting deep reconnaissance against modern mobile applications on iOS and Android. Sosonkin highlights how mobile apps frequently expose sensitive backend attack surfaces that traditional web assessments overlook.

### Overcoming ARM Architecture and Static Analysis Barriers At 02:31, Sosonkin discusses the learning curve associated with reversing compiled ARM binaries. He explains how binary obfuscation, encrypted strings, and proprietary packers complicate static analysis, inspiring him to develop helper tools such as IDARef to speed up assembly instruction comprehension during reverse engineering.

### Dynamic Instrumentation and Code Coverage Tracing At 05:02, the presentation focuses on dynamic runtime analysis using frameworks like Frida and Cycript. By hooking into runtime method dispatchers, Sosonkin logs executed functions in real-time, mapping out code coverage to verify that automated and manual testing reaches all critical application components.

### In-Memory UI Trees and Infrastructure Endpoint Discovery At 07:28 and 09:53, Sosonkin demonstrates how inspecting in-memory view hierarchies and object states reveals hidden functionality. By manipulating UI properties and intentionally triggering error routines, testers can induce client applications into leaking undocumented API gateways, staging environments, and backend cloud microservices.

### Workflow Integration and Reconnaissance Tooling Concluding between 12:11 and 15:20, Sosonkin shares techniques for structuring mobile reconnaissance data. He emphasizes compiling discovered endpoints, auth parameters, and structural graphs into centralized research notebooks to empower comprehensive vulnerability chaining during subsequent testing phases.

(AI summary — Gemini Flash)

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