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LLMs show promise in building tools for mobile software analysis

By PulseAugur Editorial · [1 sources] ·

Researchers explored using large language models (LLMs) to aid in reverse engineering mobile software data formats, specifically focusing on iOS location services. While LLMs like Claude Opus 4.8 and Codex GPT Sol 5.6 proved unreliable for direct analysis due to subtle errors in schema reconstruction, they demonstrated utility in creating tools for validation. A Python tool named Extractor, developed with Claude's assistance, was shown to reliably recover protobuf schemas from Apple binaries, enabling analysis of actual network traffic from an iPhone. AI

IMPACT LLMs can be leveraged to build reliable tools for complex software analysis, even if direct application proves unreliable.

RANK_REASON The cluster contains an academic paper detailing research findings on the use of LLMs for reverse engineering mobile software. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs show promise in building tools for mobile software analysis

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The cluster contains an academic paper detailing research findings on the use of LLMs for reverse engineering mobile software. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Douglas Leith ·

    Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker

    arXiv:2610.21597v1 Announce Type: cross Abstract: Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for the protobuf messages that an iPhone sends to Apple's location services. Working …

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