Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure

Pellegrino Casoria, Simon Pietro Romano · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31573 · open-access PDF

Abstract

In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured information from unstructured content and deduce attributes users never disclosed. A person's true exposure is not what they post but what can be inferred from it. As these practices become routine, exposure to social engineering attacks becomes an operational certainty, demanding a shared framework for assessment and mitigation. We make this exposure measurable by quantifying how accurately a target can be profiled, and how far that profile enables social

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