Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure
Framework quantifies how cheaply LLMs can reconstruct personal profiles from public social-media data, underlining the OSINT exposure risk for executives and employees in an era of automated reconnaissance.
Summary written by editorial AI · Source link below
arXiv:2610.31573v1 Announce Type: new 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 assess
Editorial Analysis
Automated LLM-powered profiling dramatically lowers the cost of social engineering reconnaissance; enterprises should assume adversaries can cheaply build detailed profiles of key staff from public posts.
Conduct an LLM-assisted OSINT assessment of your leadership team's social-media footprint and brief them on exposure reduction.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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