Social-HUMINT：用 LLM 量化社交媒体信息暴露程度
Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure
研究者用 LLM 把社工攻击前的“你能被挖出多少信息”变成了可量化指标，提取 F1 到 97.3%，做安全的人可以拿来测自己的暴露面。
这篇论文提出 Social-HUMINT 框架，把 SOCMINT 和 Cyber-HUMINT 两种情报学科统一为覆盖 CTI 生命周期的 38 项能力分类，专家验证 Fleiss' κ 达 0.92。核心路径由 LLM 增强的多智能体流水线实现，覆盖信息提取、属性推断和易感性评估三步。在真实用户公开数据上，提取环节 F1 达 97.3%，推断属性得分 4.73/5，明显超过规则和 NER 基线。流水线还能预测目标对个性化选项的排序，与实际选择的匹配度显著高于随机水平，说明这种画像足以支撑针对性社工攻击。
Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure
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 engineering attacks. Because no existing model fully spans this continuum, we unify Social Media Intelligence (SOCMINT) and Cyber Human Intelligence (Cyber-HUMINT), two individual-focused disciplines that evolved separately, into a single doctrine-grounded taxonomy of 38 capabilities across the Cyber Threat Intelligence lifecycle, where user profiling bridges passive collection and active engagement. Domain experts validated the taxonomy (Fleiss' $κ$ = 0.92). We operationalize its core path (extraction, inference, and susceptibility assessment) as an LLM-augmented multi-agent pipeline, evaluated on real participants' public data. Susceptibility is assessed indirectly: the pipeline predicts how each target would rank personalized engagement options, matching their actual choices significantly above chance. This ranking capacity captures the personalization on which tailored attacks depend. The underlying profiles are accurate: extraction reaches F1 = 97.3%, and inferred attributes score 4.73/5, both far above rule-based and NER-based baselines at negligible cost. The framework augments security analysts' tradecraft rather than replacing it and enables defenders to gauge their own exposure before an adversary does.