Context-Aware Spear Phishing: Generative AI-Enabled Attacks Against Individuals via Public Social Media Data
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910209933312000 |
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| author | Vafa, Elham Pourabbas Roy, Sayak Saha Nilizadeh, Shirin |
| author_facet | Vafa, Elham Pourabbas Roy, Sayak Saha Nilizadeh, Shirin |
| contents | We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized, context-aware spear-phishing campaigns. With minimal attacker effort, a small amount of public activity per target is sufficient for GenAI models to extract interests and contextual cues, producing persuasive messages that mirror a target's style while bypassing generic content-moderation safeguards. We introduce a modular framework that combines multimodal signal extraction, communication-style profiling, and attack-type instantiation across seven strategies (baiting, scareware, honey trap, tailgating, impersonation, quid pro quo, and personalized emotional exploitation). We conduct a large-scale, multi-model evaluation covering thousands of generated emails and eight security-relevant criteria, benchmarking against a corpus of real-world phishing messages. The GenAI-produced emails exhibit markedly higher personalization, contextual grounding, and persuasive leverage. Importantly, a complementary user study corroborates these results, revealing that LLM-generated attacks consistently outperform APWG eCrimeX emails across eight dimensions while eliciting lower suspicion among human recipients. Finally, we measure and analyze the behavior of existing proactive, prompt-level defense mechanisms, which incorporate adaptive mechanisms, as well as two complementary defense approaches-policy-augmented SOTA safeguard models and system-instruction chain-of-thought moderation. We document how these defenses respond to contextualized and adaptive attack prompts, underscoring the need for platform-level safeguards that explicitly account for contextualized abuse at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11268 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-Aware Spear Phishing: Generative AI-Enabled Attacks Against Individuals via Public Social Media Data Vafa, Elham Pourabbas Roy, Sayak Saha Nilizadeh, Shirin Cryptography and Security We demonstrate how publicly available social-media data and generative AI (GenAI) can be misused to automate and scale highly personalized, context-aware spear-phishing campaigns. With minimal attacker effort, a small amount of public activity per target is sufficient for GenAI models to extract interests and contextual cues, producing persuasive messages that mirror a target's style while bypassing generic content-moderation safeguards. We introduce a modular framework that combines multimodal signal extraction, communication-style profiling, and attack-type instantiation across seven strategies (baiting, scareware, honey trap, tailgating, impersonation, quid pro quo, and personalized emotional exploitation). We conduct a large-scale, multi-model evaluation covering thousands of generated emails and eight security-relevant criteria, benchmarking against a corpus of real-world phishing messages. The GenAI-produced emails exhibit markedly higher personalization, contextual grounding, and persuasive leverage. Importantly, a complementary user study corroborates these results, revealing that LLM-generated attacks consistently outperform APWG eCrimeX emails across eight dimensions while eliciting lower suspicion among human recipients. Finally, we measure and analyze the behavior of existing proactive, prompt-level defense mechanisms, which incorporate adaptive mechanisms, as well as two complementary defense approaches-policy-augmented SOTA safeguard models and system-instruction chain-of-thought moderation. We document how these defenses respond to contextualized and adaptive attack prompts, underscoring the need for platform-level safeguards that explicitly account for contextualized abuse at scale. |
| title | Context-Aware Spear Phishing: Generative AI-Enabled Attacks Against Individuals via Public Social Media Data |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.11268 |