Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918488272011264 |
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| author | Chen, Jiahao Zhang, Qi Lin, Ruixiao Zhou, Chunyi Du, Tianyu Li, Qingming Zhang, Tong Li, Junhao Pu, Yuwen Ji, Shouling |
| author_facet | Chen, Jiahao Zhang, Qi Lin, Ruixiao Zhou, Chunyi Du, Tianyu Li, Qingming Zhang, Tong Li, Junhao Pu, Yuwen Ji, Shouling |
| contents | Large Language Models (LLMs) have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion: the automated and in-depth personal profiling; this engenders a chilling effect of "peepers everywhere". Existing research primarily unfolds from the training pipeline of LLM, emphasizing the exposure of Personally Identifiable Information (PII) through memorization, while privacy studies from a human-centric perspective remain underexplored. To fill this void, we empirically investigate privacy perception in the real world through the lens of human awareness and the practices of LLM-integrated platforms, revealing a significant dissonance: platforms fail to technically or policy-wise address public privacy concerns. To facilitate a systematic and quantifiable study of privacy risk, we propose the PrivacyIceberg, which categorizes real-world human privacy risks into three tiers: explicitly searched, contextually inferred, and deeply aggregated, based on the sophistication of LLM exploitation. We developed IcebergExplorer to audit privacy exposure, utilizing minimal PII as a search seed to reconstruct high-fidelity profiles, achieving over 90% factual accuracy within 10 minutes at a cost under $3, for real-world scenarios. Additionally, we identify six root causes contributing to such privacy disclosures and propose multi-stakeholder countermeasures for LLM vendors, individuals, and data publishers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06232 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents Chen, Jiahao Zhang, Qi Lin, Ruixiao Zhou, Chunyi Du, Tianyu Li, Qingming Zhang, Tong Li, Junhao Pu, Yuwen Ji, Shouling Cryptography and Security Large Language Models (LLMs) have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion: the automated and in-depth personal profiling; this engenders a chilling effect of "peepers everywhere". Existing research primarily unfolds from the training pipeline of LLM, emphasizing the exposure of Personally Identifiable Information (PII) through memorization, while privacy studies from a human-centric perspective remain underexplored. To fill this void, we empirically investigate privacy perception in the real world through the lens of human awareness and the practices of LLM-integrated platforms, revealing a significant dissonance: platforms fail to technically or policy-wise address public privacy concerns. To facilitate a systematic and quantifiable study of privacy risk, we propose the PrivacyIceberg, which categorizes real-world human privacy risks into three tiers: explicitly searched, contextually inferred, and deeply aggregated, based on the sophistication of LLM exploitation. We developed IcebergExplorer to audit privacy exposure, utilizing minimal PII as a search seed to reconstruct high-fidelity profiles, achieving over 90% factual accuracy within 10 minutes at a cost under $3, for real-world scenarios. Additionally, we identify six root causes contributing to such privacy disclosures and propose multi-stakeholder countermeasures for LLM vendors, individuals, and data publishers. |
| title | Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.06232 |