Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing
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| Format: | Preprint |
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2026
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| author | Cui, Yu Yue, Ruiqing Fu, Hang Pan, Sicheng Sun, Zhuoyu Huang, Baohan Zhang, Haibin Zuo, Cong Wang, Licheng |
| author_facet | Cui, Yu Yue, Ruiqing Fu, Hang Pan, Sicheng Sun, Zhuoyu Huang, Baohan Zhang, Haibin Zuo, Cong Wang, Licheng |
| contents | With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. Existing privacy attacks against LLMs primarily target training data, while research on inference-time contextual privacy risks in LLM agent memory remains limited. Moreover, prior methods often incur high attack costs, requiring multiple queries or relying on white-box assumptions, which limits their practicality in real-world deployments. To address these issues, we propose a training-free privacy extraction attack targeting LLM agent memory, which we name \textsc{Spore}. \textsc{Spore} is compatible with both black-box and gray-box settings. In the black-box setting, \textsc{Spore} can efficiently extract a small candidate set via a single query to recover the original private information. In the gray-box setting, \textsc{Spore} allows the attacker to leverage multi-ranked tokens for more accurate and faster privacy extraction. We provide an information-theoretic analysis of \textsc{Spore} and show that it achieves high query efficiency with substantial per query information leakage. Experiments on multiple frontier LLMs show that \textsc{Spore} outperforms attack success rate over existing state-of-the-art (SOTA) schemes. It also maintains low attack cost and remains stable across different model parameter settings. We further evaluate the robustness of \textsc{Spore} against existing defense mechanisms. Our results show that \textsc{Spore} consistently bypasses both detection and strong safety alignment, demonstrating resilient performance in diverse defensive settings and real-world safety threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23711 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing Cui, Yu Yue, Ruiqing Fu, Hang Pan, Sicheng Sun, Zhuoyu Huang, Baohan Zhang, Haibin Zuo, Cong Wang, Licheng Cryptography and Security With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM) agents has become a critical issue. Existing privacy attacks against LLMs primarily target training data, while research on inference-time contextual privacy risks in LLM agent memory remains limited. Moreover, prior methods often incur high attack costs, requiring multiple queries or relying on white-box assumptions, which limits their practicality in real-world deployments. To address these issues, we propose a training-free privacy extraction attack targeting LLM agent memory, which we name \textsc{Spore}. \textsc{Spore} is compatible with both black-box and gray-box settings. In the black-box setting, \textsc{Spore} can efficiently extract a small candidate set via a single query to recover the original private information. In the gray-box setting, \textsc{Spore} allows the attacker to leverage multi-ranked tokens for more accurate and faster privacy extraction. We provide an information-theoretic analysis of \textsc{Spore} and show that it achieves high query efficiency with substantial per query information leakage. Experiments on multiple frontier LLMs show that \textsc{Spore} outperforms attack success rate over existing state-of-the-art (SOTA) schemes. It also maintains low attack cost and remains stable across different model parameter settings. We further evaluate the robustness of \textsc{Spore} against existing defense mechanisms. Our results show that \textsc{Spore} consistently bypasses both detection and strong safety alignment, demonstrating resilient performance in diverse defensive settings and real-world safety threats. |
| title | Spore: Efficient and Training-Free Privacy Extraction Attack on LLMs via Inference-Time Hybrid Probing |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2604.23711 |