新研究前沿：针对大语言模型的参数高效微调，如何通过经验隐私审计量化个体记忆泄露风险，提升数据安全。
arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization…
针对聊天智能体记忆的成员推理攻击工具，助你评估AI系统的隐私泄露风险
arXiv:2605.27825v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) test whether a target data record belongs to a system's private …
系统梳理LLM预训练数据暴露的成员推理、数据污染与安全影响，已被NLDB 2025接收，是研究数据隐私的必备综述。
arXiv:2605.26133v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become the predominant paradigm in NLP, advancing both research an…
大模型分词器也能泄露训练隐私？新研究揭示针对LLM分词器的成员推理攻击
arXiv:2510.05699v4 Announce Type: replace-cross Abstract: Membership inference attacks (MIAs) are widely used to assess the privacy risks associated w…
针对视觉语言模型的单样本黑盒成员推理攻击，利用跨模态语义对齐检测模型记忆风险，为数据隐私安全提供新评估方法。
arXiv:2605.17341v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved remarkable success, yet their reliance on massive datasets…