arXiv AI August 24, 2026 By Junseok Kim, Nakyeong Yang, Kyomin Jung

Personalized Privacy Control in LLMs via Attention Head Intervention

The paper introduces the concept of personalized privacy for large language models, allowing user‑specific disclosure preferences to guide information sharing. It presents P3Bench, a benchmark that extends contextual privacy policies with personalized rules, and shows that existing prompt‑based methods often ignore these policies. To improve compliance, the authors propose “Repair”, an inference‑time attention head intervention that aligns model responses with user‑specific privacy rules.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 30

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

arXiv:2609.35937v1 Announce Type: cross Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice o...

By Lucas Biechy, C\'edric Eichler, H\'eber H. Arcolezi, Nicolas Anciaux

llms agents safety

arXiv AI

Aug 11

CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

arXiv:2608. 09164v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms.

By Bingcan Guo, Eryue Xu, Jijie Zhou, Zhiping Zhang, Tianshi Li

llms safety

arXiv AI

Jun 10

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

arXiv:2606. 09844v1 Announce Type: cross Abstract: Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor.

By Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah

llms agents benchmarks safety

arXiv AI

Jul 2

NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents

arXiv:2601. 14660v2 Announce Type: replace-cross Abstract: Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data.

By Saswat Das, Ferdinando Fioretto

llms agents safety

arXiv AI

Oct 27

InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants

InferPriv is a new benchmark that evaluates inferential privacy leakage in large language model (LLM) assistants. It measures whether an honest-but-curious recipient can deduce a user’s sensitive attributes from an LLM’s reply during routine tasks, a type of leakage not captured by existing direct‑leakage benchmarks. Testing five frontier models, the study finds that inferential leakage rates (16.2–25.2%) far exceed direct leakage, even when direct leakage is near zero under privacy‑aware prompts.

By Pengrun Huang, Yiwei Fu, Luoxi Meng, Kamalika Chaudhuri

llms agents benchmarks safety

arXiv Machine Learning

Sep 1

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization

arXiv:2608.30141v1 Announce Type: cross Abstract: Preference optimization is widely used to align large language models with human preferences, but preference-data composition may also influence priv...

By Dishu Yang, Jingjing Liu, Jize Li

llms reinforcement-learning efficiency safety

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PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

The Interlocutor Effect: Why LLMs Leak More Personal Data to Agents Than Humans

NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents

InferPriv: Evaluating Inferential Privacy Leakage in LLM Assistants

Balancing Privacy, Utility, and Safety in LLM Alignment through Preference Optimization