arXiv AI June 29, 2026 By Shijing Hu, Liang Liu, Zhu Meng, Zhicheng Zhao

ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents

arXiv:2606. 28061v1 Announce Type: cross Abstract: Large language models (LLMs) have increasingly moved from standalone text generation systems to agents that invoke external tools, access environments, and execute multi-step tasks.

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

Jun 2

PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say

arXiv:2606. 00152v1 Announce Type: cross Abstract: LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users.

By Mingxuan Zhang, Jiahui Han, Dadi Guo, Songze Li, Guanchu Wang, Na Zou, Dongrui Liu, Xia Hu

llms agents benchmarks safety

arXiv AI

Jun 2

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512. 16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks.

By Yuxuan Qiao, Dongqin Liu, Hongchang Yang, Wei Zhou, Songlin Hu

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 26

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents

arXiv:2606. 26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a user's behalf.

By Nada Lahjouji, Ashwin Gerard Colaco

llms rag agents benchmarks safety data-engineering

arXiv AI

Jun 16

AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems

arXiv:2602. 11510v3 Announce Type: replace Abstract: Multi-agent Large Language Model (LLM) systems create privacy risks that current output-only benchmarks cannot measure.

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

llms agents benchmarks safety

arXiv AI

4d ago

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

Sep 15

Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control

arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...

By Minsun Shim, Ramisha Raida Karim, Ruthwik Jakkula, Kaiwen Zhou, Xin Liu, Xin Eric Wang, Zhou Li

llms agents benchmarks safety

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PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents

AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems

PrivacySkills: How Privacy Guidance Shapes Source Selection in LLM Agents

Confuse the Model, Control the Flow: Understanding and Mitigating Privacy Leakage from LLM Agents with Information Flow Control