The study investigates how users perceive the helpfulness and privacy-preservation of large language model (LLM) responses in privacy-sensitive scenarios. Using 94 participants and 90 PrivacyLens scenarios, researchers found that users’ evaluations of identical LLM outputs varied widely, whereas five proxy LLM judges showed high agreement but low correlation with user judgments. The results suggest that proxy LLMs cannot reliably estimate users’ diverse perceptions of utility and privacy, highlighting the need for more user-centered evaluation methods.
By Xiaoyuan Wu, Roshni Kaushik, Wenkai Li, Lujo Bauer, Koichi Onoue
The paper proposes a methodological framework to assess large language models (LLMs) as surrogate experts in security surveys, particularly for Security Operations Centres (SOCs). By comparing persona-based and aggregate LLM-generated responses to real SOC professional data, the study evaluates stability, inter-model agreement, and alignment with human answers. Findings reveal that while LLMs produce internally consistent responses, they systematically diverge from experts, showing reduced variance, central tendency bias, and homogenised opinions, indicating they are suitable for piloting and hypothesis generation but not for replacing expert elicitation.
By Despoina Giarimpampa, Roland Meier, Tegawend\'e F. Bissyand\'e, Vincent Lenders, Jacques Klein
arXiv:2604. 09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasingly marketing autonomous AI solutions for SOCs.
By Souradip Nath, Chih-Yi Huang, Aditi Ganapathi, Kashyap Thimmaraju, Jaron Mink, Gail-Joon Ahn
arXiv:2607. 18496v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks.
By Shufan Chai, Liangliang Sun, Jessica Staddon
arXiv:2609.00578v1 Announce Type: new
Abstract: Large Language Models (LLMs) can solve complex problems, but their misuse in high-risk domains can lead to severe consequences. Model providers therefo...
By Rui Yang, Yang Hong, Yichao Xu, Zhengyu Liu, Ziyang Li, Yinzhi Cao
WildSEEK is a new dataset of 3,000 real user information‑seeking queries, manually annotated for risk‑sensitive domains and whether the query is factoid or analytical. The accompanying evaluation framework tests LLM responses against four failure criteria—sycophantic behavior, overreliance, a default US‑centric perspective, and poor handling of vulnerable populations—finding higher failure rates for analytical queries. The authors also train classifiers on WildSEEK to analyze over 1.8 million realistic queries, revealing that more than a third are high‑risk and often analytical.
By Tanise Ceron, Joachim Baumann, Elisa Bassignana, Berat Cabuk, Dirk Hovy, Debora Nozza
Large Language Models (LLMs) can solve complex problems, but their misuse in high-risk domains can lead to severe consequences. Model providers therefore restrict assistance for potentially harmful re...
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
arXiv:2607. 16227v1 Announce Type: new Abstract: LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks.
By Ruppikha Sree Shankar, Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan, Troy Paulus Asia, Saptarshi Sengupta
The paper introduces concept provenance, a framework that categorizes query concepts into backstory-supported, human-central, human-tail, and candidate answer-side zones to detect knowledge intrusion in LLM-generated search queries. Applying this framework to 77,004 queries across 100 topics, 8 LLMs, and 5 prompt conditions, the authors find that 7.40% of non-generic concepts are candidate answer-side and appear in 97 of 100 topics, with human validation revealing 68.2% relaxed precision and equal contributions from knowledge and deployment intrusion. The study shows that while prompt conditions do not eliminate intrusion, post-generation concept-provenance selection can achieve 99% elimination, positioning concept provenance as a diagnostic tool rather than an evaluation predictor.
By Chenglong Ma, Xinye Wanyan, Danula Hettiachchi, Ziqi Xu, Jeffrey Chan
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
By Sunnie S. Y. Kim, Margit Bowler, Leon A Gatys
arXiv:2609.14849v1 Announce Type: cross
Abstract: We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' aut...
By Myra Cheng, Lujain Ibrahim, Grace Liu, Michelle S. Lam, Vishakh Padmakumar, Nick Madibekov, Diyi Yang, Dan Jurafsky