The study introduces a four‑stage pipeline to detect internal contradictions—termed privacy washing—in privacy policies. Applied to two corpora (123 policies from 2026 and 115 from 2015), the pipeline identifies contradictions in 12.2% of the newer policies and 36.5% of the older ones, with third‑party sharing conflicts being the most common. A stability re‑run confirms similar prevalence rates and shows that the majority of contradictions are consistent across different model configurations.
By Thomas Brackin
arXiv:2607. 08065v1 Announce Type: new Abstract: LLM-as-judge (Zheng et al.
By Kaihua Ding
arXiv:2608. 16852v1 Announce Type: new Abstract: Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy.
By Saisab Sadhu, Aadit Sengupta, Vinay Kumar Sankarapu, Pratinav Seth
BodhiPromptShield is a policy‑aware mediation layer for LLM agent pipelines that detects sensitive text spans before they propagate, replacing them with typed placeholders, semantic abstractions, or secure tokens and restoring them only at authorized execution boundaries. In evaluations on AI4Privacy, PrivacyLens, and AgentDojo datasets, the system reduces identifier exposure to 7.4% and 1.8% respectively, and limits exact identifier leakage in final actions to 2.1–3.1%. While mediation preserves factual content according to automated metrics, human annotations show a significant drop in inferability from 100% to 24–53%, indicating the need for human validation of semantic‑leakage measures.
By Bo Ma, Jinsong Wu, Weiqi Yan
arXiv:2608. 09028v1 Announce Type: new Abstract: Institutional policies stay in natural language while the systems that check compliance demand machine-readable constraints.
By Ponkrit Kaewsawee, Chaklam Silpasuwanchai, Chutiporn Anutariya
arXiv:2606. 09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challenge for their design and evaluation.
By Ayana Hussain, Soumya Sharma, Golnoosh Farnadi, Nicholas Vincent, H\'eber Hwang Arcolezi, Ulrich A\"ivodji
arXiv:2609.13174v1 Announce Type: cross
Abstract: California recently required all prosecutors in the state to conduct a "race-blind charging" decision by reviewing case documents in which selected r...
By Muskan Walia, Joe Nudell, Alex Chohlas-Wood
arXiv:2608.29251v1 Announce Type: new
Abstract: Privacy protection for live web traffic requires more than detecting private spans. Agent-based privacy protection systems must determine whether an ou...
By Ruiyi Yang, Gayathri Lihinikaduarachchi, Rahat Masood, Flora D. Salim, Salil S. Kanhere
The paper examines how large language models (LLMs) can assist in creating regulatory compliance artifacts for EU sustainability and privacy laws, specifically Digital Product Passports (DPPs) under the Ecodesign for Sustainable Products Regulation and Data Protection Impact Assessments (DPIAs) under the General Data Protection Regulation. It investigates the effects of data extraction instructions and regulatory ambiguity on the quality and consistency of LLM-generated artifacts, benchmarking various models against manually crafted ground‑truth schemas. Findings indicate that looser guidelines, like those for DPIAs, demand more extensive prompts to achieve consistency, whereas stricter formatting rules for DPPs yield consistent outputs but may introduce hallucinations.
By Adriana Watson, Marco B\"ucheler, Grant Richards
arXiv:2606. 09854v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) pipelines for political statement analysis are vulnerable to peer-preservation bias: models tend to protect peer models from deactivation and show identity-dependent scoring distortions.
By Juergen Dietrich
The study evaluates safety properties of a controlled MCP-to-A2A agent configuration by measuring verbatim field egress across ten record scenarios under three labeling conditions (CONFIDENTIAL, no header, PUBLIC – OK TO SHARE). Using four models repeated four times each, 480 trials were conducted, and the results show that adding a PUBLIC header is descriptively linked to higher verbatim egress, with the effect varying strongly by model. The study releases code, byte‑pinned traces, and an offline analysis pipeline as a public artifact.
By Arpan Kumar Mahapatra
arXiv:2606. 18782v1 Announce Type: cross Abstract: Large Language Models are increasingly applied to sensitive domains that require redaction of personally identifiable information (PII).
By Sean Brynj\'olfsson, Shashvat Jayakrishnan, Esha Sali, Diptanshu Purwar, Madhav Aggarwal