CVE-2025-31242

A privacy issue in Apple's operating systems allows an application to potentially access sensitive user data due to inadequate private data redaction for log entries. This vulnerability affects several products and versions, including iOS 18.5 , iPadOS 18.5 , iPadOS 17.7.7 , macOS Sequoia 15.5 , ...

CVE-2025-31242

A privacy issue was addressed with improved private data redaction for log entries. This issue is fixed in iOS 18.5 and iPadOS 18.5, iPadOS 17.7.7, macOS Sequoia 15.5, macOS Sonoma 14.7.3, macOS Sonoma 14.7.6, macOS Ventura 13.7.3, macOS Ventura 13.7.6, tvOS 18.5, visionOS 2.5, watchOS 11.5. An a...

CVE-2025-31242

A privacy issue was addressed with improved private data redaction for log entries. This issue is fixed in iPadOS 17.7.7, macOS Ventura 13.7.6, macOS Sequoia 15.5, macOS Sonoma 14.7.6. An app may be able to access sensitive user data...

Mirror Mirror on the Wall, Have I Forgotten It All? A New Framework for Evaluating Machine Unlearning

Machine unlearning methods take a model trained on a dataset and a forget set, then attempt to produce a model as if it had only been trained on the examples not in the forget set. We empirically show that an adversary is able to distinguish between a mirror model a control model produced by...

Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization'S Impact on ML Fairness

Machine learning ML algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers. This raises critical privacy concerns. Anonymization techniques have emerged as a practical solution to address these...

Federated Large Language Models: Feasibility, Robustness, Security and Future Directions

The integration of Large Language Models LLMs and Federated Learning FL presents a promising solution for joint training on distributed data while preserving privacy and addressing data silo issues. However, this emerging field, known as Federated Large Language Models FLLM, faces significant...

PT-2025-20793 · Apple · Apple Macos

Name of the Vulnerable Software and Affected Versions: macOS versions prior to 15.5 Description: An information disclosure issue was addressed with improved privacy controls. This issue may allow an app to access sensitive user data. Recommendations: For versions prior to 15.5, update to macOS...

PT-2025-20770

Name of the Vulnerable Software and Affected Versions iPadOS versions prior to 17.7.7 macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description A privacy issue was addressed by removing sensitive data. A malicious app may be able...

Apple macOS 安全漏洞

Apple macOS is a suite of specialized operating systems developed for Mac computers by Apple Inc. in the United States. A security vulnerability exists in Apple macOS that stems from a logic issue that could lead to bypassing privacy preferences...

About the security content of iOS 18.5 and iPadOS 18.5

About the security content of iOS 18.5 and iPadOS 18.5 This document describes the security content of iOS 18.5 and iPadOS 18.5. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches ...

PT-2025-20733 · Apple · Macos Sequoia +3

Name of the Vulnerable Software and Affected Versions: macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A logic issue was addressed with improved checks, allowing an app to potentially bypass certain Privacy preferences...

PT-2025-20782 · Apple · Apple Macos

Name of the Vulnerable Software and Affected Versions: macOS versions prior to 15.5 Description: An information disclosure issue was addressed with improved privacy controls. This issue allows an app to potentially access sensitive user data. Recommendations: For versions prior to 15.5, update to...

Apple macOS 安全漏洞

Apple macOS is a specialized operating system developed for Mac computers by Apple Inc. in the United States. A security vulnerability exists in Apple macOS that stems from a privacy issue and could lead to applications accessing sensitive user data...

PT-2025-20788 · Apple · Ipados +4

Name of the Vulnerable Software and Affected Versions: iPadOS versions prior to 17.7.7 macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A privacy issue was addressed with improved private data redaction for log entries...

PT-2025-20774 · Apple · Ipados +1

Name of the Vulnerable Software and Affected Versions: iOS versions prior to 18.5 iPadOS versions prior to 18.5 Description: A privacy issue was addressed by removing sensitive data. Call history from deleted apps may still appear in spotlight search results. Recommendations: For iOS versions pri...

PT-2025-20741 · Apple · Macos Sonoma +3

Name of the Vulnerable Software and Affected Versions: macOS Ventura versions prior to 13.7.6 macOS Sequoia versions prior to 15.5 macOS Sonoma versions prior to 14.7.6 Description: A privacy issue was addressed with improved private data redaction for log entries, which may have allowed an app t...

Source Anonymity for Private Random Walk Decentralized Learning

This paper considers random walk-based decentralized learning, where at each iteration of the learning process, one user updates the model and sends it to a randomly chosen neighbor until a convergence criterion is met. Preserving data privacy is a central concern and open problem in decentralize...

Security of Internet of Agents: Attacks and Countermeasures

With the rise of large language and vision-language models, AI agents have evolved into autonomous, interactive systems capable of perception, reasoning, and decision-making. As they proliferate across virtual and physical domains, the Internet of Agents IoA has emerged as a key infrastructure fo...

DP-TRAE: a Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection

In the field of digital security, Reversible Adversarial Examples RAE combine adversarial attacks with reversible data hiding techniques to effectively protect sensitive data and prevent unauthorized analysis by malicious Deep Neural Networks DNNs. However, existing RAE techniques primarily focus...

Securing Genomic Data against Inference Attacks in Federated Learning Environments

Federated Learning FL offers a promising framework for collaboratively training machine learning models across decentralized genomic datasets without direct data sharing. While this approach preserves data locality, it remains susceptible to sophisticated inference attacks that can compromise...