CVE-2025-43502

A privacy issue was addressed by removing sensitive data. This issue is fixed in iOS 26.1 and iPadOS 26.1, macOS Tahoe 26.1, visionOS 26.1, Safari 26.1. An app may be able to bypass certain Privacy preferences...

CVE-2025-43439

CVE-2025-43439 affects Apple platforms (iOS, iPadOS, visionOS). Description and connected references indicate a privacy issue where an app may fingerprint the user, addressed by removing sensitive data. Fixes are included in iOS 18.7.2, iPadOS 18.7.2, iOS 26.1, iPadOS 26.1, and visionOS 26.1. Pra...

About the security content of Safari 26.1

About the security content of Safari 26.1 This document describes the security content of Safari 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are available...

About the security content of Safari 26.1

About the security content of Safari 26.1 This document describes the security content of Safari 26.1. About Apple security updates For our customers' protection, Apple doesn't disclose, discuss, or confirm security issues until an investigation has occurred and patches or releases are available...

EUVD-2020-27951

Malware in sbrugna...

EUVD-2019-3417

Malware in sbrugna...

EUVD-2023-23482

Malicious code in bioql PyPI...

EUVD-2022-32609

Malicious code in bioql PyPI...

EUVD-2022-2877

Malicious code in bioql PyPI...

CVE-2023-53232

CVE-2023-53232 affects the Linux kernel MT7921/MT76 stack. The vulnerability arises from a kernel panic due to accessing unallocated eeprom.data, leading to a NULL dereference in mt7921_mcu_parse_response. A fix is present: the MT7921 driver no longer uses eeprom.data, and the code referencing it...

A New Platform Offers Privacy Tools to Millions of Public Servants

From data-removal services to threat monitoring, the Public Service Alliance says its new marketplace will help public servants defend themselves in an era of data brokers and political violence...

CVE-2025-0011

Improper removal of sensitive information before storage or transfer in AMD Crash Defender could allow an attacker to obtain kernel address information potentially resulting in loss of confidentiality...

CVE-2025-0011

CVE-2025-0011 concerns AMD Crash Defender. The issue is improper removal of sensitive information before storage or transfer, potentially revealing kernel address information and harming confidentiality. The CVE is rated CVSS v3.1 with a base score of 3.3 (LOW); attack vector LOCAL, required priv...

Linux Distros Unpatched Vulnerability : CVE-2024-9026

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - In PHP versions 8.1. before 8.1.30, 8.2. before 8.2.24, 8.3. before 8.3.12, when using PHP-FPM SAPI and it is configured to catch workers output through...

CVE-2025-53112 GLPI's incomprehensive permission checks can lead to data removal from allowed users

GLPI is a Free Asset and IT Management Software package, that provides ITIL Service Desk features, licenses tracking and software auditing. In versions 9.1.0 through 10.0.18, a lack of permission checks can result in unauthorized removal of some specific resources. This is fixed in version 10.0.1...

CVE-2025-53112 GLPI's incomprehensive permission checks can lead to data removal from allowed users

GLPI is a Free Asset and IT Management Software package, that provides ITIL Service Desk features, licenses tracking and software auditing. In versions 9.1.0 through 10.0.18, a lack of permission checks can result in unauthorized removal of some specific resources. This is fixed in version 10.0.1...

CVE-2025-53112

GLPI (versions 9.1.0–10.0.18) has a permission-checking weakness that can allow unauthorized removal of specific resources. The root cause is lack of permission checks on certain deletion actions. The issue is fixed in version 10.0.19. Mitigation is to upgrade to 10.0.19 or apply vendor-provided ...

Recalling the Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy

Machine Unlearning MU technology facilitates the removal of the influence of specific data instances from trained models on request. Despite rapid advancements in MU technology, its vulnerabilities are still under explored, posing potential risks of privacy breaches through leaks of ostensibly...

SoK: Machine Unlearning for Large Language Models

Large language model LLM unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of techniques have been proposed, including Gradient Ascent, model editing, and...

Certified Unlearning for Neural Networks

We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regulatory requirements such as the "right to be forgotten." Unfortunately, existing methods rely on restrictive assumptio...