CVE-2025-6431

When a link can be opened in an external application, Firefox for Android will, by default, prompt the user before doing so. An attacker could have bypassed this prompt, potentially exposing the user to security vulnerabilities or privacy leaks in external applications. This bug only affects...

The Impact of the Russia-Ukraine Conflict on the Cloud Computing Risk Landscape

The Russian invasion of Ukraine has fundamentally altered the information technology IT risk landscape, particularly in cloud computing environments. This paper examines how this geopolitical conflict has accelerated data sovereignty concerns, transformed cybersecurity paradigms, and reshaped clo...

Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing

We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption FHE and multiparty computation MPC, as well as statistic...

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation

Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis PPDS. However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate syntheti...

Verifiable Unlearning on Edge

Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edg...

Machine Learning with Privacy for Protected Attributes

Differential privacy DP has become the standard for private data analysis. Certain machine learning applications only require privacy protection for specific protected attributes. Using naive variants of differential privacy in such use cases can result in unnecessary degradation of utility. In...

Mozilla -- persistent UUID that identifies browser

[email protected] reports: An attacker who enumerated resources from the WebCompat extension could have obtained a persistent UUID that identified the browser, and persisted between containers and normal/private browsing mode, but not profiles. This vulnerability affects Firefox 140, Firefox E...

Retrieval-Confused Generation Is a Good Defender for Privacy Violation Attack of Large Language Models

Recent advances in large language models LLMs have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack PVA, revealed by Staab et al., introduces serious personal privacy issues...

PrivacyXray: Detecting Privacy Breaches in LLMs through Semantic Consistency and Probability Certainty

Large Language Models LLMs are widely used in sensitive domains, including healthcare, finance, and legal services, raising concerns about potential private information leaks during inference. Privacy extraction attacks, such as jailbreaking, expose vulnerabilities in LLMs by crafting inputs that...

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...

ZK-SERIES: Privacy-Preserving Authentication Using Temporal Biometric Data

Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as tim...

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning

Whitepaper called Secure Multi-Key Homomorphic Encryption With Application To Privacy-Preserving Federated Learning...

PT-2025-26728

Name of the Vulnerable Software and Affected Versions: Firefox for Android versions prior to 140 Description: The issue allows an attacker to bypass the default prompt that appears when a link can be opened in an external application, potentially exposing the user to security risks or privacy lea...

A week in security (June 15 – June 21)

Last week on Malwarebytes Labs: The data on denying social media for kids re-air Lock and Code S06E12 Reddit’s new AI-powered tools scan your posts to serve you better ads Smart air fryers ordered to stop invading our digital privacy WhatsApp to start targeting you with ads Scammers hijack websit...

Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models

Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak ICML 2023. They define near access-freeness NAF and propose it as...

Private Model Personalization Revisited

Whitepaper called Private Model Personalization Revisited...

Network Structures As an Attack Surface: Topology-Based Privacy Leakage in Federated Learning

Federated learning systems increasingly rely on diverse network topologies to address scalability and organizational constraints. While existing privacy research focuses on gradient-based attacks, the privacy implications of network topology knowledge remain critically understudied. We conduct th...

HARPT: a Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps

We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and...

Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus

Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval PIR system designed to preserve user...

Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Cybersecurity, Privacy & AI Safety: a Comprehensive Survey, Roadmap & Implementation Blueprint

Large Language Models LLMs and generative AI GenAI systems such as ChatGPT, Claude, Gemini, LLaMA, and Copilot, developed by OpenAI, Anthropic, Google, Meta, and Microsoft are reshaping digital platforms and app ecosystems while introducing key challenges in cybersecurity, privacy, and platform...