CVE-2025-27558

IEEE P802.11-REVme D1.1 through D7.0 allows FragAttacks against mesh networks. In mesh networks using Wi-Fi Protected Access WPA, WPA2, or WPA3 or Wired Equivalent Privacy WEP, an adversary can exploit this vulnerability to inject arbitrary frames towards devices that support receiving non-SSP...

Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queries

Retrieval-Augmented Generation RAG systems enhance large language models LLMs by incorporating external knowledge bases, but they are vulnerable to privacy risks from data extraction attacks. Existing extraction methods typically rely on malicious inputs such as prompt injection or jailbreaking,...

GDPRShield: AI-Powered GDPR Support for Software Developers in Small and Medium-Sized Enterprises

With the rapid increase in privacy violations in modern software development, regulatory frameworks such as the General Data Protection Regulation GDPR have been established to enforce strict data protection practices. However, insufficient privacy awareness among SME software developers...

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT

Industrial Internet of Things IIoT systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems IDS often rely on centralized architectures that raise concerns over data...

Outsourcing SAT-Based Verification Computations in Network Security

The emergence of cloud computing gives huge impact on large computations. Cloud computing platforms offer servers with large computation power to be available for customers. These servers can be used efficiently to solve problems that are complex by nature, for example, satisfiability SAT problem...

CVE-2025-27558

IEEE P802.11-REVme D1.1 through D7.0 allows FragAttacks against mesh networks. In mesh networks using Wi-Fi Protected Access WPA, WPA2, or WPA3 or Wired Equivalent Privacy WEP, an adversary can exploit this vulnerability to inject arbitrary frames towards devices that support receiving non-SSP...

Privacy-Preserving Socialized Recommendation Based on Multi-View Clustering in a Cloud Environment

Recommendation as a service has improved the quality of our lives and plays a significant role in variant aspects. However, the preference of users may reveal some sensitive information, so that the protection of privacy is required. In this paper, we propose a privacy-preserving, socialized,...

An Efficient Private GPT Never Autoregressively Decodes

The wide deployment of the generative pre-trained transformer GPT has raised privacy concerns for both clients and servers. While cryptographic primitives can be employed for secure GPT inference to protect the privacy of both parties, they introduce considerable performance overhead.To accelerat...

23andMe and its customers’ genetic data bought by a pharmaceutical org

The bankrupt genetic testing company 23andMe has been scooped up by drug producer Regeneron Pharmaceuticals for $256 million dollars. But why would a pharmaceutical company like Regeneron buy a bankrupt genetics testing company like 23andMe for such a large amount of money? Well, Regeneron is a...

Covert Attacks on Machine Learning Training in Passively Secure MPC

Secure multiparty computation MPC allows data owners to train machine learning models on combined data while keeping the underlying training data private. The MPC threat model either considers an adversary who passively corrupts some parties without affecting their overall behavior, or an adversa...

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption

Federated Learning FL is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key...

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents

The growing adoption of large language models LLMs has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex tasks directly on smartphones. However, the security implications of these agents remain largely unexplored. In this paper,...

Can Large Language Models Really Recognize Your Name?

Large language models LLMs are increasingly being used to protect sensitive user data. However, current LLM-based privacy solutions assume that these models can reliably detect personally identifiable information PII, particularly named entities. In this paper, we challenge that assumption by...

Moneros Decentralized P2P Exchanges: Functionality, Adoption, and Privacy Risks

Privacy-focused cryptocurrencies like Monero remain popular, despite increasing regulatory scrutiny that has led to their delisting from major centralized exchanges. The latter also explains the recent popularity of decentralized exchanges DEXs with no centralized ownership structures. These...

Beyond Text: Unveiling Privacy Vulnerabilities in Multi-Modal Retrieval-Augmented Generation

Multimodal Retrieval-Augmented Generation MRAG systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-based RAG privacy risks have been studied, multimodal data presents unique challenges. We provide the first systematic...

Vulnerability of Transfer-Learned Neural Networks to Data Reconstruction Attacks in Small-Data Regime

Training data reconstruction attacks enable adversaries to recover portions of a released model's training data. We consider the attacks where a reconstructor neural network learns to invert the random mapping between training data and model weights. Prior work has shown that an informed adversar...

A week in security (May 12 – May 18)

Last week on Malwarebytes Labs: Data broker protection rule quietly withdrawn by CFPB Meta sent cease and desist letter over AI training Google to pay $1.38 billion over privacy violations Android users bombarded with unskippable ads Last week on ThreatDown: ThreatDown introduces Firewall...

DynaNoise: Dynamic Probabilistic Noise Injection for Defending against Membership Inference Attacks

Membership Inference Attacks MIAs pose a significant risk to the privacy of training datasets by exploiting subtle differences in model outputs to determine whether a particular data sample was used during training. These attacks can compromise sensitive information, especially in domains such as...

Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy

Federated Learning with client-level differential privacy DP provides a promising framework for collaboratively training models while rigorously protecting clients' privacy. However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must...

WordPress plugin GDPR CCPA Compliance Support 安全漏洞

WordPress and WordPress plugin are both products of the WordPress Foundation. WordPress is a blogging platform developed in the PHP language. The platform supports personal blog sites on PHP and MySQL servers.WordPress plugin is an application plugin. A security vulnerability exists in WordPress...