AZL-67169 CVE-2025-39779 affecting package kernel 6.6.126.1-1

In the Linux kernel, the following vulnerability has been resolved: btrfs: subpage: keep TOWRITE tag until folio is cleaned btrfssubpagesetwriteback calls foliostartwriteback the first time a folio is written back, and it also clears the PAGECACHETAGTOWRITE tag even if there are still dirty block...

UBUNTU-CVE-2025-39779

In the Linux kernel, the following vulnerability has been resolved: btrfs: subpage: keep TOWRITE tag until folio is cleaned btrfssubpagesetwriteback calls foliostartwriteback the first time a folio is written back, and it also clears the PAGECACHETAGTOWRITE tag even if there are still dirty block...

Efficient Decoding Methods for Language Models on Encrypted Data

Large language models LLMs power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption HE enables computation on encrypted data for secure inference. However, neural text generation requires decoding methods like argmax and...

Fortifying the Agentic Web: a Unified Zero-Trust Architecture against Logic-Layer Threats

This paper presents a Unified Security Architecture that fortifies the Agentic Web through a Zero-Trust IAM framework. This architecture is built on a foundation of rich, verifiable agent identities using Decentralized Identifiers DIDs and Verifiable Credentials VCs, with discovery managed by a...

Enhancing Privacy in Decentralized Min-Max Optimization: a Differentially Private Approach

Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neighboring agents, eliminating the need for a central server. However, sharing model updates in such systems carry a ris...

Bidirectional TLS Handshake Caching for Constrained Industrial IoT Scenarios

While TLS has become the de-facto standard for end-to-end security, its use to secure critical communication in evolving industrial IoT scenarios is severely limited by prevalent resource constraints of devices and networks. Most notably, the TLS handshake to establish secure connections incurs...

A Provably Secure Network Protocol for Private Communication with Analysis and Tracing Resistance

Anonymous communication networks have emerged as crucial tools for obfuscating communication pathways and concealing user identities. However, their practical deployments face significant challenges, including susceptibility to artificial intelligence AI-powered metadata analysis, difficulties in...

Collusion Resistant DNS with Private Information Retrieval

There has been a growing interest in Internet user privacy, demonstrated by the popularity of privacy-preserving products such as Telegram and Brave, and the widespread adoption of HTTPS. The Domain Name System DNS is a key component of Internet-based communication and its privacy has been...

CVE-2025-38360

CVE-2025-38360 is a Linux kernel vulnerability affecting drm/amd/display where insufficient HUBP/DSC domain checks could leave the HUBP domain mismatched to a DSC instance, risking an undefined tile state and potential system hang. The advisory states that the fix adds more checks to ensure the H...

Adaptive Network Security Policies Via Belief Aggregation and Rollout

Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response procedures and modifications to access controls, among others. Reinforcement learning methods have been proposed for...

Mitigating Watermark Stealing Attacks in Generative Models Via Multi-Key Watermarking

Watermarking offers a promising solution for GenAI providers to establish the provenance of their generated content. A watermark is a hidden signal embedded in the generated content, whose presence can later be verified using a secret watermarking key. A threat to GenAI providers are \emphwaterma...

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Federated Learning FL faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced...

Efficient Unlearning with Privacy Guarantees

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning ML models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data...

Balancing Privacy and Utility in Correlated Data: a Study of Bayesian Differential Privacy

Privacy risks in differentially private DP systems increase significantly when data is correlated, as standard DP metrics often underestimate the resulting privacy leakage, leaving sensitive information vulnerable. Given the ubiquity of dependencies in real-world databases, this oversight poses a...

ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

CVE-2025-6545

A flaw was found in the npm pbkdf2 library, allowing signature spoofing. When executing in javascript engines other than Nodejs or Nodejs when importing pbkdf2/browser, certain algorithms will silently fail and return invalid data. The return values are predictable, which undermines the security...

Towards Provable (In)Secure Model Weight Release Schemes

Recent secure weight release schemes claim to enable open-source model distribution while protecting model ownership and preventing misuse. However, these approaches lack rigorous security foundations and provide only informal security guarantees. Inspired by established works in cryptography, we...

Bridging Unsupervised and Semi-Supervised Anomaly Detection: a Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Anomaly detection AD is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to train classifiers to distinguish normal data from synthetic anomalies. We extend this principle to semi-supervised AD,...

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...

GeoClip: Geometry-Aware Clipping for Differentially Private SGD

Differentially private stochastic gradient descent DP-SGD is the most widely used method for training machine learning models with provable privacy guarantees. A key challenge in DP-SGD is setting the per-sample gradient clipping threshold, which significantly affects the trade-off between privac...