Detecting Trojaned DNNs Via Spectral Regression Analysis

Modern DNNs are repeatedly fine-tuned to incorporate new data and functionality. This evolutionary workflow introduces a security risk when updated data cannot be fully trusted, as adversaries may implant Trojans during fine-tuning. We present MIST, a Trojan detection approach that analyzes how a...

GenAI-Driven Threat Detection with Microsoft Security Copilot

Defending against today's increasingly sophisticated cyberattacks requires security analysts to continuously translate evolving attacker tradecraft into detection logic. This places defenders in a reactive posture, requiring constantly updated expertise across an increasingly fragmented security...

MAL-2026-4606 Malicious code in martinez-polygon-clipping-tony (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector dabf04b2f99e28eb10740bd7459bf64513fac98a064b60071b1e7aabf8674dd0 Package name impersonates the legitimate martinez-polygon-clipping library: README, badges, and API surface are copied verbatim, while repository...

GHSA-3QCW-2RHX-2726 Turbo: Unexpected local code execution during Yarn Berry detection

Impact Turborepo can be vulnerable to arbitrary code execution when run in untrusted repositories that contain malicious Yarn configuration. In affected versions, package manager detection executed yarn --version from the project directory, which could cause Yarn to load and execute a...

Turbo: Unexpected local code execution during Yarn Berry detection

Impact Turborepo can be vulnerable to arbitrary code execution when run in untrusted repositories that contain malicious Yarn configuration. In affected versions, package manager detection executed yarn --version from the project directory, which could cause Yarn to load and execute a...

NPM: Turbo: Unexpected local code execution during Yarn Berry detection

NPM: Turbo: Unexpected local code execution during Yarn Berry detection vulnerability discovered by ? in WordPress Npm turbo versions = 1.1.0, 2.9.14...

WordPress Anomify AI – Anomaly Detection and Alerting plugin <= 0.3.6 - Cross-Site Request Forgery vulnerability

Cross-Site Request Forgery vulnerability discovered by Muhammad Nur Ibnu Hubab Ibnu - Pondok Teknologi in WordPress Plugin Anomify AI – Anomaly Detection and Alerting versions = 0.3.6...

Introducing Runtime Threat Detection for Google Cloud Run

Wiz Runtime Sensor support for Google Cloud Run Containers is now generally available, giving teams real-time threat detection and response for their serverless container workloads...

WordPress Anomify AI – Anomaly Detection and Alerting plugin <= 0.3.6 - Authenticated (Administrator+) Stored Cross-Site Scripting vulnerability

Authenticated Administrator+ Stored Cross-Site Scripting vulnerability discovered by Muhammad Nur Ibnu Hubab Ibnu - Pondok Teknologi in WordPress Plugin Anomify AI – Anomaly Detection and Alerting versions = 0.3.6...

YouTube wants your face to fight deepfakes

If you're worried about deepfake likenesses of yourself showing up online, you're not alone; YouTube is worried for you. It wants to protect you by having you upload a selfie video and government ID to its site. The idea is that the video giant will use its own AI to patrol the service for fake...

XAI FL-IDS: A Federated Learning and SHAP-Based Explainable Framework for Distributed Intrusion Detection Systems

An Intrusion Detection System IDS is vital in cybersecurity, detecting unauthorized activity across networks. With attacks on network layers increasing, stronger IDSs are needed. Yet most IDSs rely on centralized detection, forcing IoT nodes to ship data to a server, adding overhead and offering ...

Suricata IDPE 8.0.5

Suricata is a network intrusion detection and prevention engine developed by the Open Information Security Foundation and its supporting vendors. The engine is multi-threaded and has native IPv6 support. It's capable of loading existing Snort rules and signatures and supports the Barnyard and...

Hunting Vulnerability Variants in AI Infra: Measurement and Reference-Driven Detection

AI infra has become a shared execution layer for model training, deployment, and agent orchestration. Because many projects reimplement similar model-centric workflows, a vulnerability disclosed in one repository can recur as a variant in another repository with a related design. Yet the prevalen...

SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection

Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and legitimate content creators. Traditional fraud detection approaches struggle with a critical challenge: many legitimat...

CLSA-2026-1779122132 expat: Fix of CVE-2026-45186

CVE-2026-45186: fix quadratic runtime behavior in attribute collision detection...

How to Reduce Phishing Exposure Before It Turns into Business Disruption

What happens when a phishing email looks clean enough to pass through security, but dangerous enough to expose the business after one click? That is the gap many SOCs still struggle with: the attacks that leave teams unsure what was exposed, who else was targeted, and how far the risk has spread...

Continuous Detection, Continuous Response: Mate Security Redefines the Modern SOC

New York, USA, 18th May 2026, CyberNewswire...

From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

A No-Defense Defense against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?

Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning ML models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently robust Deep Neural Network DNN-based Network Intrusion Detection Systems NIDS,...

Federated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection

Federated learning for intrusion detection rests on a flawed premise: that every participating institution contributes equally to the shared model. In practice, a financial institution with mature security controls and low vulnerability exposure produces fundamentally different data than a...