35650 matches found
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...
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...
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 ...
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,...
Explainable Machine Learning for Phishing Detection on Heterogeneous Datasets with MCP-Enabled Deployment
With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes...
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...
Three Heads Are Better Than One: A Multi-Perspective Reasoning Framework for Enhanced Vulnerability Detection
Automated vulnerability detection is crucial for enhancing software security by identifying potential flaws that attackers could exploit, thereby reducing the reliance on labor-intensive manual code audits. Recent advancements have shifted towards leveraging large language models LLMs for...
Devilray: A Systematic Adversarial Model Revealing Blind Spots in Fake Base Station Detection
Fake Base Station FBS detection has been a critical focus of cellular security research for over two decades. However, significant financial and regulatory barriers to accessing commercial FBS C-FBS devices have limited direct visibility into real-world operations, forcing detection systems to be...
