Are You Optimizing Your NHIs for Cyber Attack Resilience? When faced with the myriad challenges of cybersecurity, have you considered how to optimize your Non-Human Identities (NHIs) for better protection against cyber threats? NHIs, representing machine identities, are increasingly targeted by cybercriminals in their quest for unauthorized system access. They comprise encrypted passwords, tokens, or […]

A new phishing automation platform named Quantum Route Redirect is using around 1,000 domains to steal Microsoft 365 users' credentials. [...]

Tenable is recognized as a Leader in the 2025 Gartner Magic Quadrant for Exposure Assessment Platforms. This placement acknowledges Tenable's completeness of vision and ability to execute in the exposure management field. Tenable views this recognition as a validation of customer trust and their joint effort to reduce cyber exposure. The company's evolution was driven by addressing the growing attack surface and customer challenges like alert fatigue. Tenable One, the industry's first exposure management platform, emerged from this evolution, providing a comprehensive view of risk. The platform helps organizations identify exposures, prioritize risks, and remediate vulnerabilities effectively. Tenable's approach stems from listening to customers and shaping solutions that address real-world cybersecurity challenges. This customer-centric approach led to the development of Nessus and its evolution into a vulnerability management leader. Tenable aims to continue expanding its offerings, leveraging AI to enhance security solutions and address AI security challenges. The company works in partnership with customers to provide tools and insights for proactive cyber exposure reduction.

The government shutdown's end brought back a cybersecurity law. The U.S. military is developing a new cyber forces model. North Korean hackers used hijacked Google accounts to take over South Korean phones. The EU is reducing AI regulations due to pressure from tech companies. A serious vulnerability was found in Monsta FTP, endangering servers. A new Android spyware campaign targeted Samsung devices in the Middle East. Intelligence cooperation among Five Eyes nations is reportedly weakening. NSO Group, an Israeli spyware firm, has appointed a former U.S. ambassador as its new chairman. The CyberWire Daily podcast featured a segment on uncertainty regarding the federal Cyber Corp program. The episode encourages listeners to provide feedback and consider sponsoring the show.

Google's Mandiant Threat Defense on Monday said it discovered n-day exploitation of a now-patched security flaw in Gladinet's Triofox file-sharing and remote access platform. The critical vulnerability, tracked as CVE-2025-12480 (CVSS score: 9.1), allows an attacker to bypass authentication and access the configuration pages, resulting in the upload and execution of arbitrary payloads.  The

Salespeople today need to learn how to use AI so each one of them can become a superhuman. Sales technology was about features a decade ago, now it’s about accuracy. As AI systems handle more of the repetitive work, scoring leads, predicting pipeline health, they’re only as good as the data they ingest.

The North Korea-affiliated threat actor known as Konni (aka Earth Imp, Opal Sleet, Osmium, TA406, and Vedalia) has been attributed to a new set of attacks targeting both Android and Windows devices for data theft and remote control. "Attackers impersonated psychological counselors and North Korean human rights activists, distributing malware disguised as stress-relief programs," the Genians

Black Friday sales are an excellent opportunity to upgrade your headphones or earbuds. Here are the best early deals we've found from Sony, Bose, Apple, and more.

If your iPad feels slow, clearing the cache can speed it up and free up storage. Here's how.

GlassWorm malware resurfaces in Open VSX and GitHub, infecting VS Code extensions weeks after its removal from the official marketplace. GlassWorm malware has resurfaced on the Open VSX registry and newly appeared in GitHub repositories, infecting three more VS Code extensions just weeks after its removal from the official marketplace, Koi Security researchers warn. In […]

CISA ordered U.S. federal agencies today to patch a critical Samsung vulnerability that has been exploited in zero-day attacks to deploy LandFall spyware on devices running WhatsApp. [...]

There are docks, and there are docks that can handle professional workloads. This one is pro-grade in every regard.

A Russian national will plead guilty to acting as an initial access broker (IAB) for Yanluowang ransomware attacks that targeted at least eight U.S. companies between July 2021 and November 2022. [...]

Resolution acquiesced to by 8 Dems includes CISA Act funding, layoff reversals, and could be easily undone The US Senate voted on Sunday to advance a short-term funding bill for the federal government, moving the country closer to ending its longest-ever shutdown. Part of the spending bill also restores critical cybersecurity programs that lapsed as the shutdown began. …

Some organizations report a decrease in their workforce, while others are hiring for AI-related jobs - two roles in particular.

This case study examines how gender bias manifests in podcast recommendation systems through latent feature representation (LFR) models. Using a dataset of 19,000 users and 31,000 podcasts, the researchers analyzed associations between user gender and podcast genres like true crime and sports. By comparing models trained with and without gender as a feature, they visualized bias directions, tested classification scenarios, and flagged statistically significant gender associations in item embeddings. The findings reveal how algorithmic systems can unintentionally replicate societal gender preferences, underscoring the need for fairness auditing and bias mitigation in real-world recommender models.

This article introduces a four-step methodology—scope, identify, implement, and flag—for auditing attribute association bias in latent factor recommendation (LFR) algorithms. Built upon the SIIM framework, it helps practitioners determine what to analyze, apply appropriate evaluation methods, and statistically test for significance in detected bias. The framework integrates both qualitative (e.g., PCA visualization) and quantitative (e.g., WEAT, R-RIPA, classification) tools to measure bias strength and direction. By operationalizing bias detection in a practical, step-by-step manner, it provides researchers and engineers with a replicable process for identifying and validating fairness issues in real-world AI recommendation systems.

This paper introduces a framework to evaluate representation bias within latent factor recommendation (LFR) models, focusing on how user and item embeddings may encode implicit associations with sensitive attributes like gender. Unlike prior research that centers on performance metrics or exposure bias, this work examines attribute association bias and demonstrates its measurement through an industry case study in podcast recommendations. The goal is to help practitioners audit, interpret, and mitigate bias propagation across multi-stage recommender pipelines, promoting greater fairness and transparency in AI systems.

This paper introduces an evaluation framework to measure attribute association bias in recommendation systems, expanding fairness research beyond traditional allocation harms. Building on NLP bias-detection methods, it quantifies representational harms in latent factor models, focusing on gender associations as a case study. By analyzing how stereotypes can be encoded and amplified through vector embeddings, the study enhances transparency and offers new directions for mitigating bias in AI-driven recommendations.

This paper introduces a practical evaluation framework for detecting attribute association bias (AAB) in latent factor recommendation systems — a subtle but critical form of representation bias where sensitive attributes (like gender) become entangled in model embeddings. By adapting methods from NLP bias research, the authors propose four evaluation strategies—bias vector creation, AAB metrics, classification explanations, and latent space visualization—to help practitioners quantify and interpret bias in recommendation outputs. Tested on a real-world podcast recommendation model, the framework exposes significant user gender bias even after mitigation attempts, underscoring the need for systematic AAB audits in AI-driven personalization systems.

Buburuza CEO Daniel Zakharov predicts that AI financial assistants will soon replace manual money management. Instead of stressing over budgets and bills, users will co-create financial strategies that AI executes and monitors—eliminating emotional spending, shame, and decision fatigue. The future of finance is collaborative, not controlling.

Humanity Protocol has integrated Mastercard’s open finance technology into its Human ID system, empowering users to verify income, assets, and financial eligibility seamlessly. Using zero-knowledge cryptography, users can access credit, RWAs, and DeFi securely—bridging traditional and blockchain finance with privacy-preserving digital identity.

One company alone was hit with more than 4,200 emails More than 5,000 businesses that use Facebook for advertising were bombarded by tens of thousands of phishing emails in a credential- and data-stealing campaign.…

A critical vulnerability in the popular expr-eval JavaScript library, with over 800,000 weekly downloads on NPM, can be exploited to execute code remotely through maliciously crafted input. [...]

Intel, the leading computer chip maker, has filed a lawsuit seeking at least $250,000 in damages from a…