CVE-2025-58262 WordPress Sweet Energy Efficiency plugin <= 1.0.8 - Cross Site Request Forgery (CSRF) vulnerability

Cross-Site Request Forgery CSRF vulnerability in WPDirectoryKit Sweet Energy Efficiency sweet-energy-efficiency allows Stored XSS.This issue affects Sweet Energy Efficiency: from n/a through = 1.0.8...

CVE-2025-58262 WordPress Sweet Energy Efficiency plugin <= 1.0.8 - Cross Site Request Forgery (CSRF) vulnerability

Cross-Site Request Forgery CSRF vulnerability in WPDirectoryKit Sweet Energy Efficiency sweet-energy-efficiency allows Stored XSS.This issue affects Sweet Energy Efficiency: from n/a through = 1.0.8...

CVE-2025-58262

Technical details for CVE-2025-58262 are not publicly available in the provided documents. The initial entry mentions a CSRF issue and Stored XSS in the Sweet Energy Efficiency plugin for WPDirectoryKit (

PT-2025-38925

Name of the Vulnerable Software and Affected Versions wpdirectorykit Sweet Energy Efficiency versions through 1.0.6 Description A Cross-Site Request Forgery CSRF issue exists in wpdirectorykit Sweet Energy Efficiency, which also allows Stored Cross-Site Scripting XSS. Recommendations Update...

WordPress plugin Sweet Energy Efficiency 跨站请求伪造漏洞

WordPress and WordPress plugin are both products of the WordPress Foundation.WordPress is a blogging platform developed in the PHP language. The platform has the ability to host personal blog sites on PHP and MySQL based servers.WordPress plugin is an application plugin. A cross-site request...

Microsoft Defender delivered 242% return on investment over three years​​

The latest Forrester Total Economic Impact™ TEI study reveals a 242% return on investment ROI over three years for organizations that chose Microsoft Defender. It helps security leaders consolidate tools, reduce overhead, and empower their security operations SecOps teams with operational...

Multi-Channel Secure Communication Framework for Wireless IoT (MCSC-WoT): Enhancing Security in Internet of Things

In modern smart systems, the convergence of the Internet of Things IoT and Wireless of Things WoT have been revolutionized by offering a broad level of wireless connectivity and communication among various devices. Hitherto, this greater interconnectivity poses important security problems,...

AI in Government

Just a few months after Elon Musk's retreat from his unofficial role leading the Department of Government Efficiency DOGE, we have a clearer picture of his vision of government powered by artificial intelligence, and it has a lot more to do with consolidating power than benefitting the public. Ev...

Mask-GCG: Are All Tokens in Adversarial Suffixes Necessary for Jailbreak Attacks?

Jailbreak attacks on Large Language Models LLMs have demonstrated various successful methods whereby attackers manipulate models into generating harmful responses that they are designed to avoid. Among these, Greedy Coordinate Gradient GCG has emerged as a general and effective approach that...

VulRTex: a Reasoning-Guided Approach to Identify Vulnerabilities from Rich-Text Issue Report

Software vulnerabilities exist in open-source software OSS, and the developers who discover these vulnerabilities may submit issue reports IRs to describe their details. Security practitioners need to spend a lot of time manually identifying vulnerability-related IRs from the community, and the...

A Technical Review on Comparison and Estimation of Steganographic Tools

Steganography is technique of hiding a data under cover media using different steganography tools. Image steganography is hiding of data Text/Image/Audio/Video under a cover as Image. This review paper presents classification of image steganography and the comparison of various Image steganograph...

Towards Scalable and Interpretable Mobile App Risk Analysis Via Large Language Models

Mobile application marketplaces are responsible for vetting apps to identify and mitigate security risks. Current vetting processes are labor-intensive, relying on manual analysis by security professionals aided by semi-automated tools. To address this inefficiency, we propose Mars, a system that...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Federated Learning FL is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...

Accelerating Secure Enterprise Kubernetes Adoption

Learn how LKE-E solves critical problems while providing streamlined adoption, operational simplicity, and cost efficiency at scale...

CVE-2025-38509

In the Linux kernel, the following vulnerability has been resolved: wifi: mac80211: reject VHT opmode for unsupported channel widths VHT operating mode notifications are not defined for channel widths below 20 MHz. In particular, 5 MHz and 10 MHz are not valid under the VHT specification and must...

Efficient Mediated Multiparty Semi-Quantum Secret Sharing Protocol Based on Single-Qubit Reordering

Typical multiparty semi-quantum secret sharing MSQSS protocols require the dealer to possess full quantum capabilities, while the classical users usually need to perform three operations. To address this practical limitation, this paper introduces a new mediated MSQSS protocol that enables Alice,...

From Learning to Unlearning: Biomedical Security Protection in Multimodal Large Language Models

The security of biomedical Multimodal Large Language Models MLLMs has attracted increasing attention. However, training samples easily contain private information and incorrect knowledge that are difficult to detect, potentially leading to privacy leakage or erroneous outputs after deployment. An...

DUP: Detection-Guided Unlearning for Backdoor Purification in Language Models

As backdoor attacks become more stealthy and robust, they reveal critical weaknesses in current defense strategies: detection methods often rely on coarse-grained feature statistics, and purification methods typically require full retraining or additional clean models. To address these challenges...

Using LLMs as a reverse engineering sidekick

This research explores how large language models LLMs can complement, rather than replace, the efforts of malware analysts in the complex field of reverse engineering. LLMs may serve as powerful assistants to streamline workflows, enhance efficiency, and provide actionable insights during malware...

Hierarchical Graph Neural Network for Compressed Speech Steganalysis

Steganalysis methods based on deep learning DL often struggle with computational complexity and challenges in generalizing across different datasets. Incorporating a graph neural network GNN into steganalysis schemes enables the leveraging of relational data for improved detection accuracy and...