WordPress Plugin CoSchool LMSSQL Injection Vulnerability

WordPress is a blogging platform developed using the PHP language. The platform has the ability to set up a personal blog site on a PHP and MySQL based server.WordPress plugin is an application plugin. A SQL injection vulnerability exists in the WordPress plugin CoSchool LMS, which stems from the...

DrAttack

DrAttack: Prompt Decomposition and Reconstruction Makes Powerf...

Secure Low-Altitude Maritime Communications Via Intelligent Jamming

Low-altitude wireless networks LAWNs have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles UAVs serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and...

CVE-2025-12098

CVE-2025-12098 affects Academy LMS Pro (WordPress plugin) up to version 3.3.8, exposing sensitive data via enqueue_social_login_script. Unauthenticated attackers could exfiltrate secrets (e.g., Facebook App Secret) when Facebook Social Login is enabled. Mitigation: update to 3.3.9 or later (patch...

PT-2025-45559

Name of the Vulnerable Software and Affected Versions Academy LMS – WordPress LMS Plugin for Complete eLearning Solution versions prior to 3.3.9 Description The software is susceptible to a PHP Object Injection due to deserialization of untrusted input within the import all courses function. This...

BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...

A Secured Intent-Based Networking (SIBN) with Data-Driven Time-Aware Intrusion Detection

While Intent-Based Networking IBN promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blin...

EUVD-2025-38108

Improper Neutralization of Special Elements used in an SQL Command 'SQL Injection' vulnerability in Codexpert, Inc CoSchool LMS coschool allows Blind SQL Injection.This issue affects CoSchool LMS: from n/a through = 1.4.3...

CVE-2025-60239 WordPress CoSchool LMS plugin <= 1.4.3 - SQL Injection vulnerability

Improper Neutralization of Special Elements used in an SQL Command 'SQL Injection' vulnerability in Codexpert, Inc CoSchool LMS coschool allows Blind SQL Injection.This issue affects CoSchool LMS: from n/a through = 1.4.3...

Black-Box Guardrail Reverse-Engineering Attack

Large language models LLMs increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this...

Adversarially Robust and Interpretable Magecart Malware Detection

Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning ML models...

Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems

With the increasing frequency and sophistication of Distributed Denial of Service DDoS attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

WordPress OOPSpam Anti-Spam plugin IP Header Forgery Vulnerability

WordPress OOPSpam Anti-Spam plugin is an anti-spam plugin designed for WordPress that protects forms and comments from spam through AI and machine learning techniques without the use of CAPTCHA validation. The WordPress OOPSpam Anti-Spam plugin suffers from an IP header forgery vulnerability that...

Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era

Quantum machine learning QML is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent...

Federated Cyber Defense: Privacy-Preserving Ransomware Detection across Distributed Systems

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence AI detectors requires diverse datasets, which are often distributed acros...

Detecting Vulnerabilities from Issue Reports for Internet-Of-Things

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things IoT where analysis is slower than non-IoT systems. While Machine Learning ML and Large Language Models LLMs detect vulnerability-indicating issues in non-IoT systems, their I...

Machine and Deep Learning for Indoor UWB Jammer Localization

Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...

Android Malware Detection: A Machine Learning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...

Meta-Learning Based Radio Frequency Fingerprinting for GNSS Spoofing Detection

The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time PVT information to perform their functions. As such, the Global Navigation Satellite Systems GNSS have been adopted as one of the most promising solutions to provide PVT...