Quantum Support Vector Regression for Robust Anomaly Detection

Anomaly Detection AD is critical in data analysis, particularly within the domain of IT security. In recent years, Machine Learning ML algorithms have emerged as a powerful tool for AD in large-scale data. In this study, we explore the potential of quantum ML approaches, specifically quantum kern...

Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization'S Impact on ML Fairness

Machine learning ML algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers. This raises critical privacy concerns. Anonymization techniques have emerged as a practical solution to address these...

Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication

Vehicular Ad Hoc Networks VANETs play a key role in Intelligent Transportation Systems ITS, particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service DDoS attacks, which interfere with safety-critical communication channels, can severely impai...

The vulnerabilities of Machine Learning functions and the Reporting service of the Kibana data visualization platform allow a hacker to execute arbitrary code.

The vulnerability of Machine Learning and Reporting services in the Kibana data visualization platform lies in the lack of a mechanism for controlling changes to object prototypes’ attributes. Exploiting this vulnerability could allow an attacker to execute arbitrary code by sending specially...

CVE-2025-25014

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

BIT-KIBANA-2025-25014 Kibana arbitrary code execution via prototype pollution

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

BIT-ELK-2025-25014 Kibana arbitrary code execution via prototype pollution

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

CVE-2025-25014

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

CVE-2025-25014

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

CVE-2025-25014

KIBANA: CVE-2025-25014 is a prototype-pollution vulnerability in Kibana that enables arbitrary code execution via crafted HTTP requests to the Machine Learning or Reporting endpoints. Public details indicate exploitation is possible remotely over the network with low complexity and requires high ...

CVE-2025-25014 Kibana arbitrary code execution via prototype pollution

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

CVE-2025-25014 Kibana arbitrary code execution via prototype pollution

A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

PT-2025-19876 · Microsoft · Internet Explorer

Name of the Vulnerable Software and Affected Versions: The product name cannot be determined. Description: The issue is related to a transient Denial of Service DOS that occurs while parsing per Station STA profile in Machine Learning ML Internet Explorer IE. No additional details are provided...

PT-2025-19890 · Kibana · Kibana

Name of the Vulnerable Software and Affected Versions: Kibana versions 8.3.0 through 8.17.5 Kibana version 8.18.0 Kibana version 9.0.0 Description: A Prototype pollution vulnerability in Kibana leads to arbitrary code execution via crafted HTTP requests to machine learning and reporting endpoints...

Detecting Quishing Attacks with Machine Learning Techniques through QR Code Analysis

The rise of QR code based phishing "Quishing" poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing detection methods predominantly focus on URL analysis, which requires the extraction of the QR code payload, and may...

Security Bulletin: FreeType Remote Code Execution Vulnerability affects IBM Watson Machine Learning Accelerator on Cloud Pak for Data

Summary FreeType Remote Code Execution Vulnerability affects IBM Watson Machine Learning Accelerator on Cloud Pak for Data. The vulnerability has been addressed. Vulnerability Details CVEID:CVE-2025-27363 DESCRIPTION: An out of bounds write exists in FreeType versions 2.13.0 and below newer...

Securing the Future of IVR: AI-Driven Innovation with Agile Security, Data Regulation, and Ethical AI Integration

The rapid digitalization of communication systems has elevated Interactive Voice Response IVR technologies to become critical interfaces for customer engagement. With Artificial Intelligence AI now driving these platforms, ensuring secure, compliant, and ethically designed development practices i...

编号撤回

H2O is an in-memory platform for distributed, scalable machine learning open-sourced by H2O.ai. This CVE number has been withdrawn...

Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

CVE-2025-30390 Azure ML Compute Elevation of Privilege Vulnerability

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