Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

Connected and Autonomous Vehicles CAVs enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network CAN bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robus...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...

On the Interplay of Explainability, Privacy and Predictive Performance with Explanation-Assisted Model Extraction

Machine Learning as a Service MLaaS has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to leverage advanced analytics without substantial investments in specialized infrastructure or expertise. However, MLaaS...

Ivanti Neurons for ITSM 安全漏洞

Ivanti Neurons for ITSM is an automation platform for IT service management, based on artificial intelligence and machine learning technologies, designed to optimize the IT service delivery process and enhance user experience. An authentication bypass vulnerability exists in Ivanti Neurons for...

GPML: Graph Processing for Machine Learning

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML Graph Processing for Machine Learning library addresses this need by transforming raw network traffic traces into graph representations, enabling...

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...