CVE-2025-47995

Azure Machine Learning is identified in CVE-2025-47995 as having weak authentication that enables a network-based privilege escalation by an authorized attacker. The entry derives from Microsoft/Red Hat and multiple security sources, describing the vulnerability as affecting Microsoft Azure Machi...

CVE-2025-49746 Azure Machine Learning Elevation of Privilege Vulnerability

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CVE-2025-49746

CVE-2025-49746 affects Microsoft Azure Machine Learning. The vulnerability is caused by improper authorization, enabling an attacker with network access to escalate privileges within the affected service. Documented impact is privilege escalation with high confidentiality, integrity, and availabi...

CVE-2025-49747

CVE-2025-49747 is a privilege-escalation vulnerability affecting Microsoft Azure Machine Learning reported as missing authorization. Affected component: Azure Machine Learning (machine learning services platform). Root cause per description: insufficient access control that allows an authorized a...

CVE-2025-49746 Azure Machine Learning Elevation of Privilege Vulnerability

...

CVE-2025-49747 Azure Machine Learning Elevation of Privilege Vulnerability

...

CVE-2025-49747 Azure Machine Learning Elevation of Privilege Vulnerability

...

Azure Machine Learning Elevation of Privilege Vulnerability

Missing authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...

Azure Machine Learning Elevation of Privilege Vulnerability

Improper authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...

PT-2025-30067 · Microsoft · Azure Machine Learning

Name of the Vulnerable Software and Affected Versions: Azure Machine Learning affected versions not specified Description: Improper authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network. Recommendations: At the moment, there is no information...

PT-2025-30066 · Microsoft · Azure Machine Learning

Name of the Vulnerable Software and Affected Versions: Azure Machine Learning affected versions not specified Description: Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network. Recommendations: At the moment, there is no information abou...

Microsoft Azure Machine Learning 授权问题漏洞

Microsoft Azure Machine Learning is a machine learning services platform from Microsoft USA. Microsoft Azure Machine Learning has a security vulnerability that can be exploited by an attacker to potentially cause elevation of privilege...

PT-2025-30068 · Microsoft · Azure Machine Learning

Name of the Vulnerable Software and Affected Versions: Azure Machine Learning affected versions not specified Description: Missing authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network. Recommendations: At the moment, there is no information...

Microsoft Azure Machine Learning 安全漏洞

Microsoft Azure Machine Learning is a machine learning services platform from Microsoft USA. Microsoft Azure Machine Learning has a security vulnerability that can be exploited by an attacker to potentially cause elevation of privilege...

Microsoft Azure Machine Learning 安全漏洞

Microsoft Azure Machine Learning is a machine learning services platform from Microsoft USA. Microsoft Azure Machine Learning has a security vulnerability that can be exploited by an attacker to potentially cause elevation of privilege...

CVE-2025-46102

Cross Site Scripting vulnerability in Beakon Software Beakon Learning Management System Sharable Content Object Reference Model SCORM version V.5.4.3 allows a remote attacker to obtain sensitive information via the URL parameter...

CVE-2025-46102

Cross Site Scripting vulnerability in Beakon Software Beakon Learning Management System Sharable Content Object Reference Model SCORM version V.5.4.3 allows a remote attacker to obtain sensitive information via the URL parameter...

CVE-2025-46102

Cross Site Scripting vulnerability in Beakon Software Beakon Learning Management System Sharable Content Object Reference Model SCORM version V.5.4.3 allows a remote attacker to obtain sensitive information via the URL parameter...

Learning-Based Cost-Aware Defense of Parallel Server Systems against Malicious Attacks

We consider the cyber-physical security of parallel server systems, which is relevant for a variety of engineering applications such as networking, manufacturing, and transportation. These systems rely on feedback control and may thus be vulnerable to malicious attacks such as denial-of-service,...

A Crowdsensing Intrusion Detection Dataset for Decentralized Federated Learning Models

This paper introduces a dataset and experimental study for decentralized federated learning DFL applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls,...