8973 matches found
DP2Guard: a Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
Privacy-Preserving Federated Learning PPFL has emerged as a secure distributed Machine Learning ML paradigm that aggregates locally trained gradients without exposing raw data. To defend against model poisoning threats, several robustness-enhanced PPFL schemes have been proposed by integrating...
CVE-2025-49746
Improper authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
CVE-2025-49747
Missing authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
CVE-2025-47995
Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
Federated Learning FL enables collaborative model training without sharing raw data, making it a promising approach for privacy-sensitive domains. Despite its potential, FL faces significant challenges, particularly in terms of communication overhead and data privacy. Privacy-preserving Technique...
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-2025-49747
Missing authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
CVE-2025-49746
Improper authorization in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
CVE-2025-47995
Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network...
CVE-2025-47995 Azure Machine Learning Elevation of Privilege Vulnerability
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CVE-2025-47995 Azure Machine Learning Elevation of Privilege Vulnerability
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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 Azure Machine Learning Elevation of Privilege Vulnerability
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CVE-2025-49746 Azure Machine Learning Elevation of Privilege Vulnerability
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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-2025-49747 Azure Machine Learning Elevation of Privilege Vulnerability
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