7276 matches found
On One-Shot Signatures, Quantum Vs Classical Binding, and Obfuscating Permutations
One-shot signatures OSS were defined by Amos, Georgiou, Kiayias, and Zhandry STOC'20. These allow for signing exactly one message, after which the signing key self-destructs, preventing a second message from ever being signed. While such an object is impossible classically, Amos et al observe tha...
Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security
Multi-Domain Operations MDOs emphasize cross-domain defense against complex and synergistic threats, with civilian infrastructures like smart cities and Connected Autonomous Vehicles CAVs emerging as primary targets. As dual-use assets, CAVs are vulnerable to Multi-Surface Threats MSTs,...
Learning-Based Privacy-Preserving Graph Publishing against Sensitive Link Inference Attacks
Publishing graph data is widely desired to enable a variety of structural analyses and downstream tasks. However, it also potentially poses severe privacy leakage, as attackers may leverage the released graph data to launch attacks and precisely infer private information such as the existence of...
hermes-agent
Hermes Agent ☤ The self-improving AI agent b...
LENS-DF: Deepfake Detection and Temporal Localization for Long-Form Noisy Speech
This study introduces LENS-DF, a novel and comprehensive recipe for training and evaluating audio deepfake detection and temporal localization under complicated and realistic audio conditions. The generation part of the recipe outputs audios from the input dataset with several critical...
Scaling Decentralized Learning with FLock
Fine-tuning the large language models LLMs are prevented by the deficiency of centralized control and the massive computing and communication overhead on the decentralized schemes. While the typical standard federated learning FL supports data privacy, the central server requirement creates a...
In-Context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems
Recent advances in biometric systems have significantly improved the detection and prevention of fraudulent activities. However, as detection methods improve, attack techniques become increasingly sophisticated. Attacks on face recognition systems can be broadly divided into physical and digital...
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...