EntraGoat - a Deliberately Vulnerable Entra ID Environment

EntraGoat is a deliberately vulnerable Microsoft Entra ID infrastructure designed to simulate real-world identity security misconfigurations and attack vectors. EntraGoat introduces intentional vulnerabilities in your environment to provide a realistic learning platform for security professionals...

VeriPHY: Physical Layer Signal Authentication for Wireless Communication in 5G Environments

Physical layer authentication PLA uses inherent characteristics of the communication medium to provide secure and efficient authentication in wireless networks, bypassing the need for traditional cryptographic methods. With advancements in deep learning, PLA has become a widely adopted technique...

Generative AI for Critical Infrastructure in Smart Grids: a Unified Framework for Synthetic Data Generation and Anomaly Detection

In digital substations, security events pose significant challenges to the sustained operation of power systems. To mitigate these challenges, the implementation of robust defense strategies is critically important. A thorough process of anomaly identification and detection in information and...

CVE-2025-55006

Frappe Learning is a learning system that helps users structure their content. In versions 2.33.0 and below, the image upload functionality did not adequately sanitize uploaded SVG files. This allowed users to upload SVG files containing embedded JavaScript or other potentially malicious content...

CVE-2025-55006 Frappe Learning Holds Potential for Malicious SVG Upload in Image Upload Feature

Frappe Learning is a learning system that helps users structure their content. In versions 2.33.0 and below, the image upload functionality did not adequately sanitize uploaded SVG files. This allowed users to upload SVG files containing embedded JavaScript or other potentially malicious content...

CVE-2025-55006

CVE-2025-55006 affects Frappe LMS 2.34.x/2.35.0. The issue stems from an incomplete fix for CVE-2025-55006, enabling cross-site scripting via manipulated input. Remote exploitation is described as possible; an exploit has been made public per connected sources. A remediation is to upgrade to a ve...

CVE-2025-55006 Frappe Learning Holds Potential for Malicious SVG Upload in Image Upload Feature

Frappe Learning is a learning system that helps users structure their content. In versions 2.33.0 and below, the image upload functionality did not adequately sanitize uploaded SVG files. This allowed users to upload SVG files containing embedded JavaScript or other potentially malicious content...

CVE-2025-55006 Frappe Learning Holds Potential for Malicious SVG Upload in Image Upload Feature

Frappe Learning is a learning system that helps users structure their content. In versions 2.33.0 and below, the image upload functionality did not adequately sanitize uploaded SVG files. This allowed users to upload SVG files containing embedded JavaScript or other potentially malicious content...

Frappe Learning 输入验证错误漏洞

Frappe Learning is an easy-to-use open source learning management system from Frappe Open Source. An input validation error vulnerability exists in Frappe Learning version 2.33.0 and earlier, which stems from insufficient cleanup of uploaded SVG files and could lead to the execution of arbitrary...

PT-2025-32424

Name of the Vulnerable Software and Affected Versions Frappe Learning versions 2.33.0 and below Description Frappe Learning is a learning system designed to help users structure content. The image upload functionality did not properly sanitize uploaded SVG files, allowing users to upload files...

Non-Omniscient Backdoor Injection with a Single Poison Sample: Proving the One-Poison Hypothesis for Linear Regression and Linear Classification

Backdoor injection attacks are a threat to machine learning models that are trained on large data collected from untrusted sources; these attacks enable attackers to inject malicious behavior into the model that can be triggered by specially crafted inputs. Prior work has established bounds on th...

RL-MoE: an Image-Based Privacy Preserving Approach in Intelligent Transportation System

The proliferation of AI-powered cameras in Intelligent Transportation Systems ITS creates a severe conflict between the need for rich visual data and the fundamental right to privacy. Existing privacy-preserving mechanisms, such as blurring or encryption, are often insufficient, creating an...

Enhancing Software Vulnerability Detection through Adaptive Test Input Generation Using Genetic Algorithm

Software vulnerabilities continue to undermine the reliability and security of modern systems, particularly as software complexity outpaces the capabilities of traditional detection methods. This study introduces a genetic algorithm-based method for test input generation that innovatively...

Security Bulletin: Multiple vulnerabilities in IBM Business Automation Workflow Machine Learning Server are addressed with 24.0.0-IF006

Summary In addition to updates to operating system level packages, IBM Business Automation Workflow Machine Learning Server 24.0.0-IF006 addresses the following vulnerabilities. Vulnerability Details CVEID:CVE-2024-47081 DESCRIPTION: Requests is a HTTP library. Due to a URL parsing issue, Request...

Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies

Privacy-enhancing technologies PETs have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data protection. At the same time, the information and communication technology ICT sector faces growing pressure to reduce its...

SelectiveShield: Lightweight Hybrid Defense against Gradient Leakage in Federated Learning

Federated Learning FL enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user information. Existing defense mechanisms, such as differential privacy DP and homomorphic encryption HE, often introduce a...

SenseCrypt: Sensitivity-Guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios

Homomorphic Encryption HE prevails in securing Federated Learning FL, but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in...

Multilingual Source Tracing of Speech Deepfakes: a First Benchmark

Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages...

Per-Element Secure Aggregation against Data Reconstruction Attacks in Federated Learning

Federated learning FL enables collaborative model training without sharing raw data, but individual model updates may still leak sensitive information. Secure aggregation SecAgg mitigates this risk by allowing the server to access only the sum of client updates, thereby concealing individual...

When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs

As large language models become increasingly integrated into daily life, audio has emerged as a key interface for human-AI interaction. However, this convenience also introduces new vulnerabilities, making audio a potential attack surface for adversaries. Our research introduces WhisperInject, a...