CVE-2025-46102: Improper Neutralization of Input During Web Page Generation

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...

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy

To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balan...

Thought Purity: Defense Paradigm for Chain-Of-Thought Attack

While reinforcement learning-trained Large Reasoning Models LRMs, e.g., Deepseek-R1 demonstrate advanced reasoning capabilities in the evolving Large Language Models LLMs domain, their susceptibility to security threats remains a critical vulnerability. This weakness is particularly evident in...

Safeguarding Federated Learning-Based Road Condition Classification

Federated Learning FL has emerged as a promising solution for privacy-preserving autonomous driving, specifically camera-based Road Condition Classification RCC systems, harnessing distributed sensing, computing, and communication resources on board vehicles without sharing sensitive image data...

Reporte De Vulnerabilidades En IIoT. Proyecto DEFENDER

The main objective of this technical report is to conduct a comprehensive study on devices operating within Industrial Internet of Things IIoT environments, describing the scenarios that define this category and analysing the vulnerabilities that compromise their security. To this end, the report...

HASSLE: a Self-Supervised Learning Enhanced Hijacking Attack on Vertical Federated Learning

Vertical Federated Learning VFL enables an orchestrating active party to perform a machine learning task by cooperating with passive parties that provide additional task-related features for the same training data entities. While prior research has leveraged the privacy vulnerability of VFL to...

BandFuzz: an ML-Powered Collaborative Fuzzing Framework

Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative...

DNS Tunneling: Threat Landscape and Improved Detection Solutions

Detecting Domain Name System DNS tunneling is a significant challenge in security due to its capacity to hide harmful actions within DNS traffic that appears to be normal and legitimate. Traditional detection methods are based on rule-based approaches or signature matching methods that are often...

Differentially Private Federated Low Rank Adaptation beyond Fixed-Matrix

Large language models LLMs typically require fine-tuning for domain-specific tasks, and LoRA offers a computationally efficient approach by training low-rank adapters. LoRA is also communication-efficient for federated LLMs when multiple users collaboratively fine-tune a global LLM model without...

Contrastive-KAN: a Semi-Supervised Intrusion Detection Framework for Cybersecurity with Scarce Labeled Data

In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal...

PLA: Prompt Learning Attack against Text-To-Image Generative Models

Text-to-Image T2I models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work NSFW content. To investigate the vulnerability of T2I models, this paper delves into adversarial...

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions

Hacker forums provide critical early warning signals for emerging cybersecurity threats, but extracting actionable intelligence from their unstructured and noisy content remains a significant challenge. This paper presents an unsupervised framework that automatically detects, clusters, and...

LLMalMorph: on the Feasibility of Generating Variant Malware Using Large-Language-Models

Large Language Models LLMs have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in modifying malware source code to generate variants. We introduce LLMalMorph, a semi-automated framework that leverages...

CLIProv: a Contrastive Log-To-Intelligence Multimodal Approach for Threat Detection and Provenance Analysis

With the increasing complexity of cyberattacks, the proactive and forward-looking nature of threat intelligence has become more crucial for threat detection and provenance analysis. However, translating high-level attack patterns described in Tactics, Techniques, and Procedures TTP intelligence...

Entangled Threats: a Unified Kill Chain Model for Quantum Machine Learning Security

Quantum Machine Learning QML systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers of quantum computing. Despite a growing body of research on individual attack vectors - ranging from adversarial poisoni...

Phishing Detection in the Gen-AI Era: Quantized LLMs Vs Classical Models

Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning ML, Deep Learning DL, and quantized...

Towards Privacy-Preserving and Personalized Smart Homes Via Tailored Small Language Models

Large Language Models LLMs have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home...

Agent Safety Alignment Via Reinforcement Learning

The emergence of autonomous Large Language Model LLM agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents, empowered to execute external functions, are vulnerable to both user-initiated threats e.g., adversarial prompts and...