Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

Zero Day Attacks: Novel Behaviour or Novel Vulnerability?

Zero-day attacks pose severe cybersecurity risks due to their high success rates and stealth. Because signature-based approaches struggle to detect such attacks, building Intrusion Detection Systems IDSs for detecting zero-day attacks is essential. We contend that for an IDS to be effective it mu...

Phishing Detection in Ethereum Via Temporal Graph Contrastive Learning

Blockchain and decentralized finance have revolutionized the financial ecosystem while simultaneously exposing it to cryptocurrency phishing attacks. Existing phishing detection methods primarily rely on graph learning, but they face significant limitations. Static graph learning approaches fail ...

STARE: Step-Wise Temporal Alignment and Red-Teaming Engine for Multi-Modal Toxicity Attack

Red-teaming Vision-Language Models is essential for identifying vulnerabilities where adversarial image-text inputs trigger toxic outputs. Existing approaches treat image generation as a black box, returning only terminal toxicity scores and leaving open the question of when and how toxic semanti...

RoboKA: KAN Informed Multimodal Learning for RoboCall Surveillance System

Wide exploration on robocall surveillance research is hindered due to limited access to public datasets, due to privacy concerns. In this work, we first curate Robo-SAr, a synthetic robocall dataset designed for robocall surveillance research. Robo-SAr comprises of 200 unwanted and 1200 legitimat...

Trident: Improving Malware Detection with LLMs and Behavioral Features

Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to incorporating dynamic analysis features has been the semi-structured nature of sandbox behavior reports. We show that,...

TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic Via Asymmetric Contrastive Learning

Decompositional jailbreaks pose a critical threat to large language models LLMs by allowing adversaries to fragment a malicious objective into a sequence of individually benign queries that collectively reconstruct prohibited content. In real-world deployments, LLMs face a continuous, untraceable...

A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

AI-powered edge computing security is moving Intelligent Transportation Systems ITS from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT,...

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and...

CVE-2026-7213 ef10007 MLOps_MCP save_file Tool fastmcp_server.py path traversal

A vulnerability was detected in ef10007 MLOpsMCP 1.0.0. This impacts an unknown function of the file fastmcpserver.py of the component savefile Tool. The manipulation of the argument filename/destination results in path traversal. The attack may be performed from remote. The exploit is now public...

NVIDIA FLARE SDK Input Validation Error Vulnerability

NVIDIA FLARE SDK is a federal learning application development toolkit provided by NVIDIA Corporation in the United States. The NVIDIA Flare SDK has a vulnerability related to input validation errors. This vulnerability stems from path traversal, which leads to improper input validation,...

EDySec: A Deep Learning-Based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem

The security of open-source software repositories is increasingly threatened by next-gen software supply chain attacks. These attacks include multiphase malware execution, remote access activation, and dynamic payload generation. Traditional Machine Learning ML detectors struggle to detect these...

Threat-Oriented Digital Twinning for Security Evaluation of Autonomous Platforms

Open, unclassified research on secure autonomy is constrained by limited access to operational platforms, contested communications infrastructure, and representative adversarial test conditions. This paper presents a threat-oriented digital twinning methodology for cybersecurity evaluation of...

secops-ai-threat-analyzer

🛡️ SecOpsAI: Threat Analysis & Adaptive Security Engine An e...

Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection

The global financial ecosystem confronts a critical asymmetry: while fraud syndicates operate as borderless, distributed networks, banking institutions remain constrained by regulatory data silos, limiting visibility into cross-institutional threat patterns under strict privacy laws such as GDPR...

CVE-2026-3007

Successful exploitation of the stored cross-site scripting XSS vulnerability could allow an attacker to execute arbitrary JavaScript on any user account that has access to Koollab LMS’ courselet feature...

CVE-2026-3007

CVE-2026-3007 is a stored XSS in Koollab LMS, affecting the courselet feature. Exploitation could run arbitrary JS in accounts with access to the courselet, with a CVSS 3.1 base score of 5.4 (AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N). The vulnerability requires user interaction and has low confidentia...

PT-2026-34832

Critical vulnerability in Anthropic Mythos and reported NSA adoption CVE-2026-21841 https://t.co/ZwHNBc0RF8 machinelearning ai...

PT-2026-34630

Name of the Vulnerable Software and Affected Versions Koollab LMS affected versions not specified Description A stored cross-site scripting XSS issue exists within the courselet feature. This flaw allows an attacker to execute arbitrary JavaScript on any user account that has access to this...

VeRL Permission Authorization and Access Control Vulnerabilities

VeRL is an open-source reinforcement learning framework developed by ByteDance, aimed at optimizing large model training and inference processes. Versions of VeRL prior to 0.7.0 contained vulnerabilities related to permission licensing and access control. These vulnerabilities stemmed from a...