CVE-2026-46280

A flaw was found in the Linux kernel's Heterogeneous Memory Management HMM test module. When a device mirror dmirror structure is freed, its associated device private pages are not properly migrated back to system memory. This can lead to a use-after-free condition where a dangling pointer to the...

Towards Cybersecurity SuperIntelligence (CSI): What'S the Best Harness for Cybersecurity?

What is the best harness for cybersecurity AI? Cybersecurity systems are converging on a single execution scaffold per agent, an iterative shell loop driven by a Large Language Model LLM. However, scaffolds are not interchangeable, rarely interoperable, and no single scaffold dominates across all...

Explainable Machine Learning for Phishing Detection on Heterogeneous Datasets with MCP-Enabled Deployment

With the growth in digital transformation and Internet usage, the Social Engineering techniques such as Phishing have become a major concern for the users and the organizations. Phishing attacks involve deceptive techniques to trick users into revealing confidential information that causes...

AI Native Asset Intelligence

Modern security environments generate fragmented signals across cloud resources, identities, configurations, and third-party security tools. Although AI-native security assistants improve access to this data, they remain largely reactive: users must ask the right questions and interpret...

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 ...

AnyPoC: Universal Proof-Of-Concept Test Generation for Scalable LLM-Based Bug Detection

While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection. We frame this challenge as a test generation task: given a candidate report, synthesizing an...

Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs

Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials SBOM-based pipelines for security analysis typically treat scanner findings as independent per-CVE Common...

Challenges and Design Considerations for Finding CUDA Bugs through GPU-Native Fuzzing

Modern computing is shifting from homogeneous CPU-centric systems to heterogeneous systems with closely integrated CPUs and GPUs. While the CPU software stack has benefited from decades of memory safety hardening, the GPU software stack remains dangerously immature. This discrepancy presents a...

Next-Generation Cyberattack Detection with Large Language Models: Anomaly Analysis across Heterogeneous Logs

This project explores large language models LLMs for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems suffer from high false positive rates, semantic blindness, and data scarcity, as logs are inherently sensitive, making clean datasets rare. We address...

Operational Runtime Behavior Mining for Open-Source Supply Chain Security

Open-source software OSS is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate...

AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection

Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneou...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...

Extending the OWASP Multi-Agentic System Threat Modeling Guide: Insights from Multi-Agent Security Research

We propose an extension to the OWASP Multi-Agentic System MAS Threat Modeling Guide, translating recent anticipatory research in multi-agent security MASEC into practical guidance for addressing challenges unique to large language model LLM-driven multi-agent architectures. Although OWASP's...

SVC 2025: the First Multimodal Deception Detection Challenge

Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown promise in surpassing human-level performance, their effectiveness often depends on the availability of high-quality a...

MalFlows: Context-Aware Fusion of Heterogeneous Flow Semantics for Android Malware Detection

Static analysis, a fundamental technique in Android app examination, enables the extraction of control flows, data flows, and inter-component communications ICCs, all of which are essential for malware detection. However, existing methods struggle to leverage the semantic complementarity across...

Coward: toward Practical Proactive Federated Backdoor Defense Via Collision-Based Watermark

Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning FL, where malicious clients upload poisoned updates that compromise the global model and undermine the reliability of FL deployments. Existing backdoor detection techniques fall into two...

LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks

This work tackles the physical layer security PLS problem of maximizing the secrecy rate in heterogeneous UAV networks HetUAVNs under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario...

A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing Constraints

Federated Learning has gained increasing attention for its ability to enable multiple nodes to collaboratively train machine learning models without sharing their raw data. At the same time, Generative AI -- particularly Generative Adversarial Networks GANs -- have achieved remarkable success...

QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality

This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devices. Our approach trains specialized local models for different image quality levels and aggregates their features...