EUVD-2025-3571

Malicious code in bioql PyPI...

CodeQL zero to hero part 5: Debugging queries

When you're first getting started with CodeQL, you may find yourself in a situation where a query doesn't return the results you expect. Debugging these queries can be tricky, because CodeQL is a Prolog-like language with an evaluation model that's quite different from mainstream languages like...

Coherence-Driven Inference for Cybersecurity

Large language models LLMs can compile weighted graphs on natural language data to enable automatic coherence-driven inference CDI relevant to red and blue team operations in cybersecurity. This represents an early application of automatic CDI that holds near- to medium-term promise for...

CVE-2023-53261 coresight: Fix memory leak in acpi_buffer->pointer

In the Linux kernel, the following vulnerability has been resolved: coresight: Fix memory leak in acpibuffer-pointer There are memory leaks reported by kmemleak: ... unreferenced object 0xffff00213c141000 size 1024: comm "systemd-udevd", pid 2123, jiffies 4294909467 age 6062.160s hex dump first 3...

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...

URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection

Malicious URL detection remains a major challenge in cybersecurity, primarily due to two factors: 1 the exponential growth of the Internet has led to an immense diversity of URLs, making generalized detection increasingly difficult; and 2 attackers are increasingly employing sophisticated...

Mind the Gap: Evaluating Model- and Agentic-Level Vulnerabilities in LLMs with Action Graphs

As large language models transition to agentic systems, current safety evaluation frameworks face critical gaps in assessing deployment-specific risks. We introduce AgentSeer, an observability-based evaluation framework that decomposes agentic executions into granular action and component graphs,...

Linux Distros Unpatched Vulnerability : CVE-2023-49086

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - Cacti is a robust performance and fault management framework and a frontend to RRDTool - a Time Series Database TSDB. A vulnerability in versions prior to 1.2.2...

graph-rag-poc

Graph RAG Pipeline - Proof of Concept A locally-executable Gr...

Linux Distros Unpatched Vulnerability : CVE-2023-39359

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - Cacti is an open source operational monitoring and fault management framework. An authenticated SQL injection vulnerability was discovered which allows...

Linux Distros Unpatched Vulnerability : CVE-2022-24919

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - An authenticated user can create a link with reflected Javascript code inside it for graphs' page and send it to other users. The payload can be executed only...

Linux Distros Unpatched Vulnerability : CVE-2019-17357

The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - Cacti through 1.2.7 is affected by a graphs.php?templateid= SQL injection vulnerability affecting how template identifiers are handled when a string and id...

Attack Graph Generation on HPC Clusters

Attack graphs AGs are graphical tools to analyze the security of computer networks. By connecting the exploitation of individual vulnerabilities, AGs expose possible multi-step attacks against target networks, allowing system administrators to take preventive measures to enhance their network's...

CVE-2025-8867 Graphina - Elementor Charts and Graphs <= 3.1.3 - Authenticated (Contributor+) Stored Cross-Site Scripting

The Graphina - Elementor Charts and Graphs plugin for WordPress is vulnerable to Stored Cross-Site Scripting via multiple chart widget parameters in version 3.1.3 and below. This is due to insufficient input sanitization and output escaping on user supplied attributes such as chart categories,...

CVE-2025-8867 Graphina - Elementor Charts and Graphs <= 3.1.3 - Authenticated (Contributor+) Stored Cross-Site Scripting

The Graphina - Elementor Charts and Graphs plugin for WordPress is vulnerable to Stored Cross-Site Scripting via multiple chart widget parameters in version 3.1.3 and below. This is due to insufficient input sanitization and output escaping on user supplied attributes such as chart categories,...

WordPress plugin Graphina - Elementor Charts and Graphs 跨站脚本漏洞

WordPress and WordPress plugin are both products of the WordPress Foundation.WordPress is a blogging platform developed using the PHP language. The platform supports personal blog sites on servers running PHP and MySQL.WordPress plugin is an application plug-in. A cross-site scripting vulnerabili...

Malicious code in hm-graphs (npm)

The package hm-graphs was found to contain malicious code...

MAL-2025-22479 Malicious code in hm-graphs (npm)

The package hm-graphs was found to contain malicious code...

Causal Graph Profiling Via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems

With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns. Traditional methods -- statistical,...

Explainable Ensemble Learning for Graph-Based Malware Detection

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks GNNs have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such a...