EUVD-2026-1130

Malicious code in n8n-performance-metrics npm...

Malicious Package

Overview n8n-nodes-performance-metrics is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this...

EUVD-2026-1131

Malicious code in n8n-nodes-performance-metrics npm...

Malicious code in n8n-nodes-performance-metrics (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector decf8fbf4cfd483a5fc72a3c3037f88de636f0732e3d591a09309e54faceff05 The package n8n-nodes-performance-metrics was found to contain malicious code. Source: ghsa-malware...

Malicious Package

Overview n8n-performance-metrics is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this packa...

Malicious code in n8n-performance-metrics (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector ff33e7eec92ad0ad734fb7babcf427a9a5df69ba94b95c904c1ecfce32d5ef97 The package n8n-performance-metrics was found to contain malicious code. Source: ghsa-malware...

MAL-2026-69 Malicious code in n8n-performance-metrics (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector ff33e7eec92ad0ad734fb7babcf427a9a5df69ba94b95c904c1ecfce32d5ef97 The package n8n-performance-metrics was found to contain malicious code. Source: ghsa-malware...

Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis

The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based...

GHSA-7CX5-254X-CGRQ Parse Server allows public `explain` queries which may expose sensitive database performance information and schema details

Impact The MongoDB explain method provides detailed information about query execution plans, including index usage, collection scanning behavior, and performance metrics. Parse Server permits any client to execute explain queries without requiring the master key. This exposes: - Database schema...

Parse Server allows public `explain` queries which may expose sensitive database performance information and schema details

Impact The MongoDB explain method provides detailed information about query execution plans, including index usage, collection scanning behavior, and performance metrics. Parse Server permits any client to execute explain queries without requiring the master key. This exposes: - Database schema...

BIT-PARSE-2025-64502 Parse Server allows public `explain` queries which may expose sensitive database performance information and schema details

Parse Server is an open source backend that can be deployed to any infrastructure that can run Node.js. The MongoDB explain method provides detailed information about query execution plans, including index usage, collection scanning behavior, and performance metrics. Prior to version 8.5.0, Parse...

kernel: perf/x86/intel: Fix crash in icl_update_topdown_event()

In the Linux kernel, the following vulnerability has been resolved: perf/x86/intel: Fix crash in iclupdatetopdownevent The perffuzzer found a hard-lockup crash on a RaptorLake machine: Oops: general protection fault, maybe for address 0xffff89aeceab400: 0000 CPU: 23 UID: 0 PID: 0 Comm: swapper/23...

CVE-2025-64502

Parse Server is an open source backend that can be deployed to any infrastructure that can run Node.js. The MongoDB explain method provides detailed information about query execution plans, including index usage, collection scanning behavior, and performance metrics. Prior to version 8.5.0-alpha....

Sensing Security in Near-Field ISAC: Exploiting Scatterers for Eavesdropper Deception

In this paper, we explore sensing security in near-field NF integrated sensing and communication ISAC scenarios by exploiting known scatterers in the sensing scene. We propose a location deception LD scheme where scatterers are deliberately illuminated with probing power that is higher than that...

Malicious Package

Overview kpi-media-metrics is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package...

EUVD-2025-20924

Malicious code in bioql PyPI...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

UBUNTU-CVE-2025-38322

In the Linux kernel, the following vulnerability has been resolved: perf/x86/intel: Fix crash in iclupdatetopdownevent The perffuzzer found a hard-lockup crash on a RaptorLake machine: Oops: general protection fault, maybe for address 0xffff89aeceab400: 0000 CPU: 23 UID: 0 PID: 0 Comm: swapper/23...

Evaluating the Efficacy of LLM Safety Solutions : the Palit Benchmark Dataset

Large Language Models LLMs are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives...

OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques

This technical report presents a comprehensive analysis of malware classification using OpCode sequences. Two distinct approaches are evaluated: traditional machine learning using n-gram analysis with Support Vector Machine SVM, K-Nearest Neighbors KNN, and Decision Tree classifiers; and a deep...