Linux Kernel Security Vulnerabilities

The Linux kernel is the core of the open-source operating system Linux, developed by the Linux Foundation in the United States. There is a security vulnerability in the Linux kernel, which stems from the local overlap detection logic in netfilter’s nftsetrbtree. This logic skips the initial eleme...

Amazon Linux 2023 : python3.13-pip, python3.13-pip-wheel (ALAS2023-2026-1719)

It is, therefore, affected by a vulnerability as referenced in the ALAS2023-2026-1719 advisory. pip handles concatenated tar and ZIP files as ZIP files regardless of filename or whether a file is both a tar and ZIP file. This behavior could result in confusing installation behavior, such as...

From poisoned search results to GPU mining: A cryptojacking campaign abusing ScreenConnect and Microsoft .NET utilities

In this article 1. Attack chain overview 2. Mitigation and protection guidance 3. References 4. Learn more Microsoft Defender Experts identified an active cryptojacking campaign in which malicious download sites are surfaced not only through traditional search engine poisoning, but also through A...

artificial-detection (0.1.0), benchformer (0.0.1.dev1) +1 more potentially affected by CVE-2026-4372 via transformers (4.12.3)

transformers PYPI version 4.12.3 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - artificial-detection 0.1.0 - benchformer 0.0.1.dev1 - tai-chi-engine =0.0.4, =0.0.6 Source cves: CVE-2026-4372 Source advisory:...

XSSaudit

XSSAudit v2.0 — Advanced XSS Vulnerability Scanner For au...

Malicious code in helu (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 15a97c1f0e23d838c86d69a3ceae306071a9b4b8c17162a1f563aefe489ffbe4 During import, the hidden code downloads and executes the second-stage code. After performing anti-analysis checks, it downloads a malicious executable and...

Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems

Cyber-Physical Systems CPS integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility domain, various controllers are used in CPS to ensure the system detects and...

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection

Intrusion Detection Systems IDSs must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether heterogeneous quantum learners can provide useful and...

MAL-2026-4438 Malicious code in @service-suppliers/suppliers (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector a79ca8ef6257be2fbac9c361b969d9e63ce6a833e42dafa4b558e1f805276502 On npm install, scripts/postinstall.js performs two attacker-benefit actions against the installer. First, it scrapes installer-side credentials: it...

The Alert Firehose Finally Meets Its Match

Ask a cybersecurity pro about Network Detection and Response NDR and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positive...

"What Is the Problem Space?" Defining Host-Space Adversarial Perturbations against Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS are now increasingly leveraging Machine Learning ML techniques to detect malicious network activities. Numerous papers have scrutinized the security of ML-based NIDS ML-NIDS by testing them against various attacks involving adversarial perturbations. The...

Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

Software-Defined Networking SDN provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service DDoS attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple...

Malicious code in polymarket-data-fetcher (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 b6b5ac8b803d36ef490adff8a4d3110c4030063bbd2345e4b23d1871909638e9 The code attempts to monitor the clipboard and replace copied cryptocurrency addresses, as well as establish persistence. --- Category: MALICIOUS - The campaig...

MAL-2026-4286 Malicious code in polymarket-data-fetcher (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 b6b5ac8b803d36ef490adff8a4d3110c4030063bbd2345e4b23d1871909638e9 The code attempts to monitor the clipboard and replace copied cryptocurrency addresses, as well as establish persistence. --- Category: MALICIOUS - The campaig...

MAL-2026-4285 Malicious code in polydata-analytics (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 04c2f2ae400ee7411678735073e22d4c662de5653a4add84eaca159ed0ba004a Package self-describes as a Polymarket market-data analytics tool but ships a Windows clipboard monitor src/polymarketdatafetcher/clipper/winclip.py...

Malicious code in polydata-analytics (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 04c2f2ae400ee7411678735073e22d4c662de5653a4add84eaca159ed0ba004a Package self-describes as a Polymarket market-data analytics tool but ships a Windows clipboard monitor src/polymarketdatafetcher/clipper/winclip.py...

artificial-detection (0.1.0), benchformer (0.0.1.dev1) +1 more potentially affected by CVE-2026-4372 via transformers (4.12.3)

transformers PYPI version 4.12.3 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - artificial-detection 0.1.0 - benchformer 0.0.1.dev1 - tai-chi-engine =0.0.4, =0.0.6 Source cves: CVE-2026-4372 Source advisory:...

FortressWAF

FortressWAF — Web Application Firewall !Licensehttps://im...

SEED: Semi-Supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget

Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss HCL with active learning to improve robustness against drift by exploiting semantic structure...

ROS-20260524-73-0044

A vulnerability in the Jenkins Automation Server is related to incorrect symbolic link detection prior to file access during .tar and .tar.gz archive extraction. Exploitation of the vulnerability could allow an attacker acting remotely to write arbitrary files...