MAL-2025-7372 Malicious code in @crabas0npm/libero-earum-non (npm)

The package @crabas0npm/libero-earum-non was found to contain malicious code...

MAL-2025-37607 Malicious code in uim-web-sdk (npm)

The package uim-web-sdk was found to contain malicious code...

Malicious code in fabalous-login (npm)

The package fabalous-login was found to contain malicious code...

Malicious code in idig-onapp (npm)

The package idig-onapp was found to contain malicious code...

Malicious code in dyplot (npm)

The package dyplot was found to contain malicious code...

MAL-2025-37706 Malicious code in umbriel-sequelize-tool-browserify (npm)

The package umbriel-sequelize-tool-browserify was found to contain malicious code...

MAL-2025-22187 Malicious code in hapi-services (npm)

The package hapi-services was found to contain malicious code...

MAL-2025-19382 Malicious code in elec6ron (npm)

The package elec6ron was found to contain malicious code...

USN-7697-1: AIDE vulnerabilities

Rajesh Pangare discovered that AIDE incorrectly handled filenames. A local attacker could possibly use this issue to bypass the detection of malicious files. CVE-2025-54389 Rajesh Pangare discovered that AIDE incorrectly handled extended file attributes. A local attacker could possibly use this...

Demystifying the Role of Rule-Based Detection in AI Systems for Windows Malware Detection

Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples,...

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...

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...

MAL-2025-6616 Malicious code in wallets-utils (PyPI)

--- -= Per source details. Do not edit below this line.=-...

MAL-2025-6381 Malicious code in web3-rtc (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware e9dca49a40515fc43ab5bb3d987aa551a6464961dd1aee2b3e4cadae66831a01 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning ML algorithms have been applied to Android...

MAL-2025-6262 Malicious code in blank-pkg (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ossf-package-analysis e2f49c661af4e850b4d2aa252c5bdfc2d7409efbbae9396ac2901d0e9fc0f910 The OpenSSF Package Analysis project identified 'blank-pkg' @ 10.0.1...

Regression-Aware Continual Learning for Android Malware Detection

Malware evolves rapidly, forcing machine learning ML-based detectors to adapt continuously. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning CL has emerged as a scalable...

MAL-2025-6080 Malicious code in chime-utils (npm)

The package communicates with a domain associated with malicious activity. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 4c47d81929483a169d2ee7d4f0f5c08d14518a52a1efa368fc87e2101bd75de5 Any computer that has this package installed or running should be considered...

MAL-2025-6199 Malicious code in sha256-validator-pack (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 453f997676ddfb37b97659a39bdc5de8d8c2fc1b070f5feeda72545030850113 Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

A Crowdsensing Intrusion Detection Dataset for Decentralized Federated Learning Models

This paper introduces a dataset and experimental study for decentralized federated learning DFL applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls,...