250 matches found
Malicious code in alchemy-o0bup-rj9h1-drift-project (npm)
The package alchemy-o0bup-rj9h1-drift-project was found to contain malicious code...
Malicious code in test-mlw1-royst-opals-drift-chico (npm)
The package test-mlw1-royst-opals-drift-chico was found to contain malicious code...
Malicious code in drift-unity-aah846-project (npm)
The package drift-unity-aah846-project was found to contain malicious code...
Malicious code in drift-pc4dk-vjn11-flora-project (npm)
The package drift-pc4dk-vjn11-flora-project was found to contain malicious code...
MAL-2025-18788 Malicious code in drift-bse2r-92omp-zephyr-project (npm)
The package drift-bse2r-92omp-zephyr-project was found to contain malicious code...
MAL-2025-20374 Malicious code in fern-htydd-3fugw-drift-project (npm)
The package fern-htydd-3fugw-drift-project was found to contain malicious code...
MAL-2025-18796 Malicious code in drift-xzrj7-8xzcb-song-project (npm)
The package drift-xzrj7-8xzcb-song-project was found to contain malicious code...
MAL-2025-18791 Malicious code in drift-p0kij-n1y8l-plume-project (npm)
The package drift-p0kij-n1y8l-plume-project was found to contain malicious code...
MAL-2025-18787 Malicious code in drift-4sof4-88og5-harp-project (npm)
The package drift-4sof4-88og5-harp-project was found to contain malicious code...
MAL-2025-37152 Malicious code in tranquil-z8e7k-5t7m0-drift-project (npm)
The package tranquil-z8e7k-5t7m0-drift-project was found to contain malicious code...
MAL-2025-21960 Malicious code in grove-hiim3-2wce6-drift-project (npm)
The package grove-hiim3-2wce6-drift-project was found to contain malicious code...
MAL-2025-18797 Malicious code in drift-y8wyg-l9zy5-xanadu-project (npm)
The package drift-y8wyg-l9zy5-xanadu-project was found to contain malicious code...
MAL-2025-18795 Malicious code in drift-vyo0u-w3ibw-nymph-project (npm)
The package drift-vyo0u-w3ibw-nymph-project was found to contain malicious code...
MAL-2025-40748 Malicious code in zany-drift-qov382-project (npm)
The package zany-drift-qov382-project was found to contain malicious code...
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,...
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,...
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...
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and...
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...
REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
Graph Neural Network GNN-based network intrusion detection systems NIDS are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This...