Malicious code in whop-sdk (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 943bd287cb6375798fdee15ba33f85737201ea9934952ee5d1f2a2336e8cd65c The package whop-sdk was found to contain malicious code. Source: ghsa-malware 4c3e9ca78194532c222b978afd00f7bb4be1ca1ba6cd442e1892d17ee6e67ccc Any...

MAL-2026-1185 Malicious code in @bookings.microsoft.com/s (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector aa10e8f4ab4580d4d9aedaee9a9e0c036b3248364f0680727df6871025d7e2f9 The package @bookings.microsoft.com/s was found to contain malicious code. Source: ghsa-malware...

Malicious code in @zakhaevv/envai (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector ccd4de2673a9f50b205b51474085ef3eb3e78f06618873183038582fb48bacfe The package @zakhaevv/envai was found to contain malicious code. Source: ghsa-malware 12157c0eeb0adb6f316ee9f171691ae08e204ec84f3ad7dabf9213e2af244b7...

AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply uniform detection strategies, which may not account for...

Red-Teaming Claude Opus and ChatGPT-Based Security Advisors for Trusted Execution Environments

Trusted Execution Environments TEEs e.g., Intel SGX and ArmTrustZone aim to protect sensitive computation from a compromised operating system, yet real deployments remain vulnerable to microarchitectural leakage, side-channel attacks, and fault injection. In parallel, security teams increasingly...

Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy

Contemporary AI-driven cybersecurity systems are predominantly architected as model-centric detection and automation pipelines optimized for task-level performance metrics such as accuracy and response latency. While effective for bounded classification tasks, these architectures struggle to...

MAL-2026-767 Malicious code in 0xhash-utils (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 6533d0ccd6be4affddc7247e6f5e925ac35fbe47d877eb2cc0ace6e493acc497 The package 0xhash-utils was found to contain malicious code. Source: ghsa-malware df192d86e51f442508e66c54064ef3c8d9c2cbe92133f87a522bc968dc4f6f45 A...

Toward Risk Thresholds for AI-Enabled Cyber Threats: Enhancing Decision-Making under Uncertainty with Bayesian Networks

Artificial intelligence AI is increasingly being used to augment and automate cyber operations, altering the scale, speed, and accessibility of malicious activity. These shifts raise urgent questions about when AI systems introduce unacceptable or intolerable cyber risk, and how risk thresholds...

MAL-2026-403 Malicious code in worldnormal (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 406eb16c91569acae88fa2f33de6107f6a758568c40b800908ac924d1a7e87fd The package worldnormal was found to contain malicious code. Source: ghsa-malware 1c9bf70b2f92f241477ec0cae21b7f094e1d4d1090cbb837bfd90fa9430f26ac An...

Malicious code in victim-package-b (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 152e8188fd82f0ea4ee410d725bb96ab33af5767241fcefb555ef8dfaffd39bf The package victim-package-b was found to contain malicious code. Source: ghsa-malware 324aadc54f696916c968e82f4704d088384eab1ce76c08f2a3d3d0aa59fece...

Decision-Aware Trust Signal Alignment for SOC Alert Triage

Detection systems that utilize machine learning are progressively implemented at Security Operations Centers SOCs to help an analyst to filter through high volumes of security alerts. Practically, such systems tend to reveal probabilistic results or confidence scores which are ill-calibrated and...

Finite-Size Security of QKD: Comparison of Three Proof Techniques

We compare three proof techniques for composable finite-size security of quantum key distribution under collective attacks, with emphasis on how the resulting secret-key rates behave at practically relevant block lengths. As a benchmark, we consider the BB84 protocol and evaluate finite-size...

MAL-2026-70 Malicious code in @shop-cicd/webpack-package-artifact (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 2ee6154f54d35f10e1bca4b64111deef6ab6c43c9ea291a7adfac091b7334ab0 The package @shop-cicd/webpack-package-artifact was found to contain malicious code. Source: ghsa-malware...

CVE-2025-15449

CVE-2025-15449 affects the JavaMall project, specifically the delete function in MinioController.java, where manipulating the objectName argument enables path traversal. This vulnerability can be exploited remotely; affected versions are before 994f1e2b019378ec9444cdf3fce2d5b5f72d28f0. Multiple c...

MAL-2025-192978 Malicious code in tailwindcss-typography-style (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 8f2e4636e4f08bc04591afc5b27fce2e03dea82a9883b2dc8092a6f23fa6f55d The package tailwindcss-typography-style was found to contain malicious code. Source: ghsa-malware...

Uncertainty in Security: Managing Cyber Senescence

My main worry, and the core of my research, is that our cybersecurity ecosystem is slowly but surely aging and getting old and that aging is becoming an operational risk. This is happening not only because of growing complexity, but more importantly because of accumulation of controls and measure...

Enhancing Decision-Making in Windows PE Malware Classification during Dataset Shifts with Uncertainty Estimation

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable PE malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM...

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features

Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...

Quantum Key Distribution: Bridging Theoretical Security Proofs, Practical Attacks, and Error Correction for Quantum-Augmented Networks

Quantum Key Distribution QKD is revolutionizing cryptography by promising information-theoretic security through the immutable laws of quantum mechanics. Yet, the challenge of transforming these idealized security models into practical, resilient systems remains a pressing issue, especially as...

Malicious code in @lui-ui/lui-nuxt (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector aeaeb0138ac2e77901a8360aeeeec1038e7da06fabc4c4726a6fb2060f8d01b5 The package @lui-ui/lui-nuxt was found to contain malicious code. Source: ghsa-malware 7914345d453dc4753973e462d6f8e4cbd4d25656c98b9a22f073c9fdddb715...