June 23, 2026—KB5095091 (OS Build 28000.2340) Preview

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MAL-2026-5794 Malicious code in neural-network-scan (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 898c75e5a6ae94d115820736ffd2ca4cb948f72655d5c0175a3432cec835768c The package ships a collect.js script that imports childprocess and performs an HTTP POST carrying host identifiers hostname referenced multiple time...

Malicious code in neural-network-scan (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 898c75e5a6ae94d115820736ffd2ca4cb948f72655d5c0175a3432cec835768c The package ships a collect.js script that imports childprocess and performs an HTTP POST carrying host identifiers hostname referenced multiple time...

[SECURITY] Fedora 44 Update: sentencepiece-0.2.1-1.fc44

The SentencePiece is an unsupervised text tokenizer for Neural Network-based text generation. It is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece...

CVE-2026-20754

Improper conditions check in some firmware for some IntelR NPU Drivers within Ring 1: Device Drivers may allow a denial of service. Unprivileged software adversary with an authenticated user combined with a low complexity attack may enable denial of service. This result may potentially occur via...

CVE-2026-44246

nnU-Net is a semantic segmentation framework that automatically adapts its pipeline to a dataset. Prior to 2.4.1, the nnU-Net Issue Triage workflow in .github/workflows/issue-triage.yml is vulnerable to Agentic Workflow Injection. The workflow sets allowednonwriteusers: $...

ONNX-MLIR 安全漏洞

ONNX-MLIR is an open-source compiler tool developed by Open Neural Network Exchange that converts ONNX graphs into efficient code. Versions of ONNX-MLIR prior to 0.5.0.0 contained security vulnerabilities. These vulnerabilities stemmed from the use of a weak hash function in the generatehashkey...

Google Chrome 输入验证错误漏洞

Google Chrome is a web browser developed by Google Inc. Versions of Google Chrome prior to 149.0.7827.53 had a vulnerability related to input validation. This vulnerability stemmed from insufficient input validation in the WebNN component, which could allow a remote attacker with access to a...

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks SNNs are...

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...

CVE-2026-42627

In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...

CVE-2026-42627

In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...

PT-2026-42819

Name of the Vulnerable Software and Affected Versions Arm ArmNN versions prior to 2026-03-28 Description An integer overflow exists in the TensorShape::GetNumElements function within armnn/Tensor.cpp. This occurs when tensor dimensions are multiplied using 32-bit unsigned arithmetic without...

CVE-2026-42627

In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...

TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis

Electromagnetic EM side-channel analysis traditionally assumes a stationary, close-proximity probe - a threat model that underestimates aerial adversaries. TriSweep is a simulation framework that designs and evaluates a four-drone swarm architecture for autonomous standoff EM-SCA of embedded...

Encrypted Neural Networks without Overflows

Fully homomorphic encryption FHE enables private inference by evaluating neural networks on encrypted data. In this way, we can delegate the computation to a third party server without ever revealing the user's data. Currently, the CKKS scheme is the backbone of most efficient FHE implementations...

CVE-2025-51427

An issue was discovered in ModelScope 1.25.0 allowing attackers to execute arbitrary code via crafted module listed in the configuration file deymini.yaml under the key 'nnet''module'...

A No-Defense Defense against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?

Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning ML models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently robust Deep Neural Network DNN-based Network Intrusion Detection Systems NIDS,...

From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

Filter-Then-Verify: A Multiphase GNN and ModernBERT Framework for Social Engineering Detection in Email Networks

Social engineering attacks exploit human trust rather than software vulnerabilities, making them difficult to detect using conventional filters. We propose a two-stage filter-then-verify framework combining inductive Graph Neural Networks GNNs for structural anomaly detection with a co-attention...