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...

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...

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...

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'...

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...

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,...

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...

MalwarePT: A Binary-Level Foundation Model for Malware Analysis

Automated malware analysis increasingly relies on machine learning, yet most existing methods remain task-specific and depend on handcrafted features or narrowly scoped models. Recent developments in binary-level foundation models suggest a path toward reusable program representations, but their...

Intel NPU Driver May 2026 Security Update

Intel has informed HP of potential vulnerabilities identified in the Intel® NPU Drivers which might allow escalation of privilege or denial of service. Intel is releasing software updates to mitigate these potential vulnerabilities. Intel has released updates to mitigate the potential...

Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks

Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a clean model. These guarantees, however, have been confined to stylised architectures of limited practical relevance, leaving open whether comparable...

EUVD-2026-29525

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...