EUVD-2026-15003

NVIDIA Model Optimizer for Windows and Linux contains a vulnerability in the ONNX quantization feature, where a user could cause unsafe deserialization by providing a specially crafted input file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges,...

CVE-2026-24141

NVIDIA Model Optimizer for Windows and Linux contains a vulnerability in the ONNX quantization feature, where a user could cause unsafe deserialization by providing a specially crafted input file. A successful exploit of this vulnerability might lead to code execution, escalation of privileges,...

SUSE CVE-2026-28500

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

CVE-2026-28500

A flaw was found in Open Neural Network Exchange ONNX, an open standard for machine learning interoperability. A security control bypass exists in the onnx.hub.load function due to improper logic in its repository trust verification. An attacker can exploit this by providing a malicious model,...

CVE-2026-28500

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

PYSEC-2026-103

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

PYSEC-2026-103

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

UBUNTU-CVE-2026-28500

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

CVE-2026-28500

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

CVE-2026-28500 ONNX Untrusted Model Repository Warnings Suppressed by silent=True in onnx.hub.load() — Silent Supply-Chain Attack

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

CVE-2026-28500

Open Neural Network Exchange ONNX is an open standard for machine learning interoperability. In versions up to and including 1.20.1, a security control bypass exists in onnx.hub.load due to improper logic in the repository trust verification mechanism. While the function is designed to warn users...

CVE-2025-12343 Ffmpeg: double-free vulnerability in ffmpeg tensorflow dnn backend

A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnnbackendtf.c source file. The issue occurs in the dnnexecutemodeltf function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free conditio...

CVE-2025-12343 Ffmpeg: double-free vulnerability in ffmpeg tensorflow dnn backend

A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnnbackendtf.c source file. The issue occurs in the dnnexecutemodeltf function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free conditio...

Trojan Attacks on Neural Network Controllers for Robotic Systems

Neural network controllers are increasingly deployed in robotic systems for tasks such as trajectory tracking and pose stabilization. However, their reliance on potentially untrusted training pipelines or supply chains introduces significant security vulnerabilities. This paper investigates...

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...

TrojanGYM: A Detector-In-The-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Hardware Trojans HTs remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce TrojanGYM, an agentic, LLM-driven framework that automatically curates HT insertions to expose detector blind spots while...

Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis

The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based...

Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection Via Latent Space Representation Learning and Alignment

Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...

Demystifying Feature Engineering in Malware Analysis of API Call Sequences

Machine learning ML has been widely used to analyze API call sequences in malware analysis, which typically requires the expertise of domain specialists to extract relevant features from raw data. The extracted features play a critical role in malware analysis. Traditional feature extraction is...