CVE-2026-39452

Protection mechanism failure for some IntelR Transfer Learning Tool before version v0.7 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. Thi...

EUVD-2026-56533

Protection mechanism failure for some IntelR Transfer Learning Tool before version v0.7 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. Thi...

CVE-2026-39452

Protection mechanism failure for some IntelR Transfer Learning Tool before version v0.7 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. Thi...

CVE-2026-39452

Protection mechanism failure for some IntelR Transfer Learning Tool before version v0.7 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. Thi...

CVE-2026-39452

The vulnerability CVE-2026-39452 affects Intel’s Transfer Learning Tool prior to version v0.7. It is a protection mechanism failure that could allow a network-based escalation of privilege by an unauthenticated, unprivileged user with low attack complexity, without user interaction. The impact is...

PT-2026-70343

Protection mechanism failure for some IntelR Transfer Learning Tool before version v0.7 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an unauthenticated user combined with a low complexity attack may enable escalation of privilege. Thi...

ntel® Transfer Learning Tool Software Advisory

Summary: A potential security vulnerability for some Intel® Transfer Learning Tool software may allow escalation of privilege. Intel is not releasing updates to mitigate this potential vulnerability and has issued a Product Discontinuation Notice for the Intel® Transfer Learning Tool software...

Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforceme...

Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection under Resource Constraints

Distributed denial-of-service DDoS attacks threaten the availability of Internet of Things IoT infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility, a...

Machine and Deep Learning for Indoor UWB Jammer Localization

Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...

New Machine Learning Approaches for Intrusion Detection in ADS-B

With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast ADS-B protocol in air traffic management ATM, ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems IDS...

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

Internet of Vehicles IoV systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and...

Evaluating Query Efficiency and Accuracy of Transfer Learning-Based Model Extraction Attack in Federated Learning

Federated Learning FL is a collaborative learning framework designed to protect client data, yet it remains highly vulnerable to Intellectual Property IP threats. Model extraction ME attacks pose a significant risk to Machine Learning as a Service MLaaS platforms, enabling attackers to replicate...

Vulnerability of Transfer-Learned Neural Networks to Data Reconstruction Attacks in Small-Data Regime

Training data reconstruction attacks enable adversaries to recover portions of a released model's training data. We consider the attacks where a reconstructor neural network learns to invert the random mapping between training data and model weights. Prior work has shown that an informed adversar...

Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification

Deep learning DL-based image classification models are essential for autonomous vehicle AV perception modules since incorrect categorization might have severe repercussions. Adversarial attacks are widely studied cyberattacks that can lead DL models to predict inaccurate output, such as incorrect...

Privacy-Preserving CNN Training with Transfer Learning: Two Hidden Layers

Whitepaper called Privacy-Preserving CNN Training With Transfer Learning: Two Hidden Layers...

Secure Transfer Learning: Training Clean Models against Backdoor in (Both) Pre-Trained Encoders and Downstream Datasets

Transfer learning from pre-trained encoders has become essential in modern machine learning, enabling efficient model adaptation across diverse tasks. However, this combination of pre-training and downstream adaptation creates an expanded attack surface, exposing models to sophisticated backdoor...

Black Hat 2020: Open-Source AI to Spur Wave of 'Synthetic Media' Attacks

An abundance of deep-learning and open-source technologies are making it easy for cybercriminals to generate fake images, text and audio called “synthetic media”. This type of media can be easily leveraged on Facebook, Twitter and other social media platforms to launch disinformation campaigns wi...

Repurposing Neural Networks to Generate Synthetic Media for Information Operations

FireEye’s Data Science and Information Operations Analysis teams released this blog post to coincide with our Black Hat USA 2020 Briefing, which details how open source, pre-trained neural networks can be leveraged to generate synthetic media for malicious purposes. To summarize our presentation,...