CVE-2025-33030

Improper conditions check in some firmware for some IntelR NPU Drivers within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with an authenticated user combined with a low complexity attack may enable data corruption. This result may potentially...

CVE-2025-35992

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 high complexity attack may enable denial of service. This result may potentially occur via...

Intel NPU Driver Advisory - Lenovo Support US

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Intel NPU Drivers 代码问题漏洞

Intel NPU Drivers are driver programs for Intel’s Neural Network Processing Units. There are code vulnerabilities in Intel NPU Drivers, which stem from improper firmware conditional checks. These vulnerabilities may lead to denial-of-service attacks...

📄 NPU Driver Use-After-Free Detector

This Metasploit module detects vulnerable NPU drivers susceptible to CVE-2025-21424, a use-after-free vulnerability in the MSM NPU kernel driver. Additional details are included that identify shortcomings in the original proof of concept...

Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agent

Understanding TTPs Tactics, Techniques, and Procedures in malware binaries is essential for security analysis and threat intelligence, yet remains challenging in practice. Real-world malware binaries are typically stripped of symbols, contain large numbers of functions, and distribute malicious...

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...

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection

The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely on Graph Neural Networks GNNs. However, the effectiveness of these GNN-based detectors in real-world settings remains poorly understood. In practice...

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...

CVE-2023-40218

An issue was discovered in the NPU kernel driver in Samsung Exynos Mobile Processor 9820, 980, 2100, 2200, 1280, and 1380. An integer overflow can bypass detection of error cases via a crafted application...

CVE-2024-39368

Improper neutralization of special elements used in an SQL command 'SQL Injection' in some IntelR Neural Compressor software before version v3.0 may allow an authenticated user to potentially enable escalation of privilege via adjacent access...

Exploit for Off-by-one Error in Sudo_Project Sudo

ExploitForge - AI-Powered Automatic Exploit Generation !Pyt...

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...

Machine Learning Power Side-Channel Attack on SNOW-V

This paper demonstrates a power analysis-based Side-Channel Analysis SCA attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test...

CVE-2025-13708 Tencent NeuralNLP-NeuralClassifier _load_checkpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability

Tencent NeuralNLP-NeuralClassifier loadcheckpoint Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Tencent NeuralNLP-NeuralClassifier. User interaction is required to exploit th...

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...

PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS

Modern intrusion detection systems IDS leverage graph neural networks GNNs to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations...

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning QML, h...