613 matches found
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...
CVE-2025-32735
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-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...
Kill It with FIRE: On Leveraging Latent Space Directions for Runtime Backdoor Mitigation in Deep Neural Networks
Machine learning models are increasingly present in our everyday lives; as a result, they become targets of adversarial attackers seeking to manipulate the systems we interact with. A well-known vulnerability is a backdoor introduced into a neural network by poisoned training data or a malicious...
CVE-2025-32735
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...
DEBIAN-CVE-2025-32735
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-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
No description provided...
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...
