NVIDIA Nemo Framework 命令注入漏洞

NVIDIA Nemo Framework is a framework developed by NVIDIA Corporation in the United States for building and deploying generative AI models. The NVIDIA NeMo Framework has a command injection vulnerability. This vulnerability arises from malicious inputs created by attackers in the voice preprocessi...

PT-2026-20406

NVIDIA NeMo Framework for all platforms contains a vulnerability in a voice-preprocessing script, where malicious input created by an attacker could cause a code injection. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, information disclosure, a...

PIDSMaker: Building and Evaluating Provenance-Based Intrusion Detection Systems

Recent provenance-based intrusion detection systems PIDSs have demonstrated strong potential for detecting advanced persistent threats APTs by applying machine learning to system provenance graphs. However, evaluating and comparing PIDSs remains difficult: prior work uses inconsistent preprocessi...

CVE-2022-31540

The kumardeepak/hin-eng-preprocessing repository through 2019-07-16 on GitHub allows absolute path traversal because the Flask sendfile function is used unsafely...

CVE-2025-47380

Memory corruption while preprocessing IOCTLs in sensors...

CVE-2025-47380

CVE-2025-47380 is a memory corruption vulnerability in the sensors component that occurs during preprocessing of IOCTLs. Affected: the sensors functionality; root cause reported as memory corruption during IOCTL preprocessing. Documented impact indicates high severity with full confidentiality, i...

CVE-2025-47380 Untrusted Pointer Dereference in Camera

Memory corruption while preprocessing IOCTLs in sensors...

PT-2026-1541

Name of the Vulnerable Software and Affected Versions sensors affected versions not specified Description A memory corruption issue exists when processing IOCTLs within the sensors component. The issue occurs during the preprocessing of IOCTLs. Recommendations At the moment, there is no informati...

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...

Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic

Encrypted network traffic poses significant challenges for intrusion detection due to the lack of payload visibility, limited labeled datasets, and high class imbalance between benign and malicious activities. Traditional data augmentation methods struggle to preserve the complex temporal and...

EUVD-2025-199640

CGGMP24 is a state-of-art ECDSA TSS protocol that supports 1-round signing requires 3 preprocessing rounds, identifiable abort, and a key refresh protocol. In versions 0.6.3 and prior of cggmp21 and version 0.7.0-alpha.1 of cggmp24, presignatures can be used in the way that significantly reduces...

CVE-2025-66017

CVE-2025-66017 affects the CGGMP family (CGGMP21 and CGGMP24). The vulnerability arises from improper use of presignatures in specific configurations, allowing signature forgery or reduced security. Affected details indicate that in CGGMP21 <= 0.6.3 and CGGMP24

Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...

DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining

RISC-V processors are becoming ubiquitous in critical applications, but their susceptibility to microarchitectural side-channel attacks is a serious concern. Detection of microarchitectural attacks in RISC-V is an emerging research topic that is relatively underexplored, compared to x86 and ARM...

CVE-2025-21052

Out-of-bounds write under specific condition in the pre-processing of JPEG decoding in libpadm.so prior to SMR Oct-2025 Release 1 allows local attackers to cause memory corruption...

CVE-2025-21052

Out-of-bounds write under specific condition in the pre-processing of JPEG decoding in libpadm.so prior to SMR Oct-2025 Release 1 allows local attackers to cause memory corruption...

CVE-2025-21051

Out-of-bounds write in the pre-processing of JPEG decoding in libpadm.so prior to SMR Oct-2025 Release 1 allows local attackers to write out-of-bounds memory...

CVE-2025-21052

The CVE-2025-21052 issue affects the libpadm.so library in Samsung Mobile devices, caused by an out-of-bounds write during the pre-processing stage of JPEG decoding. This vulnerability can lead to memory corruption when exploited locally. Affected versions are libpadm.so prior to SMR Oct-2025 Rel...

CVE-2025-21051

Out-of-bounds write in the pre-processing of JPEG decoding in libpadm.so prior to SMR Oct-2025 Release 1 allows local attackers to write out-of-bounds memory...

EUVD-2023-33018

Malicious code in bioql PyPI...