CVE-2025-8205

The CVE-2025-8205 entry concerns Comodo Dragon up to version 134.0.6998.179, affecting the IP DNS Leakage Detector component. The issue enables cleartext transmission of sensitive information and can be exploited remotely; exploitation complexity is reported as high. Public disclosure of the expl...

CVE-2025-8205 Comodo Dragon IP DNS Leakage Detector cleartext transmission

A vulnerability, which was classified as problematic, has been found in Comodo Dragon up to 134.0.6998.179. Affected by this issue is some unknown functionality of the component IP DNS Leakage Detector. The manipulation leads to cleartext transmission of sensitive information. The attack may be...

CVE-2025-8205 Comodo Dragon IP DNS Leakage Detector cleartext transmission

A vulnerability, which was classified as problematic, has been found in Comodo Dragon up to 134.0.6998.179. Affected by this issue is some unknown functionality of the component IP DNS Leakage Detector. The manipulation leads to cleartext transmission of sensitive information. The attack may be...

Comodo Dragon 安全漏洞

Comodo Dragon is a web browser from Comodo China. A security vulnerability exists in Comodo Dragon version 134.0.6998.179 and prior versions, which stems from a component IP DNS Leakage Detector that causes sensitive information to be transmitted in clear text...

CVE-2016-15043

The WP Mobile Detector plugin for WordPress is vulnerable to arbitrary file uploads due to missing file type validation in resize.php file in versions up to, and including, 3.5. This makes it possible for unauthenticated attackers to upload arbitrary files on the affected sites server which may...

CVE-2016-15043

WP Mobile Detector plugin for WordPress

CVE-2016-15043 WP Mobile Detector <= 3.5 - Arbitrary File Upload

The WP Mobile Detector plugin for WordPress is vulnerable to arbitrary file uploads due to missing file type validation in resize.php file in versions up to, and including, 3.5. This makes it possible for unauthenticated attackers to upload arbitrary files on the affected sites server which may...

CVE-2016-15043 WP Mobile Detector <= 3.5 - Arbitrary File Upload

The WP Mobile Detector plugin for WordPress is vulnerable to arbitrary file uploads due to missing file type validation in resize.php file in versions up to, and including, 3.5. This makes it possible for unauthenticated attackers to upload arbitrary files on the affected sites server which may...

PT-2025-30129

Name of the Vulnerable Software and Affected Versions WP Mobile Detector versions up to and including 3.5 Description The WP Mobile Detector plugin for WordPress is vulnerable to arbitrary file uploads due to missing file type validation in the resize.php file. This allows unauthenticated attacke...

WordPress plugin WP Mobile Detector 代码问题漏洞

WordPress and WordPress plugin are both products of the WordPress Foundation.WordPress is a blogging platform developed using the PHP language. The platform supports setting up personal blog sites on servers with PHP and MySQL.WordPress plugin is an application plugin. A code issue vulnerability...

CovertAuth: Joint Covert Communication and Authentication in MmWave Systems

Beam alignment BA is a crucial process in millimeter-wave mmWave communications, enabling precise directional transmission and efficient link establishment. However, due to characteristics like omnidirectional exposure and the broadcast nature of the BA phase, it is particularly vulnerable to...

Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers

Deep Neural Networks DNNs are notoriously vulnerable to adversarial input designs with limited noise budgets. While numerous successful attacks with subtle modifications to original input have been proposed, defense techniques against these attacks are relatively understudied. Existing defense...

DEBIAN-CVE-2022-49994

In the Linux kernel, the following vulnerability has been resolved: bootmem: remove the vmemmap pages from kmemleak in putpagebootmem The vmemmap pages is marked by kmemleak when allocated from memblock. Remove it from kmemleak when freeing the page. Otherwise, when we reuse the page, kmemleak ma...

CVE-2022-49994

In the Linux kernel, the following vulnerability has been resolved: bootmem: remove the vmemmap pages from kmemleak in putpagebootmem The vmemmap pages is marked by kmemleak when allocated from memblock. Remove it from kmemleak when freeing the page. Otherwise, when we reuse the page, kmemleak ma...

Linux kernel 安全漏洞

Linux kernel is the kernel used by Linux, the open source operating system of the Linux Foundation in the United States. A security vulnerability exists in the Linux kernel that stems from bootmem not removing the vmemmap page from kmemleak, which could cause memory leak detection to stop...

Securing Traffic Sign Recognition Systems in Autonomous Vehicles

Deep Neural Networks DNNs are widely used for traffic sign recognition because they can automatically extract high-level features from images. These DNNs are trained on large-scale datasets obtained from unknown sources. Therefore, it is important to ensure that the models remain secure and are n...

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning FL solutions in the vehicular intrusion detectio...

Fooling the Watchers: Breaking AIGC Detectors Via Semantic Prompt Attacks

The rise of text-to-image T2I models has enabled the synthesis of photorealistic human portraits, raising serious concerns about identity misuse and the robustness of AIGC detectors. In this work, we propose an automated adversarial prompt generation framework that leverages a grammar tree...

Test-Time Immunization: a Universal Defense Framework against Jailbreaks for (Multimodal) Large Language Models

While multimodal large language models LLMs have attracted widespread attention due to their exceptional capabilities, they remain vulnerable to jailbreak attacks. Various defense methods are proposed to defend against jailbreak attacks, however, they are often tailored to specific types of...

Mal-D2GAN: Double-Detector Based GAN for Malware Generation

Machine learning ML has been developed to detect malware in recent years. Most researchers focused their efforts on improving the detection performance but ignored the robustness of the ML models. In addition, many machine learning algorithms are very vulnerable to intentional attacks. To solve...