535 matches found
CVE-2023-46616
Missing Authorization vulnerability in NSquared Draw Attention allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects Draw Attention: from n/a through 2.0.15...
CVE-2023-2764
The Draw Attention plugin for WordPress is vulnerable to unauthorized modification of data due to a missing capability check on the ajaxsetfeaturedimage function in versions up to, and including, 2.0.11. This makes it possible for authenticated attackers, with subscriber-level permissions and...
BELL-CVE-2025-37948
Bulletin has no description...
Backdoor Cleaning without External Guidance in MLLM Fine-Tuning
Multimodal Large Language Models MLLMs are increasingly deployed in fine-tuning-as-a-service FTaaS settings, where user-submitted datasets adapt general-purpose models to downstream tasks. This flexibility, however, introduces serious security risks, as malicious fine-tuning can implant backdoors...
Reliable Disentanglement Multi-View Learning against View Adversarial Attacks
Trustworthy multi-view learning has attracted extensive attention because evidence learning can provide reliable uncertainty estimation to enhance the credibility of multi-view predictions. Existing trusted multi-view learning methods implicitly assume that multi-view data is secure. However, in...
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning
Large Reasoning Models LRMs introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs,...
CSAGC-IDS: a Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing...
Diverging Towards Hallucination: Detection of Failures in Vision-Language Models Via Multi-Token Aggregation
Vision-language models VLMs now rival human performance on many multimodal tasks, yet they still hallucinate objects or generate unsafe text. Current hallucination detectors, e.g., single-token linear probing SLP and PTrue, typically analyze only the logit of the first generated token or just its...
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning
Federated learning FL enhances privacy and reduces communication cost for resource-constrained edge clients by supporting distributed model training at the edge. However, the heterogeneous nature of such devices produces diverse, non-independent, and identically distributed non-IID data, making t...
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates t...
Mitigating the Structural Bias in Graph Adversarial Defenses
In recent years, graph neural networks GNNs have shown great potential in addressing various graph structure-related downstream tasks. However, recent studies have found that current GNNs are susceptible to malicious adversarial attacks. Given the inevitable presence of adversarial attacks in the...
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
The expansion of large-scale text-to-image diffusion models has raised growing concerns about their potential to generate undesirable or harmful content, ranging from fabricated depictions of public figures to sexually explicit images. To mitigate these risks, prior work has devised machine...
Contrastive Learning for Continuous Touch-Based Authentication
Smart mobile devices have become indispensable in modern daily life, where sensitive information is frequently processed, stored, and transmitted-posing critical demands for robust security controls. Given that touchscreens are the primary medium for human-device interaction, continuous user...
MAL-2025-3417 Malicious code in sticos.oppslag.web (npm)
--- -= Per source details. Do not edit below this line.=-...
How Private Is Your Attention? Bridging Privacy with In-Context Learning
In-context learning ICL-the ability of transformer-based models to perform new tasks from examples provided at inference time-has emerged as a hallmark of modern language models. While recent works have investigated the mechanisms underlying ICL, its feasibility under formal privacy constraints...
Backdoor Defense in Diffusion Models Via Spatial Attention Unlearning
Text-to-image diffusion models are increasingly vulnerable to backdoor attacks, where malicious modifications to the training data cause the model to generate unintended outputs when specific triggers are present. While classification models have seen extensive development of defense mechanisms,...
Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks
As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major...
From Data Behavior to Code Analysis: a Multimodal Study on Security and Privacy Challenges in Blockchain-Based DApp
The recent proliferation of blockchain-based decentralized applications DApp has catalyzed transformative advancements in distributed systems, with extensive deployments observed across financial, entertainment, media, and cybersecurity domains. These trustless architectures, characterized by the...
