535 matches found
WordPress VG WORT METIS plugin <= 2.0.1 - Broken Access Control Vulnerability
Broken Access Control Vulnerability discovered by Nguyen Ngoc Quang Bach maysbachs in WordPress Plugin VG WORT METIS versions = 2.0.1...
CodeGuard: a Generalized and Stealthy Backdoor Watermarking for Generative Code Models
Generative code models GCMs significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models require computational resources and time, necessitating effective digital copyright protection to prevent unauthorized lea...
Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks
Website Fingerprinting WF attacks aim to infer which websites a user is visiting by analyzing traffic patterns, thereby compromising user anonymity. Although this technique has been demonstrated to be effective in controlled experimental environments, it remains largely limited to small-scale...
RHEL 10 : xorg-x11-server-Xwayland (RHSA-2025:9304)
The remote Redhat Enterprise Linux 10 host has packages installed that are affected by multiple vulnerabilities as referenced in the RHSA-2025:9304 advisory. Xwayland is an X server for running X clients under Wayland. Security Fixes: xorg-x11-server-Xwayland: xorg-x11-server: tigervnc:...
FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...
Mechanistic Interpretability in the Presence of Architectural Obfuscation
Architectural obfuscation - e.g., permuting hidden-state tensors, linearly transforming embedding tables, or remapping tokens - has recently gained traction as a lightweight substitute for heavyweight cryptography in privacy-preserving large-language-model LLM inference. While recent work has sho...
SecFwT: Efficient Privacy-Preserving Fine-Tuning of Large Language Models Using Forward-Only Passes
Large language models LLMs have transformed numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains, such as healthcare and finance, is constrained by the scarcity of accessible training data due to stringent privacy requirements. Secure multi-party computation...
Graph Neural Networks for Jamming Source Localization
Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introduce the first application of graph-based learning for jamming source...
Theoretically Unmasking Inference Attacks against LDP-Protected Clients in Federated Vision Models
Federated Learning enables collaborative learning among clients via a coordinating server while avoiding direct data sharing, offering a perceived solution to preserve privacy. However, recent studies on Membership Inference Attacks MIAs have challenged this notion, showing high success rates...
MINI-5R84-89C4-2239
Bulletin has no description...
CAPAA: Classifier-Agnostic Projector-Based Adversarial Attack
Projector-based adversarial attack aims to project carefully designed light patterns i.e., adversarial projections onto scenes to deceive deep image classifiers. It has potential applications in privacy protection and the development of more robust classifiers. However, existing approaches...
Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
Retrieval-augmented generation RAG systems are vulnerable to attacks that inject poisoned passages into the retrieved set, even at low corruption rates. We show that existing attacks are not designed to be stealthy, allowing reliable detection and mitigation. We formalize stealth using a...
Safety Alignment Can Be Not Superficial with Explicit Safety Signals
Recent studies on the safety alignment of large language models LLMs have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data...
Looking for Attention: Randomized Attention Test Design for Validator Monitoring in Optimistic Rollups
Optimistic Rollups ORUs significantly enhance blockchain scalability but inherently suffer from the verifier's dilemma, particularly concerning validator attentiveness. Current systems lack mechanisms to proactively ensure validators are diligently monitoring L2 state transitions, creating a...
PYSEC-2025-53
vLLM is an inference and serving engine for large language models LLMs. Prior to version 0.9.0, when a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT Time to First Token. These timing differences...
Timing Attack
Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Timing Attack due to the PageAttention mechanism. An attacker can observe timing differences to infer details about the processed data by analyzing...
MINI-9PCJ-GJQC-XV52
Bulletin has no description...
TensorShield: Safeguarding On-Device Inference by Shielding Critical DNN Tensors with TEE
To safeguard user data privacy, on-device inference has emerged as a prominent paradigm on mobile and Internet of Things IoT devices. This paradigm involves deploying a model provided by a third party on local devices to perform inference tasks. However, it exposes the private model to two primar...
Structure Disruption: Subverting Malicious Diffusion-Based Inpainting Via Self-Attention Query Perturbation
The rapid advancement of diffusion models has enhanced their image inpainting and editing capabilities but also introduced significant societal risks. Adversaries can exploit user images from social media to generate misleading or harmful content. While adversarial perturbations can disrupt...