Tootfinder

L’inculpation de James Comey, ancien directeur du FBI, marque le début des vengeances judiciaires de Donald Trump
https://www.lemonde.fr/international/artic

L’inculpation de James Comey, ancien directeur du FBI, marque le début des vengeances judiciaires de Donald Trump
Le président américain avait demandé à sa ministre de la justice de poursuivre l’ancien responsable de la police fédérale, désormais accusé de faux témoignage et d’obstruction. D’autres adversaires politiques pourraient subir le même sort.

Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models
Qiming Guo, Jinwen Tang, Xingran Huang
https://arxiv.org/abs/2508.17674 https://

Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models
We introduce Advertisement Embedding Attacks (AEA), a new class of LLM security threats that stealthily inject promotional or malicious content into model outputs and AI agents. AEA operate through two low-cost vectors: (1) hijacking third-party service-distribution platforms to prepend adversarial prompts, and (2) publishing back-doored open-source checkpoints fine-tuned with attacker data. Unlike conventional attacks that degrade accuracy, AEA subvert information integrity, causing models to …

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Hongbo Liu, Siyi Li, Zheng Yu
https://arxiv.org/abs/2508.18766 https://

Predicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to…

A Predictive Framework for Adversarial Energy Depletion in Inbound Threat Scenarios
Tam W. Nguyen
https://arxiv.org/abs/2508.17805 https://arxiv.org/pdf/25…

A Predictive Framework for Adversarial Energy Depletion in Inbound Threat Scenarios
This paper presents a predictive framework for adversarial energy-depletion defense against a maneuverable inbound threat (IT). The IT solves a receding-horizon problem to minimize its own energy while reaching a high-value asset (HVA) and avoiding interceptors and static lethal zones modeled by Gaussian barriers. Expendable interceptors (EIs), coordinated by a central node (CN), maintain proximity to the HVA and patrol centers via radius-based tether costs, deny attack corridors by harassing a…

Introduction to AT Protocol – mackuba.eu https://mackuba.eu/2025/08/20/introduction-to-atproto/

Introduction to AT Protocol
Walkthrough of the various parts and concepts in Bluesky's AT Protocol (ATProto), the types of servers involved and how it all fits together

Filing: Intel warns the US taking a 10% stake could trigger "adverse reactions", including in international sales, which made up 76% of revenue last fiscal year (Chris Eudaily/CNBC)
https://www.cnbc.com/2025/08/25/intel-trump-deal-risks-stock.html
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Intel says Trump deal has risks for shareholders, international sales
President Donald Trump called the agreement "a great Deal for America" and said the building of advanced chips "is fundamental to the future of our Nation." 

Beyond-Diagonal RIS: Adversarial Channels and Optimality of Low-Complexity Architectures
Atso Iivanainen, Robin Rajam\"aki, Visa Koivunen
https://arxiv.org/abs/2508.19000 h…

Beyond-Diagonal RIS: Adversarial Channels and Optimality of Low-Complexity Architectures
Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) have recently gained attention as an enhancement to conventional RISs. BD-RISs allow optimizing not only the phase, but also the amplitude responses of their discrete surface elements by introducing adjustable inter-element couplings. Various BD-RIS architectures have been proposed to optimally trade off between average performance and complexity of the architecture. However, little attention has been paid to worst-case performance. …

BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting
Yixun Zhang, Feng Zhou, Jianqin Yin
https://arxiv.org/abs/2509.19793 http…

BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting
Camera-based perception is critical to autonomous driving yet remains vulnerable to task-specific adversarial manipulations in object detection and monocular depth estimation. Most existing 2D/3D attacks are developed in task silos, lack mechanisms to induce controllable depth bias, and offer no standardized protocol to quantify cross-task transfer, leaving the interaction between detection and depth underexplored. We present BiTAA, a bi-task adversarial attack built on 3D Gaussian Splatting th…

Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models
Rui Zhang, Zihan Wang, Tianli Yang, Hongwei Li, Wenbo Jiang, Qingchuan Zhao, Yang Liu, Guowen Xu
https://arxiv.org/abs/2508.18805

Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models
Vision-Language Models (VLMs) are increasingly deployed in real-world applications, but their high inference cost makes them vulnerable to resource consumption attacks. Prior attacks attempt to extend VLM output sequences by optimizing adversarial images, thereby increasing inference costs. However, these extended outputs often introduce irrelevant abnormal content, compromising attack stealthiness. This trade-off between effectiveness and stealthiness poses a major limitation for existing atta…

SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weather
Edoardo Palladin, Roland Dietze, Praveen Narayanan, Mario Bijelic, Felix Heide
https://arxiv.org/abs/2508.16408

SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weather
Multimodal sensor fusion is an essential capability for autonomous robots, enabling object detection and decision-making in the presence of failing or uncertain inputs. While recent fusion methods excel in normal environmental conditions, these approaches fail in adverse weather, e.g., heavy fog, snow, or obstructions due to soiling. We introduce a novel multi-sensor fusion approach tailored to adverse weather conditions. In addition to fusing RGB and LiDAR sensors, which are employed in recent…