Tootfinder

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna
https://arxiv.org/abs/2505.23849

CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves privacy and security of the client's data by not exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data REadiness) for FL, a novel framework that allows users to define…

Trojan Horse Hunt in Time Series Forecasting for Space Operations
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa, Przemys{\l}aw Biecek, Piotr Wilczy\'nski, Agata Kaczmarek, Dawid P{\l}udowski, Artur Janicki, Evridiki Ntagiou
https://arxiv.org/abs/2506.01849

Trojan Horse Hunt in Time Series Forecasting for Space Operations
This competition hosted on Kaggle (https://www.kaggle.com/competitions/trojan-horse-hunt-in-space) is the first part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/). The competition idea is based on one of the real-life AI security threats identified within the project -- the adversarial poisoning of continuously fine-tuned satellite telemetry fore…

"new evidence considered by the appeal court was that an eyewitness was given "substantial sums of money by the authorities pursuant to an agreement after he testified at the trial," Innocence Canada said."
https://www.cbc.ca/1.7550389

Microsoft and the CWA reach a tentative contract agreement for ~300 ZeniMax QA workers after two years of talks, marking Microsoft's first US union contract (Josh Eidelson/Bloomberg)
https://www.bloomberg.com/news/articles/20

This https://arxiv.org/abs/2505.13182 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping
[Objective] This study focuses on addressing the current lack of a unified formal theoretical framework in machine learning, as well as the deficiencies in interpretability and ethical safety assurance. [Methods] A formal information model is first constructed, utilizing sets of well-formed formulas to explicitly define the ontological states and carrier mappings of typical components in machine learning. Learnable and processable predicates, along with learning and processing functions, are in…

RewardBench 2: Advancing Reward Model Evaluation
Saumya Malik, Valentina Pyatkin, Sander Land, Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Nathan Lambert
https://arxiv.org/abs/2506.01937

RewardBench 2: Advancing Reward Model Evaluation
Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from the development of benchmarks that test capabilities in specific skill areas to others that test agreement with human preferences. At the same time, progress in evaluation has not…

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Chenxu Liu, Zhiyu Gu, Guoquan Wu, Ying Zhang, Jun Wei, Tao Xie
https://arxiv.org/abs/2506.00520

Temac: Multi-Agent Collaboration for Automated Web GUI Testing
Quality assurance of web applications is critical, as web applications play an essential role in people's daily lives. To reduce labor costs, automated web GUI testing (AWGT) is widely adopted, exploring web applications via GUI actions such as clicks and text inputs. However, these approaches face limitations in generating continuous and meaningful action sequences capable of covering complex functionalities. Recent work incorporates large language models (LLMs) for GUI testing. However, these…

ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes
Akram Sheriff, Ken Huang, Zsolt Nemeth, Madjid Nakhjiri
https://arxiv.org/abs/2505.23805

ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes
This paper introduces the Adaptive Defense Agent (ADA), an innovative Automated Moving Target Defense (AMTD) system designed to fundamentally enhance the security posture of AI workloads. ADA operates by continuously and automatically rotating these workloads at the infrastructure level, leveraging the inherent ephemerality of Kubernetes pods. This constant managed churn systematically invalidates attacker assumptions and disrupts potential kill chains by regularly destroying and respawning AI …

This https://arxiv.org/abs/2405.12042 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Attribute-Based Authentication in Secure Group Messaging for Distributed Environments
Messaging Layer security (MLS) and its underlying Continuous Group Key Agreement (CGKA) protocol allows a group of users to share a cryptographic secret in a dynamic manner, such that the secret is modified in member insertions and deletions. Although this flexibility makes MLS ideal for implementations in distributed environments, a number of issues need to be overcome. Particularly, the use of digital certificates for authentication in a group goes against the group members' privacy. In this …

This https://arxiv.org/abs/2311.16139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models
Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as social network analysis and fraud detection for financial services. At the heart of these networks are the edges, which are crucial in guiding GNN models' predictions. In many scenarios, these edges represent sensitive information, such as personal associations or financial dealings, which require privacy assurance. However, their contributions to GNN mo…