Here's your daily roundup of the most relevant AI and ML news for January 05, 2026. Today's digest includes 1 security-focused story. We're also covering 7 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. AI Safety ArXiv Scraper
Article URL: https://theguardrail.net/ Comments URL: https://news.ycombinator.com/item?id=46494587 Points: 2
Comments: 0
Source: Hacker News - ML Security | 3 hours ago
Research & Papers
2. Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments
arXiv:2511.13788v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can system...
Source: arXiv - Machine Learning | 1 hours ago
3. Robust Graph Fine-Tuning with Adversarial Graph Prompting
arXiv:2601.00229v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) method has emerged as a dominant paradigm for adapting pre-trained GNN models to downstream tasks. However, existing PEFT methods usually exhibit significant vulnerability to various noise and attacks on graph...
Source: arXiv - Machine Learning | 1 hours ago
4. SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification
arXiv:2601.00189v1 Announce Type: new Abstract: Mosquitos are the main transmissive agents of arboviral diseases. Manual classification of their neuronal spike patterns is very labor-intensive and expensive. Most available deep learning solutions require fully labeled spike datasets and highly p...
Source: arXiv - Machine Learning | 1 hours ago
5. Rectifying Adversarial Examples Using Their Vulnerabilities
arXiv:2601.00270v1 Announce Type: cross Abstract: Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications. Hence, extensive rese...
Source: arXiv - Machine Learning | 1 hours ago
6. PatchBlock: A Lightweight Defense Against Adversarial Patches for Embedded EdgeAI Devices
arXiv:2601.00367v1 Announce Type: cross Abstract: Adversarial attacks pose a significant challenge to the reliable deployment of machine learning models in EdgeAI applications, such as autonomous driving and surveillance, which rely on resource-constrained devices for real-time inference. Among ...
Source: arXiv - AI | 1 hours ago
7. Adversarial Samples Are Not Created Equal
arXiv:2601.00577v1 Announce Type: new Abstract: Over the past decade, numerous theories have been proposed to explain the widespread vulnerability of deep neural networks to adversarial evasion attacks. Among these, the theory of non-robust features proposed by Ilyas et al. has been widely accep...
Source: arXiv - Machine Learning | 1 hours ago
8. A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
arXiv:2502.07514v2 Announce Type: replace Abstract: We investigate various stochastic bandit problems in the presence of adversarial corruptions. A seminal work for this problem is the BARBAR~\cite{gupta2019better} algorithm, which achieves both robustness and efficiency. However, it suffers fro...
Source: arXiv - Machine Learning | 1 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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