Here's your daily roundup of the most relevant AI and ML news for January 07, 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. World 'may not have time' to prepare for AI safety risks
Article URL: https://www.theguardian.com/technology/2026/jan/04/world-may-not-have-time-to-prepare-for-ai-safety-risks-says-leading-researcher Comments URL: https://news.ycombinator.com/item?id=46521075 Points: 1
Comments: 1
Source: Hacker News - ML Security | 5 hours ago
Research & Papers
2. When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection
arXiv:2512.10449v3 Announce Type: replace Abstract: Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional AI-powered assessment systems. This study investigates the robustness of "LLM-as-a-Judge" systems...
Source: arXiv - AI | 1 hours ago
3. Adversarial Contrastive Learning for LLM Quantization Attacks
arXiv:2601.02680v1 Announce Type: cross Abstract: Model quantization is critical for deploying large language models (LLMs) on resource-constrained hardware, yet recent work has revealed severe security risks that benign LLMs in full precision may exhibit malicious behaviors after quantization. ...
Source: arXiv - Machine Learning | 1 hours ago
4. ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generation
arXiv:2601.03121v1 Announce Type: cross Abstract: Augmenting toxic language data in a controllable and class-specific manner is crucial for improving robustness in toxicity classification, yet remains challenging due to limited supervision and distributional skew. We propose ToxiGAN, a class-awa...
Source: arXiv - Machine Learning | 1 hours ago
5. Adversarial Question Answering Robustness: A Multi-Level Error Analysis and Mitigation Study
arXiv:2601.02700v1 Announce Type: cross Abstract: Question answering (QA) systems achieve impressive performance on standard benchmarks like SQuAD, but remain vulnerable to adversarial examples. This project investigates the adversarial robustness of transformer models on the AddSent adversarial...
Source: arXiv - AI | 1 hours ago
6. JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification
arXiv:2601.03005v1 Announce Type: cross Abstract: Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specific harmful parameters, current methods remain vulnerable to divers...
Source: arXiv - AI | 1 hours ago
7. E$^2$AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models
arXiv:2503.04833v3 Announce Type: replace-cross Abstract: Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness still face critical challenges: \ding{172} how to efficient...
Source: arXiv - AI | 1 hours ago
8. Chronicals: A High-Performance Framework for LLM Fine-Tuning with 3.51x Speedup over Unsloth
arXiv:2601.02609v1 Announce Type: new Abstract: Large language model fine-tuning is bottlenecked by memory: a 7B parameter model requires 84GB--14GB for weights, 14GB for gradients, and 56GB for FP32 optimizer states--exceeding even A100-40GB capacity. We present Chronicals, an open-source train...
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.
Want to see how your favorite models score on security? Check our model dashboard for trust scores on the top 500 HuggingFace models.