Here's your daily roundup of the most relevant AI and ML news for January 08, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models
arXiv:2601.03265v1 Announce Type: cross Abstract: This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By ...
Source: arXiv - Machine Learning | 1 hours ago
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. ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification
arXiv:2601.03600v1 Announce Type: new Abstract: Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety guardrails and pose serious security risks. Existing detection methods mainly detect jailbreak status rel...
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 - AI | 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. Logic Tensor Network-Enhanced Generative Adversarial Network
arXiv:2601.03839v1 Announce Type: new Abstract: In this paper, we introduce Logic Tensor Network-Enhanced Generative Adversarial Network (LTN-GAN), a novel framework that enhances Generative Adversarial Networks (GANs) by incorporating Logic Tensor Networks (LTNs) to enforce domain-specific logi...
Source: arXiv - Machine Learning | 1 hours ago
7. Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
arXiv:2512.12069v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and efficient for practical deployment. Many current strategies fa...
Source: arXiv - Machine Learning | 1 hours ago
8. 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
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.