Here's your daily roundup of the most relevant AI and ML news for January 26, 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. Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away
Article URL: https://www.wsj.com/tech/ai/anthropic-claude-code-ai-7a46460e Comments URL: https://news.ycombinator.com/item?id=46772483 Points: 3
Comments: 0
Source: Hacker News - ML Security | 1 hours ago
Research & Papers
2. LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems
arXiv:2601.16890v1 Announce Type: cross Abstract: Automated fact-checking (AFC) systems are susceptible to adversarial attacks, enabling false claims to evade detection. Existing adversarial frameworks typically rely on injecting noise or altering semantics, yet no existing framework exploits th...
Source: arXiv - Machine Learning | 18 hours ago
3. LLM Jailbreak Detection for (Almost) Free!
arXiv:2509.14558v2 Announce Type: replace-cross Abstract: Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak att...
Source: arXiv - AI | 18 hours ago
4. The Art of Being Difficult: Combining Human and AI Strengths to Find Adversarial Instances for Heuristics
arXiv:2601.16849v1 Announce Type: new Abstract: We demonstrate the power of human-LLM collaboration in tackling open problems in theoretical computer science. Focusing on combinatorial optimization, we refine outputs from the FunSearch algorithm [Romera-Paredes et al., Nature 2023] to derive sta...
Source: arXiv - Machine Learning | 18 hours ago
5. On the Effects of Adversarial Perturbations on Distribution Robustness
arXiv:2601.16464v1 Announce Type: new Abstract: Adversarial robustness refers to a model's ability to resist perturbation of inputs, while distribution robustness evaluates the performance of the model under data shifts. Although both aim to ensure reliable performance, prior work has revealed a...
Source: arXiv - Machine Learning | 18 hours ago
6. SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models
arXiv:2601.16231v1 Announce Type: cross Abstract: Multimodal foundation models that integrate audio, vision, and language achieve strong performance on reasoning and generation tasks, yet their robustness to adversarial manipulation remains poorly understood. We study a realistic and underexplor...
Source: arXiv - Machine Learning | 18 hours ago
7. UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
arXiv:2512.10492v2 Announce Type: replace Abstract: Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications in sequential decision-making domains such as autonomous driving...
Source: arXiv - Machine Learning | 18 hours ago
8. VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents
arXiv:2601.16238v1 Announce Type: cross Abstract: VIBETENSOR is an open-source research system software stack for deep learning, generated by LLM-powered coding agents under high-level human guidance. In this paper, "fully generated" refers to code provenance: implementation changes were produce...
Source: arXiv - Machine Learning | 18 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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