Here's your daily roundup of the most relevant AI and ML news for January 29, 2026. We're also covering 7 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Benchmarking LLAMA Model Security Against OWASP Top 10 For LLM Applications
arXiv:2601.19970v1 Announce Type: cross Abstract: As large language models (LLMs) move from research prototypes to enterprise systems, their security vulnerabilities pose serious risks to data privacy and system integrity. This study benchmarks various Llama model variants against the OWASP Top ...
Source: arXiv - Machine Learning | 18 hours ago
2. Mask-GCG: Are All Tokens in Adversarial Suffixes Necessary for Jailbreak Attacks?
arXiv:2509.06350v2 Announce Type: replace-cross Abstract: Jailbreak attacks on Large Language Models (LLMs) have demonstrated various successful methods whereby attackers manipulate models into generating harmful responses that they are designed to avoid. Among these, Greedy Coordinate Gradient ...
Source: arXiv - AI | 18 hours ago
3. Abex-rat: Synergizing Abstractive Augmentation and Adversarial Training for Classification of Occupational Accident Reports
arXiv:2509.02072v4 Announce Type: replace Abstract: The automatic classification of occupational accident reports is pivotal for workplace safety analysis but is persistently hindered by severe class imbalance and data scarcity. In this paper, we propose ABEX-RAT, a resource-efficient framework ...
Source: arXiv - Machine Learning | 18 hours ago
4. One Word is Enough: Minimal Adversarial Perturbations for Neural Text Ranking
arXiv:2601.20283v1 Announce Type: cross Abstract: Neural ranking models (NRMs) achieve strong retrieval effectiveness, yet prior work has shown they are vulnerable to adversarial perturbations. We revisit this robustness question with a minimal, query-aware attack that promotes a target document...
Source: arXiv - Machine Learning | 18 hours ago
5. Feature-Space Adversarial Robustness Certification for Multimodal Large Language Models
arXiv:2601.16200v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) exhibit strong capabilities across diverse applications, yet remain vulnerable to adversarial perturbations that distort their feature representations and induce erroneous predictions. To address this vu...
Source: arXiv - Machine Learning | 18 hours ago
6. AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning
arXiv:2601.17261v2 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under strict memory constraints, as it avoids the prohibitive memory cost of storing activations for backpropagation. However, existing ZO methods typically...
Source: arXiv - Machine Learning | 18 hours ago
7. LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
arXiv:2411.08862v2 Announce Type: replace Abstract: We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access...
Source: arXiv - Machine Learning | 18 hours ago
Industry News
8. Tiny startup Arcee AI built a 400B-parameter open source LLM from scratch to best Meta’s Llama
30-person startup Arcee AI has released a 400B model called Trinity, which it says is one of the biggest open source foundation models from a U.S. company.
Source: TechCrunch - AI | 1 day 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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