Here's your daily roundup of the most relevant AI and ML news for March 19, 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. How Ceros Gives Security Teams Visibility and Control in Claude Code
Security teams have spent years building identity and access controls for human users and service accounts. But a new category of actor has quietly entered most enterprise environments, and it operates entirely outside those controls. Claude Code, Anthropic's AI coding agent, is now running acros...
Source: The Hacker News (Security) | 3 hours ago
Research & Papers
2. Generative Adversarial Networks for Resource State Generation
arXiv:2601.13708v2 Announce Type: replace-cross Abstract: We introduce a physics-informed Generative Adversarial Network framework that recasts quantum resource-state generation as an inverse-design task. By embedding task-specific utility functions into training, the model learns to generate va...
Source: arXiv - Machine Learning | 10 hours ago
3. An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
arXiv:2603.16428v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs. To address this challenge and democratize LLM fine-tuning, we present SlideFormer, a nov...
Source: arXiv - AI | 10 hours ago
4. Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
arXiv:2504.00638v3 Announce Type: replace Abstract: The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the training process, and the deployment environment. In recent years,...
Source: arXiv - Machine Learning | 10 hours ago
5. Efficient LLM Safety Evaluation through Multi-Agent Debate
arXiv:2511.06396v3 Announce Type: replace Abstract: Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether structured multi-agent debate can improve judge reliability while keep...
Source: arXiv - AI | 10 hours ago
6. rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
arXiv:2603.17628v1 Announce Type: cross Abstract: Neural networks are central to modern artificial intelligence, yet their training remains highly sensitive to data contamination. Standard neural classifiers are trained by minimizing the categorical cross-entropy loss, corresponding to maximum l...
Source: arXiv - Machine Learning | 10 hours ago
7. How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
arXiv:2603.15714v1 Announce Type: cross Abstract: LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure to indirect prompt injection attacks, where adversarial instructio...
Source: arXiv - AI | 10 hours ago
8. Amnesia: Adversarial Semantic Layer Specific Activation Steering in Large Language Models
arXiv:2603.10080v2 Announce Type: replace-cross Abstract: Warning: This article includes red-teaming experiments, which contain examples of compromised LLM responses that may be offensive or upsetting. Large Language Models (LLMs) have the potential to create harmful content, such as generatin...
Source: arXiv - AI | 10 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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