Here's your daily roundup of the most relevant AI and ML news for June 15, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
arXiv:2606.14517v1 Announce Type: cross Abstract: LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents. However, we reveal that the very reasoning and task-following capabilities enabling this protection introduce a n...
Source: arXiv - AI | 10 hours ago
2. Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review
arXiv:2112.04573v2 Announce Type: replace-cross Abstract: As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of thi...
Source: arXiv - Machine Learning | 10 hours ago
3. Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning
arXiv:2605.24609v2 Announce Type: replace-cross Abstract: Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly d...
Source: arXiv - AI | 10 hours ago
4. MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
arXiv:2406.09250v5 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To address this vulnerability, we propose MirrorCheck, a rob...
Source: arXiv - Machine Learning | 10 hours ago
5. Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches
arXiv:2606.13626v2 Announce Type: replace-cross Abstract: We study generative modeling of Bach-style symbolic piano music using a shared MIDI corpus and three model families: autoregressive LSTMs with attention, latent-variable models including recurrent VAEs and vector-quantized VAEs, and gener...
Source: arXiv - Machine Learning | 10 hours ago
6. When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
arXiv:2606.14476v1 Announce Type: cross Abstract: A growing line of work equips large language model (LLM) agents with graph neural networks (GNNs) as callable tools, assuming the agent exercises judgment over when and how much to rely on such a tool. We test this directly. We expose a frozen GN...
Source: arXiv - Machine Learning | 10 hours ago
7. Trust but Verify: Mitigating Medical Hallucinations via Post-Hoc Adversarial Auditing and Multi-Agent Feedback Loops
arXiv:2606.14149v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions are involved. This study examine whether LLMs recommend recently banned or withdrawn pharmaceutical...
Source: arXiv - Machine Learning | 10 hours ago
8. Adversarial Concept Search: Predicting Compositional Errors From Feature Geometry
arXiv:2606.13934v1 Announce Type: new Abstract: Humans cannot always intuit what scenarios are most challenging to LLMs. Hoping to capture challenging edge cases, developers either design problems to be difficult for humans or curate extensive benchmarks. What if we could instead anticipate whic...
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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