Here's your daily roundup of the most relevant AI and ML news for June 16, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Risk-Aware LLM Agents for Geospatial Data Retrieval: Design and Preliminary Adversarial Evaluation
arXiv:2606.15077v1 Announce Type: new Abstract: We present an LLM-driven framework for retrieving remote sensing data from cloud-based geospatial catalogues using natural language queries. The system converts user intent into structured API calls, enabling efficient access to satellite imagery a...
Source: arXiv - AI | 10 hours ago
2. GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
arXiv:2606.14865v1 Announce Type: new Abstract: Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters become optimizable can affect the final robust solution, even when th...
Source: arXiv - Machine Learning | 10 hours ago
3. Training-Free Adversarial Robustness in Computational MRI
arXiv:2501.01908v4 Announce Type: replace-cross Abstract: Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, res...
Source: arXiv - Machine Learning | 10 hours ago
4. Time-Varying Audio Effect Modeling by End-to-End Adversarial Training
arXiv:2512.15313v2 Announce Type: replace-cross Abstract: Deep learning has become a standard approach for the modeling of audio effects, yet strictly black-box modeling remains problematic for time-varying systems. Unlike time-invariant effects, training models on devices with internal modulati...
Source: arXiv - Machine Learning | 10 hours ago
5. Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
arXiv:2605.26595v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on fixed trigger phrases that defenses such as outlier detection, clean-data regularizatio...
Source: arXiv - Machine Learning | 10 hours ago
6. Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
arXiv:2606.15441v1 Announce Type: cross Abstract: Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet rec...
Source: arXiv - AI | 10 hours ago
7. MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks
arXiv:2602.09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capabilities, their d...
Source: arXiv - AI | 10 hours ago
8. David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM
arXiv:2606.15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. The PPM field has progressed from classical machine learning through d...
Source: arXiv - Machine Learning | 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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