Here's your daily roundup of the most relevant AI and ML news for June 25, 2026. Today's digest includes 1 security-focused story. We're also covering 6 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. New Gaslight macOS Malware Uses Prompt Injection to Disrupt AI-Assisted Analysis
A previously undocumented Rust-based macOS implant and information stealer has been found to embed a prompt injection payload designed to trick a malware analyst's artificial intelligence (AI) tools and trick it into aborting or refusing an analysis of the artifact.
The malware has been codename...
Source: The Hacker News (Security) | 4 hours ago
HuggingFace & Models
2. Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel
Source: HuggingFace Blog | 22 hours ago
Research & Papers
3. Yuvion VL: A Multimodal Foundation Model for Adversarial Content and AI Safety
arXiv:2606.25034v1 Announce Type: cross Abstract: General-purpose models often struggle to reliably identify and understand real-world multimodal risks, largely due to the inherent multimodal adversarial nature of content and AI safety. We present Yuvion VL, a family of multimodal large language...
Source: arXiv - AI | 10 hours ago
4. How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
arXiv:2606.25487v1 Announce Type: cross Abstract: Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either a safety classifier trained for the task, or a general chat model prompted...
Source: arXiv - Machine Learning | 10 hours ago
5. Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation
arXiv:2606.25782v1 Announce Type: cross Abstract: With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effective while remaining low-cost and low-latency. Safety evaluation of LLM outputs has generally...
Source: arXiv - AI | 10 hours ago
6. Learning with Monotone Adversarial Corruptions
arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model. In this model, an adversary, upon looking at a "clean" i.i.d. dataset, in...
Source: arXiv - Machine Learning | 10 hours ago
7. FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming
arXiv:2606.19887v2 Announce Type: replace-cross Abstract: Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks. Financial LLMs face regulatory compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation. We int...
Source: arXiv - AI | 10 hours ago
8. A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients
arXiv:2606.24999v1 Announce Type: new Abstract: High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet solving them efficiently in a data-driven way remains challenging. E...
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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