Here's your daily roundup of the most relevant AI and ML news for June 26, 2026. Today's digest includes 2 security-focused stories. 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) | 1 day ago
2. Miasma Malware Targets npm Packages and GitHub Actions in Supply Chain Attack
Cybersecurity researchers have flagged yet another evolution of the supply chain attack linked to the Mini Shai-Hulud, Miasma, and Hades malware family that has compromised a new set of npm packages, even as it has propagated to the Go ecosystem.
"The latest activity includes malicious npm relea...
Source: The Hacker News (Security) | 2 hours ago
Research & Papers
3. How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
arXiv:2606.25487v2 Announce Type: replace-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 ...
Source: arXiv - Machine Learning | 10 hours ago
4. Adaptive Evaluation of Out-of-Band Defenses Against Prompt Injection in LLM Agents
arXiv:2606.26479v1 Announce Type: cross Abstract: Recent work (2024 to 2026) has converged on a strategy for defending tool-using LLM agents against indirect prompt injection: rather than training the model to refuse malicious instructions, enforce security outside the model with a deterministic...
Source: arXiv - Machine Learning | 10 hours ago
5. Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text
arXiv:2606.27215v1 Announce Type: new Abstract: Deep learning models have achieved impressive performance across various fields but remain vulnerable to adversarial inputs, particularly in NLP, where such attacks can have significant real-world consequences. Adversarial attacks often involve sma...
Source: arXiv - AI | 10 hours ago
6. Prompt Injection in Automated R\'esum\'e Screening with Large Language Models: Single and Multi-Injection Settings
arXiv:2606.27287v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems. We study prompt injection in automated r\'esum\'e screening, defined as...
Source: arXiv - AI | 10 hours ago
7. Neural Architecture Search for Generative Adversarial Networks: A Comprehensive Review and Critical Analysis
arXiv:2606.26169v1 Announce Type: new Abstract: Neural Architecture Search (NAS) has emerged as a pivotal technique in optimizing the design of Generative Adversarial Networks (GANs), automating the search for effective architectures while addressing the challenges inherent in manual design. Thi...
Source: arXiv - Machine Learning | 10 hours ago
8. The Role of Input Dimensionality in the Emergence and Targeted Control of Adversarial Examples
arXiv:2606.26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry. However, the assumptions underlying these works are rarely examined empirically, and systematic...
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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