Here's your daily roundup of the most relevant AI and ML news for June 02, 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. Miasma Supply Chain Attack Compromises Red Hat npm Packages with Credential-Stealing Worm
A new Mini Shai-Hulud supply chain attack campaign, codenamed Miasma, has compromised @redhat-cloud-services packages to steal credentials and secrets from developer machines and deliver a self-propagating worm.
"This is effectively a Mini Shai-Hulud campaign: it uses the same core tactics of in...
Source: The Hacker News (Security) | 20 hours ago
Research & Papers
2. Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
arXiv:2603.24511v2 Announce Type: replace Abstract: We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injection evaluations. We deploy frontier agents, such as Claude Code a...
Source: arXiv - Machine Learning | 10 hours ago
3. REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
arXiv:2605.12813v2 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, making it important to systematically evaluate their reliability under realistic adversarial inputs. We formulate hallucina...
Source: arXiv - Machine Learning | 10 hours ago
4. Persona Attack: Incremental Memory Injection Jailbreak Attack against Large Language Models
arXiv:2606.00150v1 Announce Type: cross Abstract: As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety training. Traditional jailbreak techniques typically focus on a single prompt injection, neglectin...
Source: arXiv - AI | 10 hours ago
5. Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults
arXiv:2606.00914v1 Announce Type: new Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queues, yet safety evaluations almost always test the model or the user prompt in isolation, never t...
Source: arXiv - AI | 10 hours ago
6. A unifying Bayesian framework for adversarial robustness
arXiv:2510.09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such as adversarial training, typically robustify models by minimizing a worst-case loss. These dete...
Source: arXiv - Machine Learning | 10 hours ago
7. Calibrating Uncertainty for Zero-Shot Adversarial CLIP
arXiv:2512.12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work primarily matches predicted logits between clean and adversarial examples, which overlooks uncertainty ...
Source: arXiv - Machine Learning | 10 hours ago
Tech & Development
8. Fine-tuning an LLM to write docs like it's 1995
Article URL: https://passo.uno/fine-tuning-docs-llm/ Comments URL: https://news.ycombinator.com/item?id=48369722 Points: 1
Comments: 0
Source: Hacker News - AI | 1 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.
Want to see how your favorite models score on security? Check our model dashboard for trust scores on the top 500 HuggingFace models.