Here's your daily roundup of the most relevant AI and ML news for May 27, 2026. Today's digest includes 1 security-focused story. We're also covering 7 research developments. Click through to read the full articles from our curated sources.
Security & Safety
1. GlassWorm Malware Takedown Disrupts Developer Supply Chain Attack Infrastructure
CrowdStrike, in partnership with Google and the Shadowserver Foundation, has announced the simultaneous disruption of all command-and-control (C2) channels associated with GlassWorm, a persistent software chain campaign targeting software developers through malicious packages and extensions.
"Si...
Source: The Hacker News (Security) | 2 hours ago
Research & Papers
2. Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
arXiv:2605.26526v1 Announce Type: new Abstract: Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an assumption that new harmful behavior is learned through fine-tuning rather than elicited by jailbre...
Source: arXiv - Machine Learning | 10 hours ago
3. Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry
arXiv:2604.27019v3 Announce Type: replace Abstract: Safety-aligned language models must refuse harmful requests without broad over-refusal, but it remains unclear how dynamic adversarial fine-tuning changes refusal-control carriers: Kullback--Leibler (KL)-constrained directions or small subspace...
Source: arXiv - Machine Learning | 10 hours ago
4. EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation
arXiv:2509.04310v4 Announce Type: replace Abstract: Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in \textit{complex}, \textit{multi-turn} negotiations, opening new avenues for agentic AI. However, existing LLM agents ...
Source: arXiv - AI | 10 hours ago
5. Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
arXiv:2605.26595v1 Announce Type: 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 regularization, or on...
Source: arXiv - Machine Learning | 10 hours ago
6. Adversarial Water-Filling: Theory, Algorithms and Foundation Model
arXiv:2605.26163v1 Announce Type: cross Abstract: Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case interference. This formulation naturally arises in multi-operator low Earth orbit (LEO) satellite spe...
Source: arXiv - Machine Learning | 10 hours ago
7. Near-Optimal Regret in Adversarial Kernel Bandits
arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbert space (RKHS). We propose an exponential-weights algorithm built on a regularized importance-w...
Source: arXiv - Machine Learning | 10 hours ago
8. When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
arXiv:2605.26929v1 Announce Type: new Abstract: Adversarial training (AT) remains one of the most reliable empirical defenses against adversarial attacks. Its robustness critically depends on how the underlying min-max objective is optimized. In practice, Stochastic Gradient Descent (SGD) optimi...
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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