Here's your daily roundup of the most relevant AI and ML news for May 07, 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. DAEMON Tools Supply Chain Attack Compromises Official Installers with Malware
A newly identified supply chain attack targeting DAEMON Tools software has compromised its installers to serve a malicious payload, according to findings from Kaspersky. "These installers are distributed from the legitimate website of DAEMON Tools and are signed with digital certificates belongin...
Source: The Hacker News (Security) | 1 day ago
Research & Papers
2. SoK: Robustness in Large Language Models against Jailbreak Attacks
arXiv:2605.05058v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success but remain highly susceptible to jailbreak attacks, in which adversarial prompts coerce models into generating harmful, unethical, or policy-violating outputs. Such attacks pose real-w...
Source: arXiv - AI | 10 hours ago
3. From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
arXiv:2605.03686v2 Announce Type: replace Abstract: Automated Machine Learning (AutoML) frameworks increasingly leverage Large Language Models (LLMs) for tasks such as hyperparameter optimization and neural architecture code generation. However, current LLM-based approaches focus on generative o...
Source: arXiv - Machine Learning | 10 hours ago
4. Dissociating spatial frequency reliance from adversarial robustness advantages in neurally guided deep convolutional neural networks
arXiv:2605.04443v1 Announce Type: cross Abstract: Deep convolutional neural networks (DCNNs) have rivaled humans on many visual tasks, yet they remain vulnerable to near-imperceptible perturbations generated by adversarial attacks. Recent work shows that aligning DCNN representations with human ...
Source: arXiv - AI | 10 hours ago
5. Laundering AI Authority with Adversarial Examples
arXiv:2605.04261v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and moderating content. Users implicitly trust that these systems perceive the same visual content as they...
Source: arXiv - Machine Learning | 10 hours ago
6. What Can Be Recovered Under Sparse Adversarial Corruption? Assumption-Free Theory for Linear Measurements
arXiv:2510.24215v4 Announce Type: replace-cross Abstract: Recovery from linear measurements under sparse adversarial corruption is typically formulated as an exact-recovery problem: one seeks structural conditions on $A$ (e.g., the restricted isometry property) that guarantee unique recovery of ...
Source: arXiv - Machine Learning | 10 hours ago
7. From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
arXiv:2605.04572v1 Announce Type: new Abstract: Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of preference examples. Existing studies attempt to explain this phenomenon b...
Source: arXiv - AI | 10 hours ago
8. Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
arXiv:2605.04431v1 Announce Type: cross Abstract: Reinforcement fine-tuning (RFT) has become a core paradigm for post-training large language models, yet its training process remains highly fragile. Existing efforts mainly improve reliability at the system level or address specific issues in ind...
Source: arXiv - AI | 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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