Here's your daily roundup of the most relevant AI and ML news for May 08, 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. Phishing Arena – multi-agent LLM tournament to study adversarial email security
Article URL: https://github.com/Krabby24/phishing-arena Comments URL: https://news.ycombinator.com/item?id=48062364 Points: 1
Comments: 0
Source: Hacker News - ML Security | 1 hours ago
Research & Papers
2. Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
arXiv:2605.05534v1 Announce Type: new Abstract: Adversarial learning and the robustness of Graph Neural Networks (GNNs) are topics of widespread interest in the machine learning community, as documented by the number of adversarial attacks and defenses designed for these purposes. While a rigoro...
Source: arXiv - Machine Learning | 10 hours ago
3. 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
4. BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
arXiv:2605.06117v1 Announce Type: new Abstract: Large language models (LLMs) have recently been adapted to tabular prediction by serializing structured features into natural language, but their performance in low-data regimes remains limited compared to gradient-boosted decision trees (GBDTs). I...
Source: arXiv - Machine Learning | 10 hours ago
5. One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
arXiv:2605.06166v1 Announce Type: new Abstract: In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mechanisms. When a parameter mask and data subset jointly determine restr...
Source: arXiv - Machine Learning | 10 hours ago
6. FedAttr: Towards Privacy-preserving Client-Level Attribution in Federated LLM Fine-tuning
arXiv:2605.06596v1 Announce Type: cross Abstract: Watermark radioactivity testing type of methods can detect whether a model was trained on watermarked documents, and have become key tools for protecting data ownership in the fine-tuning of large language models (LLMs). Existing works have prove...
Source: arXiv - Machine Learning | 10 hours ago
7. How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
arXiv:2605.06605v1 Announce Type: new Abstract: Evaluating and predicting the performance of large language models (LLMs) in multi-turn conversational settings is critical yet computationally expensive; key events -- e.g., jailbreaks or successful task completion by an agent -- often emerge only...
Source: arXiv - Machine Learning | 10 hours ago
8. 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
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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