Here's your daily roundup of the most relevant AI and ML news for July 07, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
arXiv:2607.03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded genera...
Source: arXiv - Machine Learning | 10 hours ago
2. Binary Iterative Method for Non-targeted Adversarial Attack
arXiv:2607.04145v1 Announce Type: new Abstract: Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the 'piecewise linearity' of deep learning based models. Since adversarial attacks and adversari...
Source: arXiv - Machine Learning | 10 hours ago
3. UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks
arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversarial attacks in realistic conditions. However, these simulations are non-differentiable, forcing...
Source: arXiv - Machine Learning | 10 hours ago
4. DualView: Preventing Indirect Prompt Injection in Personal AI Agents
arXiv:2607.03821v1 Announce Type: cross Abstract: Personal AI agents that run on the user's local machine, such as OpenClaw, automate daily tasks including web search, email, and file management. Their access to computer resources, including the network, file system, and shell, exposes them to i...
Source: arXiv - AI | 10 hours ago
5. !Imperio, smolVLA: The Implications of Data Poisoning on Open Source Robotics
arXiv:2607.04146v1 Announce Type: cross Abstract: This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions. A few poisoned samples can sile...
Source: arXiv - Machine Learning | 10 hours ago
6. Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
arXiv:2605.07961v2 Announce Type: replace Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distributed agents to jointly refine a shared pretrained LLM by aggre...
Source: arXiv - Machine Learning | 10 hours ago
7. kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail
arXiv:2607.02072v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing guardrails predominantly rely on fine-tuning to build classifiers, which often suffer from low ...
Source: arXiv - Machine Learning | 10 hours ago
8. How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
arXiv:2605.06605v2 Announce Type: replace 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 ...
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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