Here's your daily roundup of the most relevant AI and ML news for March 05, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Image-based Prompt Injection: Hijacking Multimodal LLMs through Visually Embedded Adversarial Instructions
arXiv:2603.03637v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) integrate vision and text to power applications, but this integration introduces new vulnerabilities. We study Image-based Prompt Injection (IPI), a black-box attack in which adversarial instructions are e...
Source: arXiv - AI | 9 hours ago
2. Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
arXiv:2506.05937v2 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes. Evidential Deep Learning, an efficient paradigm for uncertainty ...
Source: arXiv - AI | 9 hours ago
3. Solving adversarial examples requires solving exponential misalignment
arXiv:2603.03507v1 Announce Type: new Abstract: Adversarial attacks - input perturbations imperceptible to humans that fool neural networks - remain both a persistent failure mode in machine learning, and a phenomenon with mysterious origins. To shed light, we define and analyze a network's perc...
Source: arXiv - Machine Learning | 9 hours ago
4. Goal-Driven Risk Assessment for LLM-Powered Systems: A Healthcare Case Study
arXiv:2603.03633v1 Announce Type: cross Abstract: While incorporating LLMs into systems offers significant benefits in critical application areas such as healthcare, new security challenges emerge due to the potential cyber kill chain cycles that combine adversarial model, prompt injection and c...
Source: arXiv - AI | 9 hours ago
5. Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models
arXiv:2602.22227v3 Announce Type: replace-cross Abstract: Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) exhibit perceptual fragility when confronted with visually complex scenes. This weakness stems from a reliance on finite training datasets, which are prohibit...
Source: arXiv - AI | 9 hours ago
6. On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generators
arXiv:2508.09844v2 Announce Type: replace-cross Abstract: We investigate the capabilities of Quantum Generative Adversarial Networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical test...
Source: arXiv - Machine Learning | 9 hours ago
7. A Multi-Dimensional Quality Scoring Framework for Decentralized LLM Inference with Proof of Quality
arXiv:2603.04028v1 Announce Type: cross Abstract: Decentralized large language model (LLM) inference networks can pool heterogeneous compute to scale serving, but they require lightweight and incentive-compatible mechanisms to assess output quality. Prior work introduced cost-aware Proof of Qual...
Source: arXiv - AI | 9 hours ago
8. Safety Guardrails for LLM-Enabled Robots
arXiv:2503.07885v2 Announce Type: replace-cross Abstract: Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversaria...
Source: arXiv - AI | 9 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.