Here's your daily roundup of the most relevant AI and ML news for June 01, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents
arXiv:2605.30677v1 Announce Type: cross Abstract: Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into the source code of executable binary files. This research demonstrates defensive tactics for detecting the presences of prompt injection strings i...
Source: arXiv - AI | 10 hours ago
2. From Prompt Injection to Persistent Control: Defending Agentic Harness Against Trojan Backdoors
arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and write files, call tools, and reuse workspace state across sessions. While such capabilities enhanc...
Source: arXiv - AI | 10 hours ago
3. The Long-Term Effects of Data Selection in LLM Fine-Tuning
arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with recent methods prioritizing samples by current utility, diversity, quality, or influence. This paper studies a different question: when fine-tuni...
Source: arXiv - Machine Learning | 10 hours ago
4. Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity
arXiv:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, and data access. Their tool observation loop creates a direct attack surface: an adversary who co...
Source: arXiv - AI | 10 hours ago
5. Prompt Injection as Role Confusion
arXiv:2603.12277v5 Announce Type: replace-cross Abstract: LLMs see the world as a single stream of text, partitioned into roles like or . We trace prompt injection to role confusion: models perceive the source of text from how it sounds, not its labeled role. A command hidden in a webpage hijac...
Source: arXiv - AI | 10 hours ago
6. Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
arXiv:2605.18024v2 Announce Type: replace-cross Abstract: Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior robust MARL methods have primarily considered value-oriented ...
Source: arXiv - AI | 10 hours ago
7. Latent Geometric Chords for Query-Efficient Decision-Based Adversarial Attacks
arXiv:2605.31219v1 Announce Type: cross Abstract: While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wise attacks frequently introduce unnatural, high-frequency visual artifacts, while latent-space...
Source: arXiv - Machine Learning | 10 hours ago
8. When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
arXiv:2605.26929v2 Announce Type: replace 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) op...
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.
Want to see how your favorite models score on security? Check our model dashboard for trust scores on the top 500 HuggingFace models.