·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Amazon’s data centers used 2.5 billion gallons of water last year2h◆Deezer’s new tool can identify AI music from Spotify, Apple Music, and others2h◆Pool’s new app turns your screenshots into something useful4h◆DoorDash’s new AI chatbot lets you order with prompts and photos5h◆Anthropic apologizes for invisible Claude Fable guardrails7h◆Google DeepMind is worried about what happens when millions of agents start to interact8h◆Deezer launches an AI music detector for other streaming services11h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing15h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning15h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!15h◆ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation15h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions15h◆The Impossibility of Eliciting Latent Knowledge15h◆Mapping Scientific Literature with Large Language Models and Topic Modeling15h◆Grounding Computer Use Agents on Human Demonstrations15h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models15h◆LSTM based IoT Device Identification15h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse15h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM15h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models15h◆Amazon’s data centers used 2.5 billion gallons of water last year2h◆Deezer’s new tool can identify AI music from Spotify, Apple Music, and others2h◆Pool’s new app turns your screenshots into something useful4h◆DoorDash’s new AI chatbot lets you order with prompts and photos5h◆Anthropic apologizes for invisible Claude Fable guardrails7h◆Google DeepMind is worried about what happens when millions of agents start to interact8h◆Deezer launches an AI music detector for other streaming services11h◆Opendoor’s India exit is fueling a bigger conversation about AI and outsourcing15h◆MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning15h◆Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!15h◆ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation15h◆Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions15h◆The Impossibility of Eliciting Latent Knowledge15h◆Mapping Scientific Literature with Large Language Models and Topic Modeling15h◆Grounding Computer Use Agents on Human Demonstrations15h◆Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models15h◆LSTM based IoT Device Identification15h◆StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse15h◆Breaking the Ice: Analyzing Cold Start Latency in vLLM15h◆Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models15h◆
News/Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security
arxiv
PublishedJune 11, 2026 at 4:00 AM

Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.11671v1 Announce Type: cross Abstract: Agent skills let LLM agents reuse instructions, resources, tools, and workflows, but they also create a new place for malicious behavior to hide. A skill may look benign in its documentation or code while becoming harmful only when it is invoked with

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivMODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning15harxivPosition: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!15harxivARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation15harxivGeneralizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions15h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews