·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Trump’s EPA wants to let data centers hide their air pollution40m◆Anthropic gets its first court win over the Pentagon’s supply-chain risk label4h◆Meta executive leaves for OpenAI as the social media giant faces growing scrutiny in India4h◆The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting13h◆Fine-Tuning of Transformer models with Frames13h◆Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection13h◆BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI13h◆Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search13h◆TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction13h◆Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification13h◆Not All Eval-Awareness Is Equal: Capabilities Framing Predicts Compliance13h◆Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation13h◆WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution13h◆Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales13h◆FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes13h◆Syntax vs. Semantics: How Transformers Learn Deep Dependencies13h◆Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes13h◆Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search13h◆Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning13h◆Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention13h◆Trump’s EPA wants to let data centers hide their air pollution40m◆Anthropic gets its first court win over the Pentagon’s supply-chain risk label4h◆Meta executive leaves for OpenAI as the social media giant faces growing scrutiny in India4h◆The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting13h◆Fine-Tuning of Transformer models with Frames13h◆Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection13h◆BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI13h◆Naive Prompt Optimization: Rethinking the Need for Complex Prompt Search13h◆TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction13h◆Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification13h◆Not All Eval-Awareness Is Equal: Capabilities Framing Predicts Compliance13h◆Mechanistic Reaction Prediction via Discrete Flow Matching on Graph-Structured Electron Occupation13h◆WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution13h◆Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales13h◆FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes13h◆Syntax vs. Semantics: How Transformers Learn Deep Dependencies13h◆Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes13h◆Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search13h◆Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning13h◆Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention13h◆
News/Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
arxiv
PublishedMay 19, 2026 at 4:00 AM

Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models

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

arXiv:2605.17310v1 Announce Type: cross Abstract: Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the same perturbed input is paired with different textual queries. This pape

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
arxivThe Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting13harxivFine-Tuning of Transformer models with Frames13harxivFeature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detection13harxivBPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI13h
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