·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI agents are flooding public services with new requests31m◆Maven Robotics wants to steal your robot deployment deal1h◆Why the current tech backlash feels different1h◆Mathematicians want proof OpenAI didn’t use their work4h◆Powering AI is an architecture problem4h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty11h◆Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models11h◆SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection11h◆WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic11h◆PAN: A World Model for General, Actionable, and Long-Horizon World Simulation11h◆FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures11h◆Generating Pretraining Tokens from Organic Data for Data-Bound Scaling11h◆Streaming LRAT Certificates into Lean Theorems11h◆DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering11h◆Deep and shallow biases in language models11h◆The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs11h◆Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients11h◆Constrained Online Learning with Noisy Constraint Values11h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification11h◆AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories11h◆AI agents are flooding public services with new requests31m◆Maven Robotics wants to steal your robot deployment deal1h◆Why the current tech backlash feels different1h◆Mathematicians want proof OpenAI didn’t use their work4h◆Powering AI is an architecture problem4h◆Planning and Scheduling Business Processes under Control-Flow Uncertainty11h◆Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models11h◆SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection11h◆WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic11h◆PAN: A World Model for General, Actionable, and Long-Horizon World Simulation11h◆FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures11h◆Generating Pretraining Tokens from Organic Data for Data-Bound Scaling11h◆Streaming LRAT Certificates into Lean Theorems11h◆DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering11h◆Deep and shallow biases in language models11h◆The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs11h◆Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradients11h◆Constrained Online Learning with Noisy Constraint Values11h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification11h◆AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories11h◆
News/PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections

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

arXiv:2606.12737v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and environments, introducing new security risks such as indirect prompt injection attacks through untrusted external sources. Existing defenses

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 →
Tags
04
#security#vulnerability#ai-safety#red-teaming

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#security#vulnerability#ai-safety#red-teaming

Related coverage

More from ARXIV
arxivPlanning and Scheduling Business Processes under Control-Flow Uncertainty11harxivDiffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models11harxivSIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detection11harxivWAPP: Safe Learning of Positive Security WAF Policies from Live Traffic11h
The Bubble Brief
WEEKLY

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

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

Originally published on arxiv ↗
HomeModelsNews