·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Figma announces new features to define how agents work on its platform2h◆AVA-Encoder: Towards Agent-Native Video Representation Learning3h◆Non-asymptotic Convergence of Average-reward Q-learning with Options3h◆Deep kernel hedging3h◆Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis3h◆Learning Reusable Agent Subroutines from Experience3h◆AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions3h◆The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations3h◆A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data3h◆SecJev: Bringing Security Expertise to System One Decision Models3h◆Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes3h◆Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data3h◆Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders3h◆Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening3h◆Token Utility for On-Policy Distillation via Local Trust-Region Optimization3h◆PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization3h◆Data Density Scaling Laws for Image Self-Distillation3h◆Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction3h◆IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence3h◆How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts3h◆Figma announces new features to define how agents work on its platform2h◆AVA-Encoder: Towards Agent-Native Video Representation Learning3h◆Non-asymptotic Convergence of Average-reward Q-learning with Options3h◆Deep kernel hedging3h◆Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis3h◆Learning Reusable Agent Subroutines from Experience3h◆AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions3h◆The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations3h◆A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data3h◆SecJev: Bringing Security Expertise to System One Decision Models3h◆Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes3h◆Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data3h◆Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders3h◆Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening3h◆Token Utility for On-Policy Distillation via Local Trust-Region Optimization3h◆PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization3h◆Data Density Scaling Laws for Image Self-Distillation3h◆Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction3h◆IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence3h◆How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts3h◆
News/Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code
arxiv
PublishedSeptember 23, 2026 at 4:00 AM

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code

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

arXiv:2608.28021v2 Announce Type: replace-cross Abstract: Large language models increasingly author Infrastructure-as-Code (IaC), where one insecure default is provisioned straight into production. Prior evaluations report vulnerability counts for models only, and so cannot say whether models are wo

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

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

Your email is used only to send The Bubble Brief. Privacy policy

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
arxivCSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing3harxivFireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks3harxivNarrowing the Collaboration Gap, Probably3harxivRiding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine3h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

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

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews