Search: “spring-framework”

τ^τ-Bench: An Environment for End-To-End, Realistic Agent Construction

New τ^τ-bench tasks coding agents with building deployable customer-service agents; best config, Claude Opus 5, passes only 23.9% of simulations.

Researchers introduce τ^τ-bench, an end-to-end benchmark where a developer agent must build a complete customer-service agent from real business records, a client with requirements, a production API, an inherited codebase, and cost/model limits, then is scored by deploying it against held-out simulated users. Across 53 tasks in four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations versus an 82.2% expert-authored reference ceiling. Failure modes mirror those of human developers: shallow queries instead of deep record comprehension, almost no client communication, and shipping the first architecture that runs rather than experimenting.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 19d agoModel release

automatic module_metadata_base.json update

Metasploit Framework automatically updates its module metadata JSON in routine maintenance commit.

The Metasploit Framework repository received an automatic update to module_metadata_base.json, the metadata file that tracks module information. No specific new vulnerability, exploit module, or feature is described in the commit message. This is routine repository maintenance rather than a notable security event.

Metasploit Framework commits·Sep 3, 16:19 UTC · 13d agoTools

automatic module_metadata_base.json update

Routine automated Metasploit Framework commit updates module_metadata_base.json with new module metadata.

The Metasploit Framework repository received an automated commit updating module_metadata_base.json, the file that tracks module metadata for the framework. This is routine maintenance accompanying new or updated exploit modules rather than a standalone disclosure. No vulnerability details, CVEs, or exploitation evidence are included.

Metasploit Framework commits·Sep 2, 23:00 UTC · 13d agoTools

automatic module_metadata_base.json update

Metasploit Framework pushed an automated update to its module_metadata_base.json module metadata file.

This repository commit is an automated update to the Metasploit Framework's module_metadata_base.json file. No new modules, vulnerabilities or exploit changes are described in the commit message.

Metasploit Framework commits·Sep 2, 14:40 UTC · 14d agoTools

automatic module_metadata_base.json update

Routine automated Metasploit Framework commit updating module metadata, with no disclosed vulnerability or exploitation activity.

The Metasploit Framework repository received an automatic update to its module_metadata_base.json file. The commit text contains no vulnerability details, CVE references, or new exploit modules. This is routine maintenance activity on the open-source penetration testing framework.

Metasploit Framework commits·Sep 1, 21:40 UTC · 14d agoTools

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

Researchers introduce CUA-Universe, a pipeline turning real desktop software into hybrid GUI+CLI agent environments, lifting a 9B model's OSWorld success rate.

CUA-Universe is an environment-to-data pipeline that converts real desktop applications into hybrid GUI+CLI environments, scaling to 16 applications via App-Forge, Task-Weave, and Path-Steer. Training on its harvested trajectories shifted a 9B model toward effective GUI+CLI orchestration, yielding +39.3 points on CUA-Verse, +16.8 points success rate on OSWorld, and +7.84 points on OSWorld-MCP while cutting steps and tokens by up to 57% and 60%. The work addresses the scarcity of scalable hybrid environments for computer-use agents.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:26 UTC · 12d agoAI research1

automatic module_metadata_base.json update

Metasploit Framework's automated pipeline refreshed its module metadata file, a routine repository maintenance commit introducing no new modules or exploits.

An automated Metasploit Framework commit updated module_metadata_base.json, the metadata database consumed by module tooling. The change is routine maintenance and contains no new exploit modules or vulnerability content.

Metasploit Framework commits·Sep 3, 13:45 UTC · 13d agoTools1

automatic module_metadata_base.json update

Automated Metasploit Framework commit refreshes module_metadata_base.json with no new exploit content or vulnerability details.

An automated commit updated module_metadata_base.json in the Rapid7 Metasploit Framework repository. The change reflects routine metadata maintenance and introduces no new modules, exploits, or vulnerability information.

Metasploit Framework commits·Sep 1, 18:53 UTC · 15d agoTools

automatic module_metadata_base.json update

Automated Metasploit Framework commit updating module_metadata_base.json to reflect newly added or modified exploit modules.

An automated commit updated module_metadata_base.json in the rapid7/metasploit-framework repository. The routine maintenance change reflects newly added or modified Metasploit modules and carries no standalone security significance.

Metasploit Framework commits·Sep 3, 17:06 UTC · 13d agoTools1

27.5KB language-agnostic WebGPU syntax highlighter

A developer released gpu-lexer, a 27.5KB language-agnostic syntax highlighter that uses a tiny WebGPU model to label code tokens in the browser.

gpu-lexer splits source into words, whitespace, and symbols, then a small WebGPU model uses local and whole-file context to assign nine token classes, working on languages never seen in training. On held-out files, 12.57% of token labels differ from Shiki, though this measures agreement with Shiki rather than objective correctness. In benchmarks against Shiki 4.4.3, Prism.js, Highlight.js, Sugar High, and Starry Night, it highlighted 10 concatenated copies of three.min.js (5.56M characters) about 10x faster on an Apple M4 Pro in Chrome 152. The author frames it as an experiment, not a grammar-equivalent highlighter.

Hacker News · security·Sep 9, 01:14 UTC · 7d agoAI tools & infraHN 21↑ · 5 comments1

Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

Payara 7.2026.1.RC1 Arbitrary EJB Method Invocation via Insecure Reflection in Payara Server

Payara Server 7.2026.1.RC1 HTTP EJB endpoints rely on attacker-controlled reflection and JNDI lookups without authorization, enabling arbitrary EJB method invocation.

Payara Server exposes multiple HTTP-accessible EJB invocation mechanisms that depend on attacker-controlled reflection, dynamic class loading, and unsafe deserialization. Remote clients can perform arbitrary JNDI lookups, resolve attacker-supplied class names, and invoke EJB business methods without sufficient authorization enforcement or input restriction. Both the deprecated InvokeEJBServlet and other endpoints are affected in version 7.2026.1.RC1.

Full Disclosure·Sep 4, 00:13 UTC · 12d agoVulnerability 2 sources

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoModel release2

Metabase Zero-Day Exploited in Wild Allows Admin Access Without Authentication

Metabase warns an actively exploited zero-day (CVE-2026-72898, CVSS 10.0) grants unauthenticated admin access; Framework, n8n, and Kilo Code disclosed data exposure.

Metabase disclosed a maximum-severity SQL injection affecting versions x.58 through x.63 that lets unauthenticated attackers inject SQL via the /api/session/reset_password endpoint and gain administrator access to Metabase instances. Metabase Cloud was attacked, patches were released, and CISA added CVE-2026-72898 to the KEV catalog with a federal remediation deadline of August 14, 2026. Downstream victims include Framework (customer names, IPs, addresses, phone numbers, and emails accessed), n8n (136 customer records, five with bcrypt-hashed passwords), and Kilo Code (Slack access tokens). Wiz estimates roughly 13% of cloud environments run self-hosted Metabase, with about 2,500 instances internet-accessible.

The Hacker News·Aug 12, 13:47 UTC · Aug 12, 2026Exploit / PoC in the wildCVE-2026-72898CVE-2023-38646

Causal Foundation Models

A paper introduces causal foundation models (CFMs): pretrained networks that estimate treatment effects on new datasets via in-context learning without fine-tuning.

Causal foundation models (CFMs) apply the foundation-model paradigm to causal inference, replacing bespoke per-problem estimator pipelines with networks pretrained once at scale. CFMs estimate causal quantities such as the average treatment effect on entirely new datasets through in-context learning, without model updates. The work serves as a practical introduction to the emerging area, covering background in causal inference and machine learning and including example code and Jupyter notebooks.

Hugging Face daily papers·Sep 1, 20:00 UTC · 15d agoAI research

Metabase zero-day exploited to access Framework customer data

Help Net Security·Aug 10, 00:00 UTC · Aug 10, 2026Exploit / PoC

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

Empirical study finds direct whole-file generation beats iterative diff-based editing for Flutter/Dart code models on about 1,790 held-out tasks.

Researchers trained Rainbow-Pony-100M from scratch and fine-tuned Qwen2.5-Coder-0.5B in both direct-generation and diff-based regimes, then evaluated four resulting models on roughly 1,790 Flutter/Dart tasks. Direct generation outperformed diff-based generation on compilation pass rate, bits-per-byte, character-level similarity, and blinded LLM-judge ratings. Diff-based editing is competitive only on short, localized edits in refactoring and error-handling tasks, a property the authors call task locality.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI research1

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research1

Introducing context-aware vulnerability discovery and remediation with Cloudflare Managed Defense and OpenAI Daybreak models

Cloudflare launches invitation-only Vulnerability Discovery and Remediation within Managed Defense, using OpenAI Daybreak models and WAF context to prioritize and patch vulnerabilities.

Cloudflare announced early access to Vulnerability Discovery and Remediation, an invitation-only service within Cloudflare Managed Defense. The service uses OpenAI Daybreak models, including GPT-5.6 Cyber, via the Daybreak Defense Network to hunt and validate vulnerabilities in customer-authorized codebases across Workers and proxied applications. Findings are prioritized using production traffic, WAF rule, and security event context, and proposed patches and WAF mitigations are automatically checked before customer review.

Cloudflare Blog·Sep 3, 21:03 UTC · 12d agoTools

StyleSmuggler: Unpatched Magento and Adobe Commerce Zero-Day Exploited

Attackers are actively exploiting an unpatched zero-day dubbed StyleSmuggler in Magento Open Source and Adobe Commerce for unauthenticated remote code execution.

A zero-day vulnerability tracked as StyleSmuggler affects Magento Open Source and Adobe Commerce and permits unauthenticated remote code execution. The flaw is unpatched and exploitation is ongoing in the wild. E-commerce stores running these platforms are at direct risk of compromise until a fix is released.

SOCRadar·Sep 7, 11:31 UTC · 9d agoExploit / PoC in the wild

[webapps] Langflow 1.8.4 - Path Traversal to Remote Code Execution

A path traversal to remote code execution exploit for Langflow 1.8.4, a popular LLM application builder, was published on Exploit-DB.

Exploit-DB lists a proof-of-concept exploit chaining path traversal to remote code execution in Langflow 1.8.4, an open-source tool used to build LLM applications and agents. The chain allows an attacker to write arbitrary files outside the intended directory and achieve code execution on the host. The provided text does not include a CVE identifier or reports of exploitation in the wild, but RCE in a widely deployed AI tooling product is notable for defenders.

Exploit-DB·Aug 31, 00:00 UTC · 16d agoExploit / PoC1

Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:30 UTC · 7d agoAI research1

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:59 UTC · 7d agoAI research2

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoModel release

[webapps] PodcastGenerator 3.2.9 - Stored XSS

A stored cross-site scripting flaw in PodcastGenerator 3.2.9 is documented with a public proof-of-concept exploit on Exploit-DB.

Exploit-DB published exploit ID 52677 targeting PodcastGenerator 3.2.9, a web application affected by stored cross-site scripting. The listing provides a proof-of-concept for the flaw but includes no CVE identifier or reports of active exploitation.

Exploit-DB·Sep 2, 00:00 UTC · 14d agoExploit / PoC1

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research

Re: AI slops from Eve

Jeroen Roovers links a header-less LLM-generated advisory to a similar fake llama.cpp GGUF parser advisory from May 2026.

In the 'AI slops from Eve' oss-security thread, Jeroen Roovers asks whether a current LLM-generated message without a Date header comes from the same source as a May 15, 2026 posting titled 'Security Advisory: Multiple Vulnerabilities in llama.cpp GGUF Format Parsers'. Both fake advisories share the missing Date header trait. The exchange highlights growing LLM-generated noise on security mailing lists.

oss-security·updated Sep 13, 15:08 UTC · 3d ago·first Sep 13, 14:51 UTC · 3d agoIndustry 12 sources