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Xen Security Advisory 510 v3 (CVE-2026-79602) - x86: improper handling of HVM emulation return codes

Xen Project released XSA-510 (CVE-2026-79602) fixing mishandled HVM emulation return codes that let PCI-passthrough guests crash Xen.

Xen Security Advisory 510 v3 publicly discloses CVE-2026-79602, improper handling of HVM emulation return codes in the Xen hypervisor on x86. A guest with an assigned PCI device that has at least one BAR in the IO port space can trigger a BUG() in Xen. The advisory was released publicly as version 3.

oss-security · 8d agoVulnerability 4 sourcesCVE-2026-796021

Xen Security Advisory 509 v3 (CVE-2026-62437) - x86: DMs may cause mem leak by IRQ binding

Xen Project released XSA-509 (CVE-2026-62437) fixing a memory leak in IRQ tracking when guests with assigned PCI devices are terminated.

Xen Security Advisory 509 v3 publicly discloses CVE-2026-62437, a memory leak affecting the Xen hypervisor on x86. When guests are terminated, cleanup of PCI devices assigned to those guests and removal of associated IRQ tracking structures may fail, leaking memory. The advisory was released publicly as version 3.

oss-security · 8d agoVulnerabilityCVE-2026-62437

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 · 19d agoModel release

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers · 6d agoAI research

X says attackers are targeting user accounts after the launch of X Money

X is investigating a wave of unsolicited password reset emails targeting users after the X Money payments launch, with no confirmed breaches yet.

Numerous X users reported unsolicited password reset emails following the launch of X Money, the platform's new payments service with accounts held at FDIC-insured Cross River Bank. Product engineer Mridul Singhai said the company found no evidence of successful breaches or mass account takeovers, while the Grok chatbot confirmed attackers are mass-triggering resets using public usernames. Users are being advised to enable two-factor authentication and Password Reset Protect while the investigation continues.

TechCrunch · Security · 14d agoPhishing & fraud in the wild

unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face

Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.

Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.

Hugging Face trending models · 21d agoModel release1

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AI · 7d agoAI tools & infraHN 41↑ · 9 comments

X Money rollout linked to password-reset attacks

X is investigating bulk unsolicited password-reset emails as its X Money payments service expands, with no confirmed breaches or account takeovers yet.

X users began reporting unexpected password-reset emails and codes on September 1, and product engineer Mridul Singhai said attackers appear to believe newly widespread X Money access makes accounts worth targeting. X says it has found no evidence of any breach or successful account takeover, and completing a reset still requires access to the account's email or phone number. X Money offers eligible US users interest-bearing accounts, a Visa debit card, and P2P payments, with Cross River Bank providing banking infrastructure. Malwarebytes warns the reset flood can serve as cover for phishing and urges reset protection, 2FA, and unique passwords.

Malwarebytes Labs · 12d agoPhishing & fraud in the wild

Fwd: XZ Utils 5.8.4 and a security fix

XZ Utils 5.8.4 fixes an invalid memory write that occurs when a decoder is reinitialized after allocation failure in 5.8.3 and older.

XZ Utils 5.8.4 has been released with a security fix for versions 5.8.3 and older. The flaw is an invalid memory write that can occur when a decoder is reinitialized after an allocation failure. The announcement was posted on the oss-security mailing list by Sam James pointing to the upstream stable release. Users and distributions running affected versions should upgrade to 5.8.4.

oss-security · 6d agoVulnerability

[webapps] CubeCart 6.7.4 - Stored XSS

A proof-of-concept stored cross-site scripting exploit targeting CubeCart 6.7.4 was published on Exploit-DB.

Exploit-DB lists a proof-of-concept exploit for a stored cross-site scripting (XSS) vulnerability in CubeCart 6.7.4, a PHP-based e-commerce web application. The listing demonstrates injection of attacker-controlled script that persists in the application, but no exploitation in the wild or CVE assignment is reported in the provided text.

Exploit-DB · 16d agoExploit / PoC1

Adobe security advisory (AV26-848)

Canada's Cyber Centre relayed Adobe advisories covering vulnerabilities in Campaign Classic, Substance 3D apps, Adobe XD, Illustrator, and C2PA tools.

Bulletin AV26-848 lists Adobe vulnerabilities affecting Campaign Classic (through 7.4.4 build 9400), Substance 3D Designer, Painter, and Sampler, Adobe XD, C2PA Tool, Content Credentials Rust SDK, and Illustrator 2025/2026. The Canadian Centre for Cyber Security encourages users and administrators to review the linked Adobe bulletins and apply updates. Specific CVE identifiers are not enumerated in the advisory text.

Canadian Centre for Cyber Security · 21d agoAdvisory

Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CL · 23h agoAI research

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF — new model trending #8 on Hugging Face

A new Qwen3.8-27B GGUF fine-tune claims ARC-C 735 at 8-bit with thinking tokens cut 2x-10x versus the base model.

Independent creator DavidAU released a GGUF fine-tune of Qwen3.8-27B built with Unsloth, claiming ARC-C of 735 at 8-bit and 719 at 4-bit, trending #8 on Hugging Face. The 'TURBO' variant cuts thinking tokens by one half to as much as one tenth while retaining output quality and detail. The repo ships both regular and MTP quants and claims gains over the base model across seven benchmarks, using 'Cold Fusion (GAIN + Unsloth)' and 'Fable Fusion 711' training methods.

Hugging Face trending models · 15d agoModel release

ExecCritic: Learn to Test, Test to Improve for Coding Agents

ExecCritic separates test generation from patching for coding agents, lifting SWE-bench Verified resolution to 72.6%.

ExecCritic pairs a test-verify-revise scaffold with role-specific reinforcement learning: a Test agent writes repository-native tests and a Repair agent fixes code from execution feedback, both using Qwen-3.5-35B-A3B backbones. Post-trained Qwen agents compose to 72.6% on SWE-bench Verified, an 11.4-point gain over the 61.2% no-test baseline, without stronger-model or oracle feedback at evaluation time. The work shows test quality is the key variable: base-agent tests lowered resolution to 57.3% while GPT-5.6-sol tests raised it to 65.3%.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

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 · 8d agoModel release1

From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.

Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver

Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.

Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.

The Decoder · 9d agoAI research

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research

nex-agi/Nex-N2.5-mini — new model trending #30 on Hugging Face

Nex-AGI releases Nex-N2.5 agentic model family (mini, Pro, Max) with a 1.6-trillion-parameter MoE Max, open weights, and hosted access via OpenRouter.

Nex-AGI launched Nex-N2.5, a family of agentic models in mini, Pro, and Max sizes, with the Max version built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation and the company's first complete post-training effort at trillion-parameter scale. The models target long-horizon computer use, web browsing, and visually grounded agentic tasks, with expanded agent training environments. Reported benchmarks include Max scoring 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro, trailing Claude Opus 5. Weights are being released openly on Hugging Face and ModelScope, with hosted access through OpenRouter.

Hugging Face trending models · 8d agoModel release1

n8n security advisory (AV26-880)

Canada's Cyber Centre flagged multiple n8n vulnerabilities in versions prior to 1.123.76 and several 2.x releases, urging users to update.

The Canadian Centre for Cyber Security issued advisory AV26-880 stating that n8n is affected by vulnerabilities in versions prior to 1.123.76 and prior to 2.35.4, 2.36.2, 2.37.7, and 2.38.2. The advisory links to the n8n-io GitHub repository for details. Users and administrators are encouraged to review the advisory and apply the necessary updates.

Canadian Centre for Cyber Security · 12d agoAdvisory

Show HN: Panel – A research workspace where the agent can build its own panes

Panel is an open-source research workspace where AI agents dynamically build their own panes, posted on Hacker News.

Panel, shared as a Show HN project on GitHub, is a research workspace in which the agent can construct its own panes rather than using a fixed interface. The post received 41 points and 8 comments on Hacker News. It targets agentic research workflows with a dynamically generated UI.

Hacker News · AI · 1d agoAI tools & infraHN 41↑ · 8 comments

Vulnerabilities fixed in libxml2-2.15.4

libxml2 2.15.4 fixes an out-of-bounds read in xmlregexp's NXT macro plus several integer overflow and parsing flaws.

libxml2 2.15.4 (released September 1, 2026) includes security fixes: an out-of-bounds read in the xmlregexp NXT macro, missing overflow checks in dict.c, uri.c, and valid.c, an integer overflow in xmlIO before the writecallback, and an overflow check in xmlXPtrEvalXPtrPart. The release also propagates parseFlags in xmlXIncludeProcess and xmlXIncludeProcessTree. No CVE identifiers, exploitation, or severity ratings are given in the announcement.

oss-security · 11d agoVulnerability

TokenRhythm/NeoHorse-1-4B — new model trending #30 on Hugging Face

TokenRhythm releases NeoHorse-1-4B, an Apache-2.0 agentic fine-tune of Qwen3.5-4B claiming +5.93 benchmark macro-average gain.

NeoHorse-1-4B is a roughly 4B-parameter text-only causal language model post-trained by TokenRhythm from Qwen/Qwen3.5-4B for agent harnesses, tool use, coding, and instruction following. It applies routing-guided curriculum SFT and routing-guided on-policy distillation over execution trajectories as an early prototype toward recursive self-improvement (RSI). The release reports a 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93) and is distributed under Apache-2.0, trending #30 on Hugging Face.

Hugging Face trending models · 11d agoModel release1

GreyNoise + CrowdStrike: Real-Time Edge Intelligence in Falcon Next-Gen SIEM and Charlotte Agentic SOAR

GreyNoise expanded its CrowdStrike Falcon integration with Next-Gen SIEM dashboards, correlation rules, and Charlotte Agentic SOAR playbooks.

GreyNoise announced an expanded integration with the CrowdStrike Falcon platform, adding purpose-built content for Falcon Next-Gen SIEM and Charlotte Agentic SOAR. The integration includes a dedicated SIEM dashboard, correlation rules that detect allowed inbound traffic from malicious infrastructure, and SOAR playbooks that inject GreyNoise threat context into automated response workflows.

GreyNoise · 16d agoTools

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Hugging Face published a tutorial on training and finetuning multi-vector embedding models using the Sentence Transformers library.

Hugging Face's blog walks through training and finetuning multi-vector embedding models with Sentence Transformers. Multi-vector approaches store multiple vectors per document to support late-interaction retrieval. The post is a practical guide for developers building retrieval pipelines with the library.

Hugging Face Blog · 21d agoAI tools & infra1

xHunt Campaign: New Watering Hole Identified for Credential Harvesting

Unit 42 tied the xHunt campaign to a watering hole on a Kuwait government website used to passively harvest visitors' NTLM credential hashes.

Palo Alto Unit 42 identified a Kuwait government organization's webpage injected with hidden HTML referencing image paths on domains (microsofte-update.com, learn-service.com) tied to xHunt/Hisoka C2 infrastructure. When visitors loaded the page, Windows would attempt SMB/NetBIOS authentication to the remote share, allowing the operators to capture NTLM hashes that could be cracked or relayed. Related DNS redirect activity on xHunt infrastructure in 2019 pointed to additional credential-harvesting interest against Kuwaiti government email servers.

Palo Alto Unit 42 · Aug 17, 2026Threat actor in the wild