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ZDI-26-653: TrendAI Apex One Security Agent Cache Mechanism Time-Of-Check Time-Of-Use Local Privilege Escalation Vulnerability

ZDI-26-653: TOCTOU race in Trend Micro Apex One cache mechanism, CVE-2025-71415, enables local privilege escalation, rated CVSS 7.8.

The Zero Day Initiative published advisory ZDI-26-653 for the Trend Micro Apex One Security Agent. A time-of-check time-of-use (TOCTOU) flaw in the cache mechanism, tracked as CVE-2025-71415, allows local attackers to escalate privileges after gaining low-privileged code execution. ZDI assigned a CVSS 7.8 rating. A vendor patch is available for affected installations.

ZDI Published AdvisoriesSep 10, 05:00 UTC · 6d agoAdvisory 3 sources CVE-2025-71415

Google Patches Pixel Modem Zero-Day Exploited in Targeted Attacks

Google patched Pixel modem zero-day CVE-2026-58704 (CVSS 8.0), exploited in limited targeted attacks, enabling adjacent privilege escalation without user interaction.

Google's September 2026 Pixel security update fixes CVE-2026-58704, a CVSS 8.0 permission bypass caused by a logic error in the cellular modem, allowing remote (proximal/adjacent) elevation of privilege with no user interaction or additional privileges. Google confirms indications of limited, targeted exploitation in the wild but provides no attribution, target count, or attack objectives. The modem location is significant because it operates below much of the Android application security model. The bulletin also patches multiple critical RCE flaws in IMS, libpixelimsmedia, VPU, modem, telephone and BigOcean components, with the 2026-09-05 patch level protecting devices.

Security Affairsupdated 17:56 UTC · 3h agofirst 13:43 UTC · 7h agoExploit / PoC in the wild 8 sources CVE-2026-58704

CVE-2026-52307: Stored XSS in 1CMS v5.6

CVE-2026-52307: authenticated stored XSS in 1CMS (ClassCMS) v5.6 Column Management lets attackers inject scripts via the title field.

ClassCMS 1CMS v5.6 contains an authenticated stored cross-site scripting vulnerability, CVE-2026-52307, in the Column Management component. Attackers can execute arbitrary web scripts or HTML by injecting a crafted payload into the title field. No CVSS score, patch information, or exploitation evidence was provided in the disclosure.

Full DisclosureSep 8, 17:24 UTC · 8d agoVulnerability CVE-2026-52307

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.CLSep 10, 17:43 UTC · 6d 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 papersSep 10, 20:00 UTC · 6d agoModel release

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.

JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:15 UTC · 1d agoAI research

Towards a Deterministic Math Solver for Clinical Language Models

Paper shows handing arithmetic to a deterministic Python solver beats direct model calculation at 32B but not reliably at 7B on MedCalc-Bench.

Researchers test a Program-Solve interface where clinical LLMs write case-specific Python executed by a restricted local solver instead of doing arithmetic directly. On MedCalc-Bench Verified (1,100 cases, 55 calculators), Qwen2.5-32B-AWQ scored 90.53% with solver handoff versus 83.47% with direct arithmetic (+7.05 points), while Qwen2.5-7B gained an unreliable +3.29 points with a confidence interval spanning zero. The authors audited the benchmark against clinical guidelines and flagged 16 of 55 calculators for version, use, or coefficient concerns.

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

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

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

Proximity Gaps for Gabidulin Codes and Applications

Researchers prove proximity-gap bounds for rank-metric and Gabidulin codes, enabling the first polynomial commitment scheme framework based on rank-metric error-correcting codes.

The paper proves every linear rank-metric code admits a proximity gap for deltas up to (d-1)/(3n) with error at most q^(e+1)/q^m, and improves the gap to (d-1)/(2n) for Gabidulin codes with error at most 10q^(n-1)/q^m, matching bounds for Reed-Solomon codes. A constructed infinite family of constant-rate Gabidulin codes shows the (d-1)/(2n) bound is tight, and a counterexample establishes a lower bound on the error at the d/(3n) gap. Applications include an IOPP for interleaved Gabidulin codes adapted from the Ligero IOPP and a q-linearized polynomial commitment scheme adapted from Ligero-based PCS, reportedly the first PCS framework based on rank-metric codes.

arXiv cs.CRSep 9, 07:45 UTC · 7d agoResearch

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space11:09 UTC · 10h agoModel release1

openbmb/MiniCPM5-2B-GGUF — new model trending #30 on Hugging Face

OpenBMB released MiniCPM5-2B, a dense 2B on-device model claiming open-source SOTA among 2B-class models.

OpenBMB released MiniCPM5-2B, the second model in the MiniCPM5 series following MiniCPM5-1B, as a dense 2B Transformer built for on-device and resource-constrained deployment with GGUF weights on Hugging Face. The team claims 2B-class open-source state-of-the-art performance, remaining competitive with 4B-class models in coding, mathematics, long-context understanding, tool use and agentic tasks. The release includes a tech report, GitHub repository and online demo, and is currently trending on Hugging Face.

Hugging Face trending modelsSep 5, 01:44 UTC · 11d agoModel release1

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending modelsAug 28, 18:38 UTC · 19d agoModel release1

Graph Machine: Towards Better Pretraining via Edges

Researchers propose Graph Machine, an O(n)-state sparse architecture that replaces 75% of Qwen3-0.6B dense layers with only slight loss change.

The paper introduces the Graph Machine (GM), an architecture that maintains an O(n)-sized state accessed through sparse, dynamic routing via pointer-like edges updated differentiably by a referral mechanism resembling pointer chasing. The authors replaced 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrained from scratch on 15.7B tokens. Retrieving 2 of 4,096 tokens per KV head in each sparse layer degrades loss only slightly, while retrieving 4 marginally improves loss over the dense baseline.

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

Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

CoTS temperature scaling cuts test-time prompt tuning's expected calibration error from 11.90% to 5.38% on ImageNet variants while raising accuracy.

The paper proposes CoTS, a post-hoc calibration method that applies temperature scaling to minimize the confidence gap between test-time-adapted and zero-shot predictions. A weak-strong ensemble variant, E-CoTS, further exploits multiple test-time augmentations to boost accuracy while maintaining calibration. E-CoTS reduces average expected calibration error from 11.90% to 5.38% on ImageNet variants while increasing accuracy from 60.74% to 62.95%.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:25 UTC · 1d agoAI research

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papersSep 2, 20:00 UTC · 14d agoAI research

Subgroup Packing for Batched PASTA Transciphering

Interleaved subgroup packing cuts PASTA homomorphic transciphering server cost by a median 1.60x at the expense of remaining noise capacity.

The paper studies how record word layout affects the cost of PASTA transciphering, where a server converts symmetrically encrypted records into homomorphic ciphertexts. A subgroup layout interleaves records so cyclic shifts preserve positions, reducing required displacements from 255 to 128 versus contiguous packing. In HElib across twelve paired corpora under six homomorphic keys, the median direct-to-subgroup server cost ratio is 1.60 including fresh generation, conversion, and two queries, but with less remaining noise capacity.

arXiv cs.CRSep 11, 09:24 UTC · 5d agoResearch

OPEN-1B: A Fully Auditable Training Run

Open-1B releases a 1B-parameter model with bitwise-reproducible training, letting independent auditors verify every step of the run on commodity hardware.

The paper introduces a 'fully auditable' tier of model transparency: every training operation is reproducible with bitwise certainty on heterogeneous commodity hardware by imposing definite ordering on GPU kernel reductions, data batch ordering, and collective communication. Because replaying a full run on one machine is infeasible, a collective verification scheme lets many independent auditors certify individual steps covering the whole run. The authors release Open-1B with its full pretraining dataset, every intermediate checkpoint, the training codebase, and an audit harness. This rules out undisclosed data, injected biases, or backdoors that proof-of-learning or proof-of-training-data techniques cannot exclude.

arXiv cs.AI / cs.LG / cs.CLSep 15, 16:23 UTC · 1d agoAI research

ZDI-26-570: Linux Kernel IGMP Subsystem Race Condition Local Privilege Escalation Vulnerability

ZDI publishes ZDI-26-570, a CVSS 7.5 race condition local privilege escalation in the Linux kernel's IGMP subsystem.

The Zero Day Initiative disclosed a race condition in the Linux kernel's IGMP subsystem that allows local attackers to escalate privileges. Exploitation requires the attacker to first execute high-privileged code on the target system. ZDI assigned a CVSS rating of 7.5 to this finding; no CVE id is stated in the advisory text.

ZDI Published AdvisoriesAug 13, 05:00 UTC · Aug 13, 2026Advisory

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Uno pairs autoregressive LLMs with lightweight diffusion weights to draw multiple tokens in parallel, delivering up to 3x lossless speedup without a draft model.

The paper introduces diffusion-augmented LLMs: autoregressive weights trained with the standard next-token objective plus lightweight diffusion weights trained via a Diffusion Distillation phase to emit multiple tokens in parallel. Psi-Spec samplers enable lossless acceleration without the separate draft model required by speculative decoding. The 8B Uno model outperforms the 26B open DiffusionGemma and proprietary Mercury 2 on agentic tool use, coding, and long-context reasoning benchmarks, with up to 3x throughput gains over the base model at all evaluated batch sizes. Code and checkpoints are released publicly.

Hugging Face daily papersSep 2, 20:00 UTC · 14d agoAI research

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

Climate-ModernBERT domain-adapted encoders reach 76.3 average F1 across nine climate benchmarks, 2.8 points above vanilla ModernBERT-Base.

The authors continue pretraining ModernBERT-Base on three climate corpora - academic text, climate-filtered web data, and synthetic documents - and compare joint mixtures against parameter-space merging of specialized checkpoints. The best model achieves 76.3 average F1 across nine climate NLP benchmarks, a 2.8-point improvement over the vanilla baseline. Academic climate corpora provide the strongest adaptation signal, and parameter-space merging outperforms joint multi-source training while preserving complementary corpus information; all variants are released.

arXiv cs.AI / cs.LG / cs.CLSep 7, 17:41 UTC · 9d agoAI research

ZDI-26-572: Linux Kernel XFRM Race Condition Local Privilege Escalation Vulnerability

ZDI publishes ZDI-26-572, a CVSS 7.5 race condition local privilege escalation in the Linux kernel's XFRM subsystem.

The Zero Day Initiative disclosed a race condition in the Linux kernel's XFRM (transform) subsystem allowing local attackers to escalate privileges. Exploitation requires the attacker to first run high-privileged code on the affected system. The advisory carries a CVSS rating of 7.5; no CVE id is listed in the disclosure text.

ZDI Published AdvisoriesAug 13, 05:00 UTC · Aug 13, 2026Advisory

Testing race conditions with memory access tracing and stack-based delay injection

Google Project Zero released MAccConc, Linux kernel tooling that traces memory accesses to explore and test race condition interleavings.

A Google Project Zero researcher published MAccConc (Memory Access Concurrency), tooling for exploring possible interleavings of multithreaded test cases in the Linux kernel, available on GitHub. The tools use KCOV with ASAN outline-mode instrumentation to record per-access memory traces, enabling automatic testing of all A-B-A interleavings plus terminal and GUI explorers for manual analysis. The work targets confirming race condition candidates, building reliable regression tests, and enabling concurrency fuzzing, drawing on ideas from SKI and Ned Williamson's sockfuzzer.

Google Project ZeroSep 8, 07:00 UTC · 8d agoResearch1

Previewing Ultrafast mode: GPT-5.6 Sol at up to 14X the speed

OpenAI previews Ultrafast, an API service tier running GPT-5.6 Sol up to 14x faster via Cerebras at up to 750 output tokens per second.

OpenAI announced a preview of Ultrafast, a new API service tier that runs GPT-5.6 Sol at up to 14 times the speed of standard inference. The tier is powered by Cerebras hardware and delivers up to 750 output tokens per second. The offering targets latency-sensitive developer workloads on OpenAI's API platform.

OpenAI NewsAug 13, 10:00 UTC · Aug 13, 2026AI tools & infra

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

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

IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license

IBM released Granite Time Series PatchTST-FM-r2, a claimed state-of-the-art time series foundation model under a commercial-friendly license.

IBM Research announced the release of Granite Time Series PatchTST-FM-r2, published via the Hugging Face blog. The model is presented as state-of-the-art for time-series forecasting and is offered under a license permitting commercial use. No benchmark numbers or model size details were provided in the available text.

Hugging Face BlogSep 9, 15:36 UTC · 7d agoModel release

Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes

Independent developer Hugo Vergnes trained a 3.8B-parameter Llama-style model to 0.384 CORE on 65B tokens for $998 in 43 hours on rented B200s.

Hugo Vergnes trained little-lm, a 3.848B-parameter decoder-only LLM, on 65.3B tokens in 43 hours for $998 using rented NVIDIA B200s, scoring 0.384 on the CORE benchmark and beating nanochat d32 (0.310) at similar cost. The Llama-style architecture uses RMSNorm, RoPE, GQA with 24 query and 8 KV heads, relu-squared MLPs, QK-norm, and ResFormer-style value embeddings that account for 19% of parameters. Key wins included the Muon optimizer for matrix parameters, a trapezoidal learning-rate schedule with linear cooldown, FP8 training plus vocabulary padding for roughly 33% throughput gains, and the ClimMix dataset over FineWeb-Edu. The project, inspired by Karpathy's nanochat, was built as a config-driven YAML framework for small LLM training.

Hacker News · securitySep 10, 02:04 UTC · 6d agoAI research HN 28↑ · 2 comments1

GLM-5.3: How Chinese labs keep stride with the frontier

Z.ai released GLM-5.3, a ~750B-parameter model with frontier agentic coding scores, with open weights on Hugging Face planned in two weeks.

Z.ai announced GLM-5.3, initially available only in its coding plan, with API access and open Hugging Face weights promised within two weeks. The roughly 750B-parameter model, one-third the size of Moonshot AI's Kimi K3, surpasses Kimi K3 on many benchmarks and beats Claude Fable 5 or GPT-5.6-Sol on some, placing it at the frontier of agentic coding benchmarks. GLM-5.3 reuses the GLM-5.2 base model with substantially extended post-training based on more RL environments, more diverse tasks and more compute. The post also analyzes how Chinese labs keep pace with the frontier, arguing release speed matters more than distillation.

InterconnectsAug 14, 21:23 UTC · Aug 14, 2026Model release

Agnes-AI/Agnes-3.0-Flash — new model trending #30 on Hugging Face

Agnes AI releases open-weight Agnes-3.0-Flash Preview, a 33B multimodal model with 262k-token context under Apache 2.0.

Agnes AI released Agnes-3.0-Flash Preview, an open-weights multimodal checkpoint with 33B parameters and a 262,144-token context window under Apache 2.0. The model supports text, image, and video understanding, tool calling, and adjustable reasoning effort. The repo clarifies this preview checkpoint is distinct from the production/API Agnes 3.0 Flash model, which uses a different configuration with a 1M-token context window. Reported reference results include IFBench 74.20 and SciCode 38.08 against peers such as Qwen3.6-35B-A3B, Kimi K2.5, and MiniMax M3.

Hugging Face trending modelsSep 11, 15:50 UTC · 5d agoModel release