Search: “NSTAC”

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The GNU C Library security advisory update for 2026-09-10

glibc advisory GLIBC-SA-2026-0016: nscd can crash from a stack overflow when an untrusted DNS server returns oversized responses.

A new GNU C Library advisory (GLIBC-SA-2026-0016) describes a stack overflow in the nscd service caused by unbounded alloca use. glibc 2.3.4 and newer may crash when a malicious DNS server returns an oversized response, resulting in degraded DNS resolution. Exploitation requires nscd to be enabled and the system to be using an untrusted DNS server. The advisory was posted to oss-security by glibc maintainer Siddhesh Poyarekar.

oss-security·updated Sep 14, 19:54 UTC · 2d ago·first Sep 11, 01:59 UTC · 5d agoVulnerability 2 sources2

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[Control Systems] National Instruments security advisory (AV26-914)

Canada's Cyber Centre relayed a National Instruments advisory covering improper access control and cleartext sensitive-information flaws in NI SystemLink and SystemLink Server.

Advisory AV26-914, dated September 11, 2026, notes that NI SystemLink and SystemLink Server prior to or equal to 2026 Q3 Patch 1 are affected by improper access controls and storage of sensitive information in cleartext. The Canadian Centre for Cyber Security urges users and administrators to review the vendor's links and apply available security updates.

Canadian Centre for Cyber Security·Sep 11, 15:56 UTC · 5d agoAdvisory2

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Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Audits of 10 classifiers on BRFSS show target leakage, not model class, drives the reported 0.89 AUROC in survey-based cardiovascular screening.

The study benchmarks ten model classes, including glass-box and tabular foundation models, for prevalent myocardial infarction on 442,067 respondents of the 2022 BRFSS across five feature tiers of decreasing leakage risk. Removing two post-diagnostic features costs every model 0.049-0.051 AUROC and collapses performance into a 0.0045-wide band, and the explainable boosting machine matches all alternatives within 0.005 while scoring roughly 104x faster than the strongest foundation model. Frozen models transport within 0.002 AUROC to 2023 data; the authors conclude evaluation practice and feature sets, not model capacity, are the binding constraint.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:29 UTC · 6d agoAI research1

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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 Zero·Sep 8, 07:00 UTC · 8d agoResearch1

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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:55 UTC · 6d agoAI research2

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ISC BIND security advisory (AV26-931)

Canadian Cyber Centre relays ISC advisory on vulnerabilities affecting multiple BIND 9 branches; administrators should review the matrix and apply updates.

On September 16, 2026, the Canadian Centre for Cyber Security (AV26-931) relayed ISC's advisory on vulnerabilities affecting ISC BIND 9 versions prior to or equal to 9.18.50, 9.20.27, and 9.21.25, including their -S1 variants. No specific CVE identifiers or exploitation details are provided in the bulletin. Administrators are directed to ISC's BIND 9 Software Vulnerability Matrix and encouraged to apply updates as they become available.

Canadian Centre for Cyber Security·19:43 UTC · 2h agoAdvisory

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[Control Systems] National Instruments security advisory (AV26-856)

Canada's Cyber Centre relayed National Instruments advisories for memory corruption, out-of-bounds read, and out-of-bounds write flaws in LabVIEW versions.

The Canadian Centre for Cyber Security published control systems advisory AV26-856 covering National Instruments LabVIEW. Affected versions include releases before 23.0.0, 23.3.10, 24.3.7, 25.3.5, and 26.3.1. The flaws include memory corruption, an integer conversion out-of-bounds read, and an integer overflow out-of-bounds write. Users and administrators are urged to review the links and apply NI security updates.

Canadian Centre for Cyber Security·Aug 28, 12:16 UTC · 19d agoAdvisory

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Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

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ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on OpenReview rebuttals to generate actionable peer-review feedback with grounded revision suggestions, benchmarked on 1,000 curated instances.

The paper defines Actionable Peer-review Generation as diagnostic claim generation plus revision suggestion generation and introduces ActReview, a rebuttal-guided post-training framework. From OpenReview review-rebuttal threads the authors build ActReview-40K, aligning reviewer weaknesses with author responses grounded in localized paper evidence, and post-train Qwen3-8B-Base with multi-task SFT followed by GRPO using weakness-specific rubric rewards. They also release ActReview-Bench, a human-curated 1,000-instance benchmark, on which ActReview outperforms prior specialized review-generation models on actionability and grounding while remaining competitive with strong prompt-based LLMs. Human evaluation confirms improved revision usefulness but identifies a remaining gap in technical accuracy.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

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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

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OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL benchmarks multimodal federated learning for chest radiograph diagnosis across 3-20 clients, with FedProx leading under severe non-IID skew.

OmniMed-FL studies multimodal federated learning combining chest radiographs and clinical notes for five-class condition classification under HIPAA/GDDR-compliant decentralized training. It benchmarks eight fusion strategies, imputation rules, and federated baselines under Dirichlet non-IID partitioning across 3-20 hospital clients. With 5 clients and severe skew (alpha=0.1), FedProx scored 0.737 macro-F1 versus 0.662 for FedAvg and 0.297 for local-only training. Multimodal fusion beat unimodal inputs (0.956 vs 0.934 text, 0.664 images) on the synthetic corpus.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 15:56 UTC · 7d agoAI research1

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Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 12d agoAI research

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Tables Decoded: DELTA for Structure, TARQA for Understanding

DELTA extracts tables into compact OTSL text and TARQA fine-tunes LLMs on it, beating VLM baselines on table QA.

DELTA separates physical structure recognition, logical structure recognition, and OCR to output tables in Optimised Table Structure Language (OTSL), a compact unified format encoding cell arrangements and content. It achieves TEDS-Structure scores comparable to state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M, with robustness tested on a curated Hindi benchmark, TORQUE. TARQA, an LLM fine-tuned on OTSL sequences, gains 9.3 percentage points on WTQ TabQA and 9.2 points on FinTabNetQA TabVQA; code, models, and the benchmark are released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:59 UTC · 1d agoAI research

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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

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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 2d agoAI safety & security

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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·Sep 15, 16:43 UTC · 1d agoAI research

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Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models

Researchers introduce KoNA, a benchmark exposing vision-language models' failures at selective non-compliance, plus fine-tuning that improves refusal and abstention accuracy.

KoNA is a benchmark for evaluating selective non-compliance in vision-language models across five categories: False Premise, Visual Inaccessibility, Universal Unknown, Task Feasibility and Safety. It tests both query-level and component-level non-compliance using paired single and compound queries, and evaluations across diverse VLMs show models often fail to refuse, correct or abstain appropriately, with failures worsening on compound queries. Fine-tuning VLMs on KoNA examples substantially improves non-compliance accuracy while largely maintaining performance on fully answerable tasks.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research1

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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·Sep 3, 18:59 UTC · 13d agoAdvisory

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Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.

Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.

MarkTechPost·04:59 UTC · 17h agoModel release

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Cisco security advisory (AV26-876)

Canada's Cyber Centre relayed Cisco advisories covering a Nexus 9000 Silicon One RCE, IOS XR hardening, and denial-of-service flaws across IP phone lines.

The Canadian Centre for Cyber Security advisory AV26-876 lists Cisco vulnerabilities affecting IOS XR, Nexus 9000 Series switches, and several IP phone series. Included are a Nexus 9000 Silicon One remote code execution vulnerability, a September 2026 IOS XR security hardening release, and SIP software denial-of-service flaws in Desk Phone 9800, IP Phone 7800/8800, and Video Phone 8875. The Cyber Centre urges users and administrators to review the Cisco advisories and apply updates as they become available. No active exploitation is reported in the advisory.

Canadian Centre for Cyber Security·Sep 3, 13:13 UTC · 13d agoAdvisory

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Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPost·06:52 UTC · 15h agoModel release

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Lightweight Zero Trust via Automotive SDN

Researchers map automotive SDN with MACsec/MKA and CORECONF/YANG to NIST SP 800-207, satisfying five of seven Zero Trust tenets without added infrastructure.

Zonal in-vehicle networks ship Ethernet, MACsec, and TSN but treat the network itself as trusted, with no standardized runtime way to revoke access, rotate keys, or contain a compromised ECU. The paper first analyzes what Open Alliance TC17 v1.0 MACsec/MKA with pre-shared CAKs already provides against the seven NIST SP 800-207 Zero Trust tenets. It then adds CORECONF/YANG management per Open Alliance TC19, mapping the SDN Controller and Agents one-to-one onto NIST's PE, PA, and PEP, and instantiates a YANG-based network-access-control flow and key-management scheme. The result fully covers five of the seven tenets and partially covers two, without any ZTA-specific infrastructure.

arXiv cs.CR·Sep 9, 07:20 UTC · 7d agoResearch

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Pyramid Solutions NetStaX EtherNet/IP Stack

CISA flags critical CVE-2026-78012 (CVSS 9.8) stack buffer overflow in Pyramid Solutions NetStaX EtherNet/IP stacks below v5.6.1, risking crashes or remote attack vectors.

CISA republished Pyramid Solutions' advisory for CVE-2026-78012, a CWE-121 stack-based buffer overflow in the NetStaX EtherNet/IP stack versions prior to 5.6.1, scored CVSS 9.8. Large Class 3 explicit-message requests can exceed the application-side receive buffer without generating a CIP error, potentially causing memory corruption, device crashes, or a silent remote attack vector. All eight adapter and scanner DLL/development kit variants, including CIP Security editions, are affected across critical manufacturing, energy, water, and chemical sectors. No public exploitation has been reported.

CISA Advisories·Sep 3, 12:00 UTC · 13d agoAdvisoryCVE-2026-78012

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Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

Referee-Based Quality Estimation flags unreliable polyp segmentations at inference without ground truth, reaching ROC-AUC 0.960 with SegFormer-B0 referees.

RBQE measures agreement between a primary segmentation model and an independently trained referee on a 1,223-image external benchmark drawn from four public datasets. A cross-architecture SegFormer-B0 referee achieves the strongest signal (ROC-AUC 0.960), beating a Test-Time Augmentation baseline by 0.055 ROC-AUC under an identical protocol. Excluding trivially separable empty-mask cases, ROC-AUC falls to 0.876 (SegFormer-B0) and 0.783 (same-architecture control), but RBQE's margin over baselines widens. Progressive rejection of low-agreement predictions increases mean Dice of retained outputs, supporting selective prediction at the cost of one extra forward pass.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:32 UTC · 7d agoAI research

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Docker security advisory (AV26-925)

Canadian Cyber Centre advisory AV26-925 flags a vulnerability in Docker Sandboxes versions prior to 0.43.0 and urges users to apply updates.

The Canadian Centre for Cyber Security issued advisory AV26-925 on September 15, 2026, stating Docker is affected by a vulnerability in Docker Sandships prior to version 0.43.0. The advisory directs users and administrators to review Docker sbx-releases and security announcements and apply updates as they become available. No exploitation or technical details are provided.

Canadian Centre for Cyber Security·Sep 15, 18:59 UTC · 1d agoAdvisory

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Cisco security advisory (AV26-197) – Update 3

CISA added Cisco CVE-2026-20079 to its KEV catalog; the Canadian Cyber Centre urges updates across Secure Firewall ASA, FTD, FMC, and SCC products.

The Canadian Centre for Cyber Security updated advisory AV26-197 covering March 2026 Cisco advisories for Security Cloud Control, Secure Firewall Management Center, ASA, and FTD. Cisco confirmed CVE-2026-20131 was actively exploited on March 18, 2026, and CISA added it to KEV on March 19. In Update 3, dated September 9, 2026, CISA added CVE-2026-20079 to the KEV catalog. The underlying flaws include FMC authentication bypass and remote code execution, ASA TCP-flood denial of service, and ASA/FTD IPsec denial of service.

Canadian Centre for Cyber Security·Sep 9, 20:04 UTC · 7d agoExploit / PoC in the wildCVE-2026-20131CVE-2026-20079

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ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 1d agoAI research

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ActReview: Rebuttal-Guided Training Data and Rubric Rewards for Actionable Peer Review Generation

ActReview post-trains Qwen3-8B-Base on 40K rebuttal-derived instances with rubric rewards to generate actionable, grounded peer-review feedback, plus a 1,000-instance benchmark.

The framework builds ActReview-40K from real OpenReview review-rebuttal threads, aligning reviewer weaknesses with author responses and grounding feedback in localized paper evidence. Qwen3-8B-Base is post-trained with multi-task supervised fine-tuning followed by GRPO using candidate-aware, weakness-specific rubric rewards. Experiments show improved actionability and grounding over prior specialized review-generation models, supported by ActReview-Bench, a human-curated 1,000-instance evaluation set. Human evaluation confirms better revision usefulness while noting a remaining gap in technical accuracy.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:30 UTC · 8d agoAI research

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n8n security advisory (AV26-916)

Canadian Cyber Centre advisory AV26-916 warns n8n before versions 2.37.7, 2.38.2, and 1.123.76 is affected by an undisclosed vulnerability; patch required.

The Canadian Centre for Cyber Security issued advisory AV26-916 on September 11, 2026, stating that n8n is affected by a vulnerability in versions prior to 2.37.7, 2.38.2, and 1.123.76. Fixed releases [email protected], [email protected], and [email protected] are available via GitHub. No CVE identifier or exploitation details are provided; administrators are urged to review linked resources and apply updates.

Canadian Centre for Cyber Security·Sep 11, 19:32 UTC · 5d agoAdvisory

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Path to Astra: critical capabilities and frontier safeguards

OpenAI says Astra is its first model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, launching with stronger safeguards.

OpenAI published 'Path to Astra', announcing that Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under its Preparedness Framework. The company says the model will be released with strengthened frontier safeguards, marking a milestone in frontier capability and safety evaluation.

OpenAI News·Sep 1, 13:00 UTC · 15d agoModel release

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