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ZDI-26-640: Oracle VirtualBox VirtioSCSI Uninitialized Memory Information Disclosure Vulnerability

ZDI detailed an uninitialized memory flaw in Oracle VirtualBox's VirtioSCSI (CVE-2026-71132) allowing local attackers to disclose sensitive information.

Zero Day Initiative published ZDI-26-640, a CVSS 5.3 information disclosure vulnerability in the VirtioSCSI component of Oracle VirtualBox. An attacker must first run high-privileged code on the guest system before the uninitialized memory issue can be used to disclose sensitive information. The flaw is tracked as CVE-2026-71132. The advisory reports no exploitation activity.

ZDI Published AdvisoriesSep 9, 05:00 UTC · 7d agoVulnerability 6 sources CVE-2026-71132

Abliteration.ai is making a business out of removing AI guardrails

Startup Abliteration.ai commercially hosts guardrail-free open-weight models like Z.ai's GLM-5.3, raising misuse concerns for offensive cyber and bio tasks.

Abliteration.ai offers hosted versions of open-weight models with refusal behavior stripped via the abliteration technique, including Z.ai's newly released GLM-5.3, accessible free through a browser or API. The startup says its goal is enabling offensive cyber, red-teaming, and agent testing work that guarded models refuse to do. Safety researchers such as CivAI's Andrew Yoon warn that easily deployed unguarded models could be used for harm, and experts suggest government interventions like classifier requirements or GPU access verification. The revenue-funded startup serves red-teaming firms working with banks and critical-infrastructure organizations and has no KYC beyond credit card logging.

TechCrunch · SecuritySep 3, 18:37 UTC · 13d agoAI safety & security

IFM/K2-Horizon-MoVA-36B-A4B — new model trending #15 on Hugging Face

IFM released K2-Horizon-MoVA-36B-A4B, an open-weights 36B-parameter MoE model with 4B active parameters and native 512K context.

IFM released the final checkpoint of K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model using Mixture-of-Values (MoVA) attention with 36B total and 4B active parameters. The model supports native 524,288-token context and reportedly outscores open-weight dense and MoE models up to 15x its size on agentic and reasoning benchmarks, while competing against closed frontier models. Intermediate checkpoints, training data, the training recipe, and training code are slated for public release.

Hugging Face trending modelsSep 1, 23:01 UTC · 14d agoModel release

PrivAudit: A Dual-Lens Auditing Framework for Website Privacy Practices under the CCPA

PrivAudit framework audits 998 websites for CCPA compliance, finding stronger disclosures but pervasive, weakly responsive third-party cookie tracking.

PrivAudit is an automated dual-lens auditing framework combining LLM-based analysis of privacy policies grounded in CCPA provisions with automated browser measurements of cookie writes under diverse privacy configurations. Applied to 998 websites, it finds CCPA-subject sites disclose opt-outs, data sharing, and user rights more frequently, yet tracking remains pervasive: 6,392 targeting cookies, 49% third-party writes. Cookies show limited-to-moderate responsiveness to privacy signals and consent choices even when sites claim to honor them. The framework is open-sourced and shared with regulators.

arXiv cs.CRSep 9, 04:31 UTC · 7d agoResearch

Untracked Nightmares: The Threats Hiding Behind Commodity Infrastructure

Unit 42 exposes CL-CRI-1171, a pay-per-install network spreading malware like Insomnia RAT via YouTube channels and SEO poisoning for over two years.

Palo Alto Networks Unit 42 details CL-CRI-1171, a cybercrime cluster operating a pay-per-install (PPI) marketplace that has delivered multiple malware families for at least two years. The group used at least eleven YouTube gaming channels with hundreds of thousands of followers, plus SEO poisoning promoting trojanized software such as a Bluetooth driver and WinDirStat, infecting gamers and corporate endpoints including critical infrastructure and government entities. A single shared loader delivered payloads including Insomnia RAT, ARKTunnel, Docro Hijacker, GCleaner and Socks5Systemz between July 2025 and April 2026, with more than 10,000 distinct loader samples and over 200 rotating C2 domains identified. YouTube terminated the malicious channels after Unit 42 notified the platform.

Palo Alto Unit 42Sep 9, 10:00 UTC · 7d agoMalware in the wild1

How loveholidays is making everyone a builder with Codex

loveholidays uses OpenAI Codex to let business teams build software, shortening the path from ideas to shipped products.

OpenAI published a customer story describing how travel company loveholidays deploys Codex to make software development accessible across business teams. The company says the approach helps non-engineers turn ideas into products faster. The piece is a vendor case study rather than new technical or research output.

OpenAI NewsAug 26, 00:00 UTC · 21d agoAI industry

Supporting independent journalism in Ukraine

OpenAI, AIRPPU, and WAN-IFRA launched a program bringing AI support to Ukrainian news organizations to strengthen independent journalism.

OpenAI is partnering with AIRPPU and WAN-IFRA on a program aimed at helping Ukrainian news organizations strengthen innovation, resilience, and independent journalism. The announcement is a partnership/philanthropic initiative rather than a product or model release.

OpenAI NewsSep 7, 00:00 UTC · 9d agoAI industry

CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation

CARDEA, a vision-language model trained only on public data, matches cardiologists on coronary angiography complexity assessment while exposing auditable bounding-box evidence.

CARDEA is a unified large vision-language model serving as the inference core of an end-to-end coronary angiography pipeline from multi-view videos to study-level diagnosis. It was trained on public datasets through visual alignment, self-distilled Chain-of-Box cold start, and reinforcement learning with verifiable rewards encouraging bounding-box reasoning. It reached 0.91 accuracy on dominance classification under domain shift and 0.90 on complexity assessment, comparable to two interventional cardiologists. RLVR raised zero-shot report generation vessel-severity macro-F1 from 0.513 to 0.686, while supervised imitation alone did not.

Hugging Face daily papersSep 6, 20:00 UTC · 9d agoAI research

Rewiring Democracy Series on The Renovator

Schneier and Sanders link a four-part Rewiring Democracy essay series covering civic AI in Japan, Switzerland, Brazil and Scotland.

Bruce Schneier and Nathan E. Sanders announced links to their Rewiring Democracy essay series on The Renovator. The four parts cover Japan's digital democracy party Team Mirai, Switzerland's public AI model Apertus, Brazil's Open Knowledge civic technologists, and civic AI in Scotland. The post is a brief index without technical details.

Schneier on SecuritySep 1, 09:59 UTC · 15d agoAI policy

Supporting Thailand’s next generation of AI startups

OpenAI and Thailand's MHESI launched an eight-week accelerator for ten health, wellness, and education AI startups.

OpenAI announced a program with Thailand's Ministry of Higher Education, Science, Research and Innovation (MHESI) to support the country's next generation of AI startups. The eight-week accelerator will help ten startups in health, wellness, and education turn AI prototypes into trusted products.

OpenAI NewsAug 28, 02:00 UTC · 19d agoAI industry

ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs

ModaLens image-swap audit shows report availability cuts MedGemma-27B image sensitivity on MIMIC-CXR from 20.94% to 4.26% answer changes.

ModaLens is a paired image-swap audit measuring how report availability affects image sensitivity in report-conditioned medical VLMs. On MedGemma-27B across 3,199 paired MIMIC-CXR cases from 293 patients (14 questions per case), generated answers changed on 4.26% of image-swap trials with the report versus 20.94% without it, a 16.7-point paired difference (95% CI 15.6-17.7). The original prompt with a lowercase first-token readout gave 4.70% versus 17.07%, and the direction replicated in two further model lineages. Labels derived from reports limit conclusions about visual correctness; code, prompts, and run records are publicly released.

Hugging Face daily papersSep 13, 20:00 UTC · 2d agoAI research

[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 · 7h agoModel release

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:56 UTC · 8d agoAI research2

ZDI-26-630: NI LabVIEW VI File Parsing Integer Overflow Information Disclosure Vulnerability

ZDI disclosed CVE-2026-18445, an integer overflow in NI LabVIEW VI file parsing that can disclose sensitive information, rated CVSS 3.3.

The Zero Day Initiative published advisory ZDI-26-630 describing an integer overflow vulnerability in NI LabVIEW's parsing of VI files. Exploitation can disclose sensitive information and requires user interaction, such as visiting a malicious page or opening a malicious file. ZDI assigned the flaw a CVSS rating of 3.3.

ZDI Published AdvisoriesSep 9, 05:00 UTC · 7d agoAdvisory 2 sources CVE-2026-18445

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon WillisonSep 4, 23:59 UTC · 11d agoAI research

What vulnerability prioritization looks like when KEV, EPSS, and CVSS disagree

Cohesity field CISO Joye Purser ranks KEV over EPSS over CVSS and urges 24-72 hour patching of exploited internet-facing systems.

In a Help Net Security interview, Cohesity Global Field CISO Joye Purser lays out a vulnerability prioritization framework that puts active exploitation (KEV) first, then exploit likelihood (EPSS), then technical severity (CVSS), adjusted for asset exposure, business criticality, and compensating controls. She endorses 24-72 hour remediation targets for critical exploited internet-facing vulnerabilities and describes the organizational tradeoffs and emergency procedures needed to hit them. The interview also covers honeypot failure modes when deception systems are over-connected or over-trusted, and budget guidance recommending OT/IT segmentation, phishing-resistant MFA, and tested recovery for a 400-person manufacturer.

Help Net SecurityAug 31, 00:00 UTC · 16d agoIndustry

Sparks Fly: NVIDIA Accelerates Local AI at IFA 2026

NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.

At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.

NVIDIA BlogSep 3, 16:00 UTC · 13d agoAI industry

Show HN: Nari Qwen3-TTS and Qwen3-ASR – High accuracy, low latency and cost

Nari Labs claims top Coval voice AI benchmark rankings with low-latency, low-cost Qwen3-ASR and Qwen3-TTS inference endpoints.

Nari Labs says its Qwen3-ASR Fast endpoint ranks #1 in Coval's time-to-final-segment latency (p50 44 ms) with 3.6% WER at $0.12/hour, behind only AssemblyAI Universal 3.5 Pro on accuracy. Its Qwen3-TTS Fast ranks #2 in time-to-first-audio (p50 63 ms) and #1 in WER at 3.8%, priced at $10 per 1M characters. The company reports beating the official Qwen3 TTS Flash Realtime endpoint (8.8% WER, 692 ms median TTFA) and Baseten's dedicated endpoint (6.0% WER, 101 ms). Public beta APIs are moving to paid general availability with $20 in credits for existing accounts.

Hacker News · securitySep 14, 16:07 UTC · 2d agoAI tools & infra HN 21↑ · 7 comments

OpenAI's Artifactory opened covert data-stealing channel alongside Hugging Face attack

Check Point disclosed a covert cross-account channel in OpenAI's internal JFrog Artifactory that let one ChatGPT session exfiltrate another user's connected-app data.

Check Point Research found that OpenAI's internal JFrog Artifactory instance exposed an item management feature allowing one ChatGPT container to attach hidden, Base64-encoded tasks that another user's session would execute with the victim's privileges, such as pulling Gmail emails and exfiltrating them invisibly. Reader credentials granted both read and write access, enabling cross-account task injection. The flaw was reported in late June, and OpenAI had already decommissioned the Artifactory instance following the related Hugging Face intrusion, closing the channel.

The Register · SecuritySep 8, 21:12 UTC · 7d agoAI safety & security

Mirai Variant V3G4 Targets IoT Devices

Unit 42 tracked the Mirai variant V3G4 exploiting 13 vulnerabilities across IoT devices and servers from July-December 2022 to build a DDoS botnet.

From July to December 2022, Unit 42 observed three campaigns spreading V3G4, a Mirai botnet variant, by exploiting 13 vulnerabilities, including CVE-2022-26134 in Atlassian Confluence and CVE-2019-15107 in Webmin. The campaigns shared the same C2 domains containing the string 8xl9, nearly identical shell script downloaders, and the same XOR keys, suggesting a single threat actor. The malware brute-forces telnet and SSH credentials, terminates rival botnet processes via a stop list, and receives DDoS commands from its C2. Compromised servers and networking devices are absorbed into the botnet for further attacks.

Palo Alto Unit 42Aug 17, 13:05 UTC · Aug 17, 2026Malware in the wildCVE-2012-4869 CVE-2014-9727 CVE-2017-5173+6 CVEs

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research1

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

Citrix adds AI-powered browser activity analysis to SecurAccess

Citrix launched Session Insights for SecurAccess with Chrome Enterprise, using AI to record and analyze browser activity from users and autonomous agents.

Citrix Session Insights adds automatic session recording and AI-powered risk detection for browser activity by human users and autonomous AI agents within Citrix SecurAccess with Chrome Enterprise. The capability creates visual forensic records, highlights risky behavior for faster investigations, and recommends policy adjustments or changes to agent authority levels. It is designed to support audits and governance as enterprise AI agent workflows expand.

Help Net Security10:14 UTC · 8h agoTools

OpenAI banned Russian ChatGPT accounts backing covert influence operation

OpenAI banned Russian-run ChatGPT accounts behind fake think tank IBI, which used AI-generated posts and a 'Sovereignty Index' to push pro-Russia narratives.

OpenAI banned a cluster of ChatGPT accounts, likely operated from Russia via VPNs, that generated English-language social media comments for X, Facebook, LinkedIn, Telegram and Substack promoting the International Burke Institute (IBI). The Israel-branded IBI website, registered in February 2025, claimed ties to figures like Francis Fukuyama, but 34 of 36 sampled articles were copied or misattributed. Its 'Sovereignty Index' consistently ranked Russia favorably while criticizing France, Germany, the EU and the US; OpenAI rated the campaign at the lower end of Brookings Breakout Scale Category Three.

Security AffairsAug 27, 07:52 UTC · 20d agoAI safety & security

IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B

MBZUAI's IFM released K2 Horizon, six Apache 2.0 models (0.9B-375B) with open training data, code, and checkpoints, claiming the largest fully open-source launch.

The Institute of Foundation Models (IFM), launched by MBZUAI, released K2 Horizon: six Apache 2.0 models (0.9B, 3.7B, 7B, 32B, 36B-A4B, 375B-A23B) shipping with the ~20-trillion-token pretraining corpus, intermediate checkpoints, training code, and logs, which IFM calls the largest fully open-source launch in AI history. The 375B-A23B scores 70.2 on Terminal-Bench 2.1 and 87.3 on GPQA Diamond; the 7B model posts 70.6 on SWE-bench Verified. New techniques include MoVA, which extends MoE routing into attention (36B total, ~4B active), and Uno, a LoRA adapter giving roughly 3x lossless decoding speedup. IFM's own reward-hacking audit re-scored 375B-A23B from 70.2% to 66.9% after flagging 24 of 712 Terminal-Bench trials.

MarkTechPostSep 7, 05:00 UTC · 9d agoModel release1

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 papersSep 3, 20:00 UTC · 12d agoAI research1

AMIE, our research medical AI system, demonstrates real-time clinical video consultation capabilities in a first-of-its-kind study.

Google's AMIE research medical AI system demonstrates real-time clinical video consultations in a first-of-its-kind simulated study.

Google introduced AMIE, its research medical AI system, demonstrating real-time clinical video consultation capabilities in a first-of-its-kind study. The evaluation was conducted in simulated settings, extending the AMIE diagnostic dialogue research line to multimodal video consultations. AMIE remains a research system rather than a deployed clinical product.

Google · AIAug 11, 17:00 UTC · Aug 11, 2026AI research

Studying Image Tokenizers as Visual Languages in Unified Multimodal Models

A controlled pure-autoregressive testbed shows task-specific validation losses rank image tokenizers differently, with I2T loss the most consistent signal.

Researchers built a controlled pure-autoregressive testbed and tracked task-specific validation losses during multimodal continual pretraining across text, image, text-to-image (T2I), and image-to-text (I2T) prediction. They find losses should be analyzed per task because they exhibit distinct scaling behavior and rank tokenizers differently, and that the loss-performance relationship depends on the predicted token space. I2T loss, computed over a shared text vocabulary, correlates consistently with both generation and visual understanding performance after supervised finetuning. Case studies revisit the discriminator, semantic supervision, and vocabulary size as tokenizer design axes.

arXiv cs.AI / cs.LG / cs.CLSep 8, 17:57 UTC · 8d agoAI research1

ZDI-26-583: Clam AntiVirus 7z Archive Parsing Integer Overflow Remote Code Execution Vulnerability

Zero Day Initiative discloses CVE-2026-20215, an integer overflow in ClamAV's 7z archive parsing enabling remote code execution, rated CVSS 8.4.

The Zero Day Initiative published ZDI-26-583 for an integer overflow in Clam AntiVirus's 7z archive parsing. A remote attacker can execute arbitrary code when the antivirus processes a crafted archive, with attack vectors varying by implementation. The flaw is tracked as CVE-2026-20215 and rated CVSS 8.4. The advisory does not mention active exploitation.

ZDI Published AdvisoriesAug 13, 05:00 UTC · Aug 13, 2026Vulnerability CVE-2026-202151

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