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GPUThor: Amplifying Rowhammer Attacks via Non-Uniform Patterns to Exploit ECC-Protected GPUs

GPUThor uses non-uniform hammering to amplify Rowhammer on NVIDIA GPUs, achieving 500X-23,500X more bit flips and first exploits of ECC-protected GPUs.

GPUThor is a Rowhammer attack on NVIDIA GPUs that reverse-engineers memory-access coalescing behavior to enable non-uniform hammering patterns activating aggressor rows more intensely than decoy rows. By identifying refresh instances where in-DRAM mitigations apply, it constructs longer patterns that escape mitigation across refresh intervals. It yields 500X to 23,500X more bit flips than prior GPU Rowhammer attacks across NVIDIA A4000, A4500, A5000, and A6000 GPUs, and enables the first Rowhammer exploits on ECC-protected GPUs via uncorrectable double and triple bit flips, making denial-of-service and privilege-escalation attacks practical.

arXiv cs.CR · 1d agoResearch

CUDA for AMD on Windows

GitHub project 'CUDA for AMD on Windows' trends on Hacker News, aiming to run CUDA workloads on AMD GPUs under Windows.

A GitHub repository named CUDA-for-AMD-Windows drew 45 points and 5 comments on Hacker News. The project targets making NVIDIA's CUDA software stack work on AMD GPUs under Windows, which matters to developers running GPU compute or inference on non-NVIDIA hardware. No security impact is described.

NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.

NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.

MarkTechPost · 5d agoAI tools & infra1

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPost · 16h agoAI tools & infra

Towards Standardized Evaluation of GPU Memory Safety with GMSBench

GMSBench provides 149 CUDA tests covering spatial, temporal, and concurrency GPU memory errors, exposing detection gaps in Compute Sanitizer.

GMSBench is a GPU memory safety benchmark comprising 149 self-contained CUDA tests spanning spatial, temporal, and concurrency errors across different GPU memory spaces and execution scenarios. The authors evaluate NVIDIA's Compute Sanitizer across multiple GPU architectures using the suite, exposing gaps in its detection coverage. The benchmark offers a standardized foundation for comparative evaluation of GPU memory safety mechanisms.

arXiv cs.CR · 7d agoResearch

Arm Mali G2-Ultra NX GPU: desktop-class mobile gameplay with AI-native graphics

Arm unveiled Mali G2-Ultra NX, its first AI-native mobile GPU with in-shader neural acceleration, third-gen ray tracing, and up to 24% higher benchmark performance.

Arm announced the Mali G2-Ultra NX, the first AI-native Mali GPU, integrating neural accelerators directly into shader cores alongside a new execution engine and third-generation hardware ray tracing. It introduces Neural Super Sampling (NSS), Neural Frame Rate Upscaling (NFRU), and Neural Super Sampling and Denoising (NSSD); the Neural Dawn demo with Sumo Digital showed up to 4x performance efficiency and 70% lower external memory traffic versus native rendering. Arm claims up to 24% higher benchmark performance, 13% lower DRAM traffic on ray tracing benchmarks, and up to 120 FPS with NFRU. Over 14 billion Mali GPUs have shipped to date.

Chipmaker Patch Tuesday: Nvidia, AMD, Arm Issue Security Advisories

AMD, Arm, and Nvidia issued Patch Tuesday advisories fixing a Linux GPU driver DoS flaw, nine Mali GPU vulnerabilities, and two high-severity Triton defects.

AMD fixed CVE-2026-43603, a NULL pointer dereference in its Linux GPU kernel driver that can crash systems and cause denial-of-service, credited to SecMate researchers, with patches for EPYC, Ryzen, Radeon, and Instinct shipped in July and embedded variants due in October. Arm published an advisory covering nine Mali GPU vulnerabilities allowing use-after-free access, kernel information leaks, or DoS, releasing fixes for Valhall and 5th Gen GPU Architecture drivers, with Bifrost also affected. Nvidia updated Triton Inference Server for Linux to resolve two high-severity flaws, one causing DoS and one enabling information disclosure, data tampering, and DoS. Intel had issued no new advisories since the previous Patch Tuesday.

SecurityWeek · 6d agoAdvisoryCVE-2026-43603

New GPUThor Rowhammer Defeats ECC on NVIDIA RTX A6000 to Gain Host Root Access

University of Toronto researchers present GPUThor, a Rowhammer attack that defeats ECC on NVIDIA RTX A4000-A6000 GDDR6 GPUs and achieves host root access.

GPUThor uses non-uniform hammering to bypass Target Row Refresh and overcome SECDED ECC on NVIDIA Ampere workstation GPUs with GDDR6 memory, inducing 72,000 to 377,552 bit flips per gigabyte across RTX A6000, A5000, A4500, and A4000 cards. Triple-bit silent data corruption enables host privilege escalation to root with the IOMMU enabled, reusing GPUBreach page-table corruption techniques, and double-bit DUEs allow escalation when the IOMMU is disabled during a ~10 ms lazy-service window. The attack cut end-to-end escalation time on the A6000 from 21.9 hours to 1.1 minutes. Reported to NVIDIA, Google, Microsoft, and AWS on April 29, 2026; findings were embargoed until August 25, 2026, and no CVE identifier was assigned.

The Hacker News · 7d agoResearch1

GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay

GPU-CFR compiles counterfactual regret minimization into static dataflow with CUDA Graph Replay, achieving 29.8-80.4x speedups over prior GPU solvers.

The paper presents a compiler and runtime that turns any fixed game's CFR iteration into a static dataflow graph of flat arrays and precomputed indices, cutting framework operations by up to 18.1x. Because shapes and buffer addresses never change, CUDA Graph Replay records the iteration once and replays it with a single launch. On one A100 across an eight-game suite, GPU-CFR runs 29.8-80.4x faster than the fastest prior GPU CFR and 14-258x faster than the CPU implementation LiteEFG on the four largest games, while reproducing reference iterates bitwise on CPU.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research3· 1 read

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 Blog · 12d agoAI industry

Why Scaling AI Compute Performance Requires a New Power Architecture

NVIDIA argues AI factories need 800 VDC power distribution as dense GPU racks outgrow traditional AC-based delivery.

NVIDIA's blog contends each generation of accelerated computing demands higher rack density and more efficient, scalable power distribution. It frames the bottleneck as how power moves from the grid to the GPU rather than raw wattage, and describes limitations of traditional AC power delivery. NVIDIA advocates a new 800 VDC power architecture for AI factories.

NVIDIA Blog · Aug 11, 2026AI industry

Opaque recurrence, and other AI terms that you should probably know

TechCrunch updates its plain-English glossary defining common AI terms from AGI and agents to chain-of-thought reasoning.

TechCrunch maintains a regularly updated glossary of AI terminology, defining terms such as AGI, AI agents, API endpoints, chain of thought, coding agents, compute, deep learning, and diffusion. It highlights 'opaque recurrence', the reasoning technique in OpenAI's new Astra model that has drawn attention from AI safety researchers. The piece is an educational living document rather than new research or a product announcement.

TechCrunch · AI · 8d agoAI industry

Building a Linux GPU Driver for the M4 Mac Mini in One Month

Two developers built a fully OpenGL ES 3.0 compliant Linux GPU driver for the M4 Mac Mini in one month via clean-room reverse engineering.

Niklas and the author reverse engineered Apple's AGX GPU firmware ABI and user-space components in about a month, a process that normally takes years, producing an OpenGL ES 3.0 conformant driver fast enough to run Minecraft at 200fps on an M4 Mac Mini. The work was done transparently using hypervisor traces without examining Apple binaries, following clean-room practices, and included a custom shader compiler, command stream builder, and a full Linux kernel driver for the firmware ABI. The A18 Pro firmware ABI proved significantly more complex than the M1's, with 1.5x as many structs and twice as many pointers. All experiments and provenance evidence were published in public agx-re repositories.

Axis Robotics Releases AXIS: A Browser-Based Data Engine With 207 Robot Manipulation Tasks and 50,129 Trajectories

Axis Robotics and academic partners released AXIS, a browser-based teleoperation system yielding 207 manipulation tasks and 50,129 trajectories that lifts pi0.5 to 88.8 on LIBERO-Plus.

A team from Axis Robotics, UC Berkeley, Georgia Tech, and NTU introduced AXIS, a browser-based data engine where contributors teleoperate a simulated Franka Research 3 in a MuJoCo WebAssembly frontend while GPU backends handle task generation, training, and evaluation. The released snapshot holds 207 tasks, 50,129 episodes, and 60K+ task or scene variants from more than 70,000 community contributors. Continual pretraining of pi0.5 on AXIS data raises LIBERO-Plus performance from 83.9 to 88.8, versus 57.5 for a volume-matched RoboCasa365 control; the 2.36 TB dataset is gated for non-commercial academic use.

MarkTechPost · 8d 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

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

GPT-6 Astra, Looped Transformers, and Hidden Reasoning

OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.

Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.

Hacker News · AIupdated · 3d agofirst · 6d agoModel release 11 sourcesHN 84↑ · 12 comments2

How XPUs Meet a World-Class AI Factory

NVIDIA argues AI factories with custom XPUs and NVLink Fusion connectivity must optimize tokens-per-second, tokens-per-watt, cost and uptime.

NVIDIA published a blog explaining that AI factories running continuously are economically defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. It argues hyperscalers and AI-native companies building custom XPUs need infrastructure designed as a complete factory rather than collections of individual accelerators. The piece promotes NVIDIA's NVLink Fusion and full-stack XPU connectivity as the foundation for such world-class AI factory builds.

NVIDIA Blog · 22d agoAI industry

AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories

At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.

Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.

NVIDIA Blog · 21h agoAI industry

[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.

Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.

Latent Space · 25d agoAI industry

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.

The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.

MarkTechPost · 3d agoAI tools & infra

Jensen Huang explains why Nvidia will grow an astounding 70% next year

Nvidia CEO Jensen Huang reiterated at a Goldman Sachs conference that revenue could grow 70% year-over-year next year, reaching roughly $680 billion.

Speaking at the Goldman Sachs Communicopia + Technology conference, Huang reaffirmed guidance of about 70% revenue growth next year, implying roughly $680 billion after an expected ~$400 billion this fiscal year. He cited the Grace-Blackwell system (36 Grace CPUs with 72 Blackwell GPUs) experiencing 27% month-over-month order growth and claimed $100 billion in revenue-generating contracts at companies Nvidia invests in. Huang argued Nvidia underpins models from OpenAI, Anthropic, and Google and tracks global data center capacity, while dismissing concerns about circular deals and competition from hyperscalers, Cerebras, and Etched.

TechCrunch · AI · 5d agoAI industry

NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels

NVIDIA launches CUDA Rust via open-source cuda-oxide (SIMT) and cutile-rs (Tile), bringing compile-time-safe Rust GPU kernels.

NVIDIA announced CUDA Rust, making Rust a first-class language for GPU kernels through two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the Tile model. Both use Rust's ownership and borrow checker to catch buffer aliasing bugs at compile time. cutile-rs is published on crates.io, runs on stable Rust 1.89+ with CUDA 13.3, and is already used in Hugging Face's Grout inference engine and mistral.rs; cuda-oxide is early alpha requiring nightly Rust, CUDA 12.x, and compute capability 8.0+. cuda-oxide compiles Rust MIR through the community Pliron IR framework and LLVM to PTX, while cutile-rs JIT-compiles kernels via CUDA Tile IR.

MarkTechPost · 7d agoAI tools & infra2

Android Security Update September 2026 – Fix for Critical Flaws that Enable RCE Attacks

Google's September 2026 Android bulletin fixes over 30 critical flaws, including no-interaction system RCEs, a TIPC kernel RCE and a Qualcomm closed-source bug

Google's Android Security Bulletin for September 2026 (patch levels 2026-09-01 and 2026-09-05) fixes numerous critical System remote code execution flaws, including CVE-2026-28604, CVE-2026-28618, CVE-2026-28639, CVE-2026-28662, CVE-2026-49882, CVE-2026-49884, CVE-2026-49919 and CVE-2026-49921, none requiring user interaction or additional privileges. It also addresses critical kernel issues including a TIPC RCE (CVE-2026-52993) and elevation-of-privilege flaws in NFC and protected KVM, plus a critical Qualcomm closed-source component flaw (CVE-2026-25289). Affected versions span Android 14 through 17; the 2026-09-05 patch level extends coverage to Android TV and chipset components, with high-severity fixes for Arm Mali, PowerVR, MediaTek, Unisoc and Qualcomm components.

Nvidia is the central bank of AI

The Economist's interactive briefing argues Nvidia functions as AI's central bank, controlling the compute supply underpinning the industry's growth.

An Economist interactive briefing titled 'Nvidia is the central bank of AI' examines the chipmaker's dominant control over AI compute supply. The link drew 45 points and 23 comments on Hacker News. The listing provides only engagement metrics, with the full analysis hosted on the Economist's site.

What must happen for AI’s trillion-dollar gamble to pay off

Hyperscalers need 2.7x productivity gains by 2030 to justify nearly $1.1 trillion in AI data center spending, or risk bankruptcy and capital misallocation.

Wharton finance professor Jessica Wachter estimates hyperscaler AI expenditure will reach nearly $1.1 trillion through 2027 and that a 2.7x productivity increase is needed to break even by 2030. AI revenues of roughly $150-200 billion this year fall far short of about $750 billion in annual spending, with total investment from Alphabet, Microsoft, Amazon, Meta, and Oracle potentially exceeding $5 trillion over four years. Alphabet reported its first free cash flow deficit (about $5.9 billion) since its 2004 IPO due to AI infrastructure costs. Researchers warn that failed demand could make the buildout the largest capital misallocation in history, with depreciating GPU chips risking stranded assets.

MIT Technology Review · AI · 1d agoAI industry

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.

Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.

MarkTechPost · 10d agoAI tools & infra1

Class Is in Session: GeForce NOW Levels Up Linux, Chromebooks and More

NVIDIA's GeForce NOW cloud gaming service ships its native Linux app out of beta and adds streaming optimizations for Frame Generation responsiveness.

NVIDIA announced that the native Linux application for its GeForce NOW cloud gaming service is officially out of beta. The update also delivers cloud optimizations that make Frame Generation feel more responsive while streaming, and Performance members will see higher frame rates. The announcement is timed for the back-to-school season and also mentions improvements for Chromebooks.

NVIDIA Blog · Aug 13, 2026AI industry

ukisai/Swift-Qwen3.8-27b — new model trending #30 on Hugging Face

UkisAI releases Swift-Qwen3.8-27B, a Qwen3.8-27B derivative using 58.3% fewer thinking tokens with <1% performance loss and ~1.95x speed-up.

UkisAI released Swift-Qwen3.8-27B, a reasoning-efficient derivative of Qwen3.8-27B that cuts thinking-token usage by 58.3% while staying within 1% of base performance, yielding a 1.95x speed-up on several tasks. The model was fine-tuned by penalizing reasoning-marker tokens that trigger overthinking, plus a transfer component from BottleCap AI's ThinkingCap-Qwen3.6-27B. Benchmarks include GPQA-Diamond 88.28% (base 88.38%), MMLU-Pro 84.95% (base 85.47%), and AIME 2026 94.00% (base 98.67%), with mean-token reductions of roughly 27-46% across tests. GGUF weights are available on Hugging Face alongside enterprise licensing options.

Hugging Face trending models · 8d agoModel release

Miles v0.1: Production-Level Post-Training

Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.

Miles v0.1 is a full-stack, open-source system for frontier-scale reinforcement-learning post-training, built on slime with rollout engines on SGLang and trainers supporting NVIDIA Megatron-LM and PyTorch FSDP backends plus three weight-synchronization transports. It supports full-parameter RL, LoRA RL, on-policy distillation, supervised fine-tuning, true-on-policy rollout-training alignment, and extends to diffusion models. The end-to-end case study ran fully asynchronous agentic RL on GLM-5.2 744B-A40B for terminal-use coding tasks on 64 NVIDIA GB300 GPUs with a median step time of 263 seconds over the first 30 measured steps. The code is open-sourced on GitHub.

Hugging Face daily papers · 8d agoAI tools & infra

Attackers Breached JetBrains Cadence via Unpatched TeamCity, Extracting AWS Credentials

Unknown attackers exploited critical TeamCity flaw CVE-2026-63077 to breach JetBrains Cadence, stealing a 2024 backup, user data, and AWS credentials; rotation urged.

JetBrains disclosed that threat actors exploited CVE-2026-63077 (CVSS 9.8), a TeamCity deserialization vulnerability added to CISA's KEV catalog on August 5, 2026, to breach its Cadence cloud computing service, with the intrusion running August 8-24, 2026. Confirmed access includes personal data (usernames, names, emails, login timestamps, IPs), a full 2024 Cadence server backup containing credentials and configuration, multiple AWS IAM users and secrets, files in JetBrains S3 buckets, and possibly PyCharm-synchronized source code. The compromised api.cadence.jetbrains.com server has been taken offline and all Cadence plugin access tokens invalidated. JetBrains urges users to immediately revoke or rotate all credentials used with Cadence and treat all executions, inputs, and outputs as potentially untrusted.

The Hacker News · 10d agoData breach in the wildCVE-2026-63077

🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Caltech professor Anima Anandkumar discusses Neural Operators and FourCastNet for physics modeling, arguing inductive biases beat pure token scaling.

Anima Anandkumar, Bren Professor at Caltech and co-founder of Accelerated Understanding, describes Fourier Neural Operators that learn in frequency and spherical-harmonic domains to model weather, fusion, and fluid or heat flow. Her team built FourCastNet 3, a global weather model competitive with physics-based simulations that runs on consumer-grade GPUs. She also introduced TorchLean, a framework for writing PyTorch-style networks inside the Lean proof assistant for formal verification, and was appointed to the United Nations Scientific Advisory Board. She argues physical domains resist scaling due to tiny datasets and context lengths in the hundreds of billions, so progress comes from built-in structure and physical priors.

Latent Space · 20d agoAI research1

Leading Publishers Bring Blockbuster PC Games and Technology to NVIDIA RTX Spark

NVIDIA announces EA, Embark, and Ubisoft titles plus anti-cheat and visual upgrades for RTX Spark ahead of launch at Gamescom.

NVIDIA is promoting RTX Spark at Gamescom in Cologne, Germany, with Electronic Arts, Embark, and Ubisoft bringing blockbuster PC titles to the platform ahead of its launch. The announcement covers new game support, anti-cheat technologies, and increased visual quality. The piece is a product-marketing update rather than security research or AI safety news.

NVIDIA Blog · 21d agoAI industry

New DDRop Attack Breaks Intel TDX and AMD SEV-SNP Confidential Computing

DDRop uses a sub-$200 DDR5 interposer to drop memory writes, breaking Intel TDX and AMD SEV-SNP confidentiality guarantees.

Researchers at KU Leuven, ETH Zurich, Durham University, and Google will present DDRop at ACM CCS 2026, a first active interposer attack on DDR5 that silently drops memory writes so processors keep reading stale encrypted data, exploiting the missing freshness guarantee in Intel TDX, Intel Scalable SGX, and AMD SEV-SNP. On Intel TDX's default logical integrity mode it enabled reading victim VM memory, toggling debug mode, and forging remote attestation; AMD SEV-SNP was limited to copying pages between VMs. TDX's stronger cryptographic integrity blocks cross-VM attacks but likely not attestation forgery. The team will release board designs, firmware, and attack code on GitHub; no evidence of real-world use exists and no simple patch is available.

The Hacker Newsupdated · 1d agofirst · 1d agoResearch 2 sources

27.5KB language-agnostic WebGPU syntax highlighter

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

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

What 90 days and a small budget can buy in AI agent security

Versa Field CISO details hidden costs of self-hosting open-weight models and a 90-day AI agent security plan of inventory, blast-radius reduction and testing.

In a Help Net Security interview, Prasad Tharippala, Field CISO at Versa, argues running open-weight models in-house improves control but shifts hardening, patching, access control, monitoring and incident response onto the buyer, with underestimated costs in GPU infrastructure, licensing review, EU AI Act compliance and scarce AI/ML security skills. On red-teaming AI agents, he recommends testing prompt injection, indirect injection, excessive permissions, data leakage, memory and RAG poisoning, malicious tool outputs, cross-agent trust abuse and infrastructure attack paths, mapped to OWASP agentic guidance and MITRE ATLAS. He highlights the handoff between chained agents as a major risk zone and stresses exercising human approval, shutdown and rollback controls under test conditions. For teams with 90 days and small budgets, he ranks inventory, blast radius reduction and ongoing testing as the priority order.

Help Net Security · 19d agoAI safety & security

From Megawatts to Tokens: How NVIDIA Maximizes AI Factory Production

NVIDIA detailed DSX power-management results: Lambda gained 24% token throughput at fixed power, and an AI factory auto-shed 1MW via Emerald AI's grid program.

NVIDIA says Lambda's first validation of DSX MaxLPS on HGX B200 servers ran 19 nodes within a 16-node power budget, lifting cluster token throughput 24% (roughly 4M to 5M tokens/second) and improving performance per watt by 23%. NVIDIA projects DSX MaxLPS can enable up to 40% more GPU capacity for Vera Rubin NVL72 factories within the same megawatt budget. Emerald AI's Conductor platform, running at NVIDIA's Eos factory with Silicon Valley Power, responded to over 200 utility demand signals, automatically dropping power from 4MW to 3MW without interrupting priority workloads. The first dedicated DSX Flex commercial deployment is planned at a 96-megawatt Manassas, Virginia facility.

NVIDIA Blog · 20h agoAI industry

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.CL · 21h agoAI research

Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care

Children's Hospital of Philadelphia uses NVIDIA open-source MONAI, Warp and Newton to build pediatric heart models in seconds for surgical planning.

CHOP's cardiac modeling service uses MONAI, Auto3DSeg and SlicerHeart to turn CT, MRI and 3D ultrasound images into anatomically precise heart models in seconds instead of four hours of manual work. More than 20 US children's hospitals run similar programs, with Boston Children's supporting roughly 500 cardiac surgery cases a year. NVIDIA's Newton physics engine, built on the Warp Python framework, aims to reduce device simulations from hours to near real time in clinical workflows.

NVIDIA Blog · 1d agoAI industry

Securing the Infrastructure of Intelligence

NVIDIA positions AI factories combining chips, networking, power and data as the defining infrastructure of the AI economy needing full-stack security.

NVIDIA's blog argues that AI factories are the defining infrastructure of the AI era, transforming energy and data into intelligence that powers businesses and countries. It frames compute as revenue and lists the full stack of critical resources required: advanced chips, packaging, memory, networking, land and power. The piece is a corporate positioning article about securing this infrastructure, with no specific incident or product announcement detailed in the excerpt.

NVIDIA Blog · Aug 17, 2026AI industry