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12 Best CWPP Solutions Compared (2026): Features & Pricing

Editorial comparison ranks twelve CWPP vendors, with Sysdig leading K8s runtime depth, Prisma Cloud workload breadth, and Wiz agentless speed.

A 2026 buyer's guide compares twelve cloud workload protection platforms across features and pricing models. Sysdig is rated deepest for container/Kubernetes runtime via its Falco lineage, Prisma Cloud broadest across hosts, containers, and serverless, Aqua strongest on cloud-native lifecycle, and Wiz/CrowdStrike lead agentless speed and platform correlation. Category notes flag Illumio as microsegmentation and Fidelis as NDR/XDR rather than classic CWPP.

GBHackers · 1d agoIndustry 2 sources

Turn it off and on again, but for critical infrastructure

KTH researchers trained a reinforcement-learning intrusion response agent on an emulated segmented OT network that autonomously resets hosts and processes to disrupt intruders.

Researchers at KTH Royal Institute of Technology built a containerized replica of a segmented industrial network, attacked it across 14 days, and captured 40,000 30-second traffic intervals to train a defense agent under partial observability. The agent observes six packet-count numbers per interval, maintains 500 running state hypotheses, and can reset supervisory hosts, water tank processes, or entire subnets, with resets rebooting the target, renewing credentials, and changing its IP. The best agent approached a full-visibility baseline but depends on an assumed attacker behavior model; the testbed comprised three supervisory hosts, two PLCs, two tanks, weak credentials, and CVE-2017-7494 exposure. The team released its implementation and plans validation on a real industrial testbed with a partner.

Help Net Security · 2d agoResearchCVE-2017-74942· 1 read

Siemens Reyrolle 7SR5

CISA advisory covers 14 vulnerabilities, CVSS 9.8, in Siemens Reyrolle 7SR5 energy-sector protection relays before V2.70.

CISA advisory ICSA-26-258-05 covers 14 vulnerabilities in Siemens Reyrolle 7SR5 protection relays before V2.70, used in the energy sector worldwide, with aggregate CVSS v3 of 9.8. Flaws include Cesanta Mongoose web server issues (CVE-2024-42384 through CVE-2024-42392) and new bugs such as web-interface session-ID exposure enabling authentication bypass (CVE-2026-62645, CVSS 9.8), predictable session tokens (CVE-2026-62646, CVE-2026-62647), and pre-auth out-of-bounds writes (CVE-2026-62648). Siemens has released V2.70 and recommends updating to the latest version.

CISA Advisories · 19h agoAdvisoryCVE-2024-42384CVE-2024-42385CVE-2024-42386+11 CVEs

Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers · 2d agoAI research

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.

LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.

arXiv cs.AI / cs.LG / cs.CL · 13h 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 · 22h agoAI industry

Top 10 Best Kubernetes Security Tools in 2026

Top 10 Kubernetes security tools roundup highlights Sysdig and Aqua as specialists plus strong free options Kubescape, Calico, and Falco.

A buying guide ranks ten Kubernetes security tools across admission control, network policy, runtime detection, and posture management. Sysdig leads runtime depth, Aqua leads full lifecycle, Red Hat ACS owns OpenShift estates, and Wiz offers agentless graph correlation. The guide notes this category has cloud security's strongest open-source floor with Kubescape, Calico, and Falco.

Cyber Security News · 1d agoIndustry1

Anatomical Grounding and Leakage-Aware Multimodal Contrastive Learning for Alzheimer's Disease Classification from Structural MRI

Study of label leakage and anatomical grounding in multimodal MRI models for Alzheimer's staging shows cognitive-score fusion accuracy of 87.3% is leakage-driven.

The authors train a ResNet18 slice-based encoder with a one-layer Transformer on 1,075 ADNI-1 T1 MRI scans, using FastSurfer segmentations and YOLOv8 localization (mAP_50 above 0.96) as anatomical reference. Grad-CAM shows the image-only classifier often attends to skull and background rather than disease-relevant structures. A CLIP-style image-tabular contrastive framework organized along a label-leakage spectrum yields 87.3% three-way accuracy with cognitive scores versus 73.0% with regional volumes, and cropping to the medial temporal lobe raises image-only accuracy from 58.7% to 65.1%. Results come from single runs on a small balanced test set with reported confidence intervals.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

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 · security · 1d agoAI tools & infraHN 21↑ · 7 comments

ROSETTA: Efficient and Accurate Privacy-Preserving LLM Decoding via Hybrid CKKS/TFHE Evaluation

ROSETTA is a hybrid CKKS/TFHE homomorphic encryption framework for privacy-preserving LLM decoding, achieving up to 4.8x Softmax and 2.1x end-to-end speedups.

The paper proposes ROSETTA, a hybrid CKKS/TFHE fully homomorphic encryption framework for private inference on generative LLMs, targeting the nonlinear operations that dominate autoregressive decoding cost. It introduces an adaptive segmented lookup-table protocol based on TFHE and a scheme-aware operator-selection framework that assigns each nonlinear operator to CKKS or TFHE to minimize latency. Experiments show up to 4.8x Softmax speedup and 1.5-2.1x end-to-end decoding speedup over the state-of-the-art CacheMir framework.

arXiv cs.CR · 21h agoResearch

ENISA: Frontier AI Is Changing the Speed of Cyberattacks. Europe Needs to Catch Up

ENISA warns frontier AI compresses attack lifecycles to minutes, with exploits possible within 15 minutes of disclosure and median 72-minute breach-to-exfiltration times.

ENISA's July 2026 paper 'ENISA's view on Cybersecurity in the Frontier AI Era' argues AI-assisted attackers may weaponize vulnerabilities within 15 minutes of disclosure and achieve initial-access-to-data-exfiltration in a median 72 minutes, creating a 'negative time-to-exploit' problem. The report cites one organisation whose CVE volume rose from roughly 80 in Q1 2025 to almost 500 in Q1 2026, then about 500 reports per day when frontier-AI tools were used. ENISA recommends machine-speed defence under 'Cybersecurity as Code', EPSS and VEX-based prioritisation, AI-assisted incident response with human oversight, and an assume-breached architecture.

Security Affairs · 1d agoAdvisory

Defense Cyber Spending Set to Surge Amid Rising Attacks on Military Systems

MarketsandMarkets projects the cyber warfare market to nearly double from $14.99bn in 2026 to $28.75bn by 2031, driven by attacks on military systems.

A MarketsandMarkets report projects the global cyber warfare market will grow from $14.99bn in 2026 to $28.75bn by 2031. Growth is driven by rising attacks on military networks, reliance on connected platforms and cloud command-and-control systems, and Western investment in offensive cyber capabilities. Europe is projected to hold the largest market share, supported by NATO's shared cyber capabilities and joint exercises. Cloud-based security is expected to be the fastest-growing market segment.

Infosecurity Magazine · 1d agoIndustry

LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows

LynnReal-Omni unifies controllable video generation tasks in a 32B multimodal diffusion transformer, with a 27B Flash variant rendering 540p clips in 377 ms.

LynnReal-Omni is a native multimodal video generation framework built on a 32B shared multimodal diffusion transformer unifying text-to-video, image-conditioned generation, reference guidance, structural control, editing, restoration and long-video generation, accepting heterogeneous inputs like 3D renders and game recordings for agentic visual workflows. A dedicated 27B Flash model enables real-time rendering, producing a 22-frame 540p video in 377 ms on one H100 versus 843 ms for the full model. The work introduces a curated multi-shot audiovisual data pipeline and MSAVP, a 100-prompt, 20-metric evaluation design covering instruction following, plausibility, visual quality, temporal behavior and audio coordination.

Hugging Face daily papers · 2d agoAI research