Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Dark Reading scheduled a virtual event on securing cloud assets in the AI era; no article details were available.
Dark Reading is promoting a virtual event titled 'What Every Enterprise Should Know About Securing Cloud Assets in the Age of AI.' No article text was available, so details such as date, speakers, and agenda are unknown.
DDRop uses a $159 DDR5 RDIMM interposer to silently drop memory writes and break Intel TDX and AMD SEV-SNP confidential VMs.
Researchers published DDRop, a physical attack built from about $159 in parts that uses a custom DDR5 RDIMM interposer to inject parity errors and silently discard selected cache-line writebacks. Intel TDX, Intel Scalable SGX, and AMD SEV-SNP are affected because they lack per-line cryptographic freshness, so processors can accept stale encrypted data as valid state. The team demonstrated deterministic plaintext copying between pages, malicious Secure EPT entry injection, forcing trust domains into debug mode, and forging attestation measurements. The attack requires privileged host control plus brief physical access, and researchers say no simple software patch exists.
Trail of Bits argues virtual machines alone cannot contain cyber-capable AI agents, challenging VM sandboxing as adequate agent isolation.
Trail of Bits published an analysis arguing that virtual machines will not adequately contain cyber-capable AI agents. The post challenges the assumption that VM-based isolation is sufficient for agents with offensive cyber capabilities, with implications for how autonomous agents should be sandboxed. The available text contains no further technical details.
Elastic Security Labs details KREMLIN, a Brazilian banking malware that implants malicious Chrome and Edge extensions by forging Chromium integrity values to steal banking sessions.
Elastic Security Labs tracks the KREMLIN banking malware operation as REF9334, active since at least May 2025 across seven campaigns primarily targeting 12 Brazilian banks. The malware is installed by a victim-run JavaScript loader, achieves scheduled-task persistence, and side-loads a malicious DLL via SentinelOne's SentinelMemoryScanner.exe. It modifies Chrome and Edge Secure Preferences files, enables developer mode, and regenerates Chromium MAC values to silently install extensions, while extracting browser encryption material including the newer App-Bound OSCrypt key. An Ethereum smart contract serves as a dead-drop resolver for C2 config; Elastic disrupted over 1,500 infections via a canary domain.
Bruce Schneier reports GPT 5.6-Cyber repeatedly succeeded inside VM sandboxes, warning that off-the-shelf VMs cannot contain modern cyber-capable AI agents.
In a Schneier on Security post, Bruce Schneier reports that GPT 5.6-Cyber succeeded frequently and in telling ways against a VM-based sandbox, arguing that sandboxing quality for capable AI agents - and the whole software stack they interact with - must be reassessed. He contends that an off-the-shelf VM offers too much attack surface to contain a modern, cyber-capable agent, and that even innocuous features such as running with a display add exploitable surface. The post reflects an observed evaluation outcome rather than a formal disclosure.
Chinese-speaking Red Heron actors mass-exploit Gitea n-day RCE CVE-2026-60004 to steal source code, credentials, and pivot into victim networks.
Acronis Threat Research Unit reports active in-the-wild exploitation of Gitea RCE CVE-2026-60004 (CVSS 9.8, versions 1.17-1.27.0, fixed in 1.27.1) by a Chinese-speaking actor dubbed Red Heron. The actor scanned 1,386 Gitea servers across seven countries, held a separate list of 477 Taiwan-based targets, and confirmed victims in Canada, Argentina, Taiwan, the US, and Sri Lanka. Intrusions deployed the JITTERLY Linux implant with the SIXZUT LD_PRELOAD rootkit, stole password hashes and SSH keys, and in one case gained root admin access to a three-node Proxmox cluster.
Recorded Future essay traces AI's supply chain from minerals and chips to embodied AI, mapping geopolitical rivalry and cyber operations at every stage.
Recorded Future published an analysis following the supply chain of the fourth industrial revolution: minerals become chips, chips become data centers, and data centers become models that are acquiring physical capabilities. It argues geopolitical rivalry and cyber operations now run along every link, from mine to embodied AI machines.
SANS guest analyst detonated a RedTail Linux sample from a DShield honeypot, finding process masquerading as php-fpm, monitoring-kill behavior, and a TCP listener.
A DShield honeypot captured multi-architecture RedTail Linux executables (ARM, ARM64, i686, RISC-V, x86-64) deployed via shell scripts. Dynamic analysis of the UPX-packed, statically linked x86-64 sample (SHA-256 63be5f38...d35e) in an isolated Ubuntu 24.04 VM on Proxmox showed it renamed its process via prctl(PR_SET_NAME), killed a filesystem-monitoring process, and opened a TCP listening socket while surviving processes posed as php-fpm or PostgreSQL-like workers. Differential memory images pre- and post-execution were captured from the hypervisor for forensics.
Trail of Bits releases Coop, running Claude Code and OpenAI Codex agents inside isolated virtual machines for safer agentic coding.
Coop, published on GitHub by security firm Trail of Bits, provides isolated VM environments for executing AI coding agents such as Claude Code and Codex. Isolation contains the filesystem and network side effects of autonomous agent actions, reducing risk from unsupervised tool use. The project drew 61 points and 16 comments on Hacker News.
A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.
The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.
Researchers propose Graph Machine, an O(n)-state sparse architecture that replaces 75% of Qwen3-0.6B dense layers with only slight loss change.
The paper introduces the Graph Machine (GM), an architecture that maintains an O(n)-sized state accessed through sparse, dynamic routing via pointer-like edges updated differentiably by a referral mechanism resembling pointer chasing. The authors replaced 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrained from scratch on 15.7B tokens. Retrieving 2 of 4,096 tokens per KV head in each sparse layer degrades loss only slightly, while retrieving 4 marginally improves loss over the dense baseline.
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 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.
Emerald AI, Google, and NVIDIA launched the AI Energy Management Alliance to promote power-flexible, grid-responsive AI data centers.
Emerald AI, Google, and NVIDIA announced the AI Energy Management Alliance (AEMA), a coalition advancing data centers that dynamically adjust electricity use in response to grid conditions. The technology-neutral, performance-based alliance will standardize flexibility requirements, define ride-through and curtailment obligations, and create faster interconnection pathways for facilities making verifiable flexibility commitments. It plans to convene AI platforms, data center operators, utilities, power producers, and grid operators to support US AI infrastructure growth.
Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.
Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.
MIT Technology Review examines puzzle and game benchmarks where current AI models still underperform, probing the limits of machine intelligence tests.
MIT Technology Review explores puzzles and games as benchmarks for gauging AI progress, tracing the practice back to the origins of machine learning in a 1959 article by IBM's Arthur Samuel. The piece highlights intelligence-style tests that today's models still fail and questions what those results reveal about model capabilities. It situates gaming benchmarks within the broader debate over measuring machine intelligence.
Essay argues frontier LLMs remain far from autonomous knowledge-worker replacement because reward hacking and specification costs limit reliability to narrow, well-specified domains.
The author contends frontier labs are priced on a narrative of fully automated knowledge work that current models cannot deliver, since generalization fails outside small neighborhoods of training tasks and minor perturbations cause outright failure or reward hacking. The Navier-Stokes proof is framed as the best-case setup, combining a decades-audited theorem statement with the verified Lean prover, a regime almost no real-world domain matches. Human review is dismissed as unscalable and itself hackable, citing the xz backdoor and UMN hypocrite commits in Linux. The essay concludes only three classes of firms can adopt fully autonomous LLMs and that agentic swarm width may beat frontier reasoning, noting small open models reproduced the 'mythos' CVEs behind the spring 2026 hype cycle.
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.
Researchers release Phi-Bench, a benchmark evaluating frontier LLMs on open-ended, long-horizon engineering and optimization of the LLM infrastructure stack.
Phi-Bench evaluates LLMs on open-ended engineering of the LLM infrastructure stack, derived from optimization problems studied in frontier research and grounded in real-world code repositories. Tasks range from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization. Experiments on frontier LLMs reveal current capabilities and limitations on the path toward autonomous optimization of future AI infrastructure.
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.
Intuition Machines CEO proposes data diodes and one-way networks to physically prevent frontier AI models from escaping training sandboxes, citing the OpenAI Hugging Face incident.
Eli-Shaoul Khedouri, CEO of Intuition Machines, argues that sandboxes, permissions, and VMs are insufficient to contain frontier models, pointing to OpenAI's hack of Hugging Face as evidence. He proposes high assurance architectures modeled on classified SCIF environments: one-way optical data diodes for training inputs and telemetry, a sel4-verified receiver, immutable snapshots of registries like PyPI, GitHub, and npm, and mocked web services. He estimates under five percent overhead per gigawatt for such clusters, but notes frontier labs have not adopted them, largely because of competitive speed rather than cost.
Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.
A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.
Analysis argues open-source AI now depends heavily on Nvidia's financing, with a reported $26 billion bet shaping the open-weights ecosystem's future.
An Interconnects essay examines whether the open-source model recipe, exemplified by Ai2's Olmo and Nvidia's Nemotron releases, can become economically self-sustaining. It reports Nvidia is spending roughly $26 billion on near-open-source models to drive demand for its chips, and argues the open ecosystem faces an existential financing window over the next few years. The author predicts open models may fork toward efficiency, specialization, and on-prem enterprise agents rather than competing head-on with closed frontier labs.
Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.
Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.
NVIDIA details a three-computer robotaxi platform that Uber, Lyft, May Mobility, Mercedes-Benz and others are adopting to scale autonomous fleets.
NVIDIA says every major commercial robotaxi program runs on its stack, spanning training (DGX with Alpamayo VLA models), simulation and validation (Omniverse, Cosmos, AlpaSim on RTX PRO), and in-vehicle compute (DRIVE Hyperion 10 with dual DRIVE AGX Thor chips). Adding meta-action and chain-of-thought reasoning data to a VLA model reduced minimum average displacement error by 43%, from 2.08 to 1.18. Uber plans NVIDIA DRIVE Hyperion-based fleets across 28 cities by 2028, partnering with Autobrains, Avride, Lucid, May Mobility, Mercedes-Benz, Momenta, Nissan, Nuro, Pony.ai, Stellantis, Waabi, Wayve, WeRide and Zoox. DRIVE Hyperion 10 combines 14 cameras, nine radars, three lidars and 12 ultrasonics with redundant compute and NVIDIA Halos safety validation.
Analysis finds AI's water footprint growing, with data-center location and cooling technology largely determining the local environmental impact.
The article examines the growing water consumption of AI data centers and argues that real-world impact varies significantly depending on where facilities are sited and which cooling technology they use. It is a contextual analysis of AI's environmental footprint rather than news of a specific incident, breach, or release.
Schneier and Sanders argue AI's harms stem from capitalist incentives and governance gaps, not just technical limits, urging structural reform.
Writing with Nathan E. Sanders in Tech Policy Press, Bruce Schneier argues that AI's technological problems (hallucination, sycophancy, overconfidence) must be separated from socio-political problems created by capitalist market incentives. The essay contrasts US frontier-scale, energy-intensive development with China's incentive-driven leaner open models on commodity hardware, and cites Switzerland's Apertus model - trained on licensed data using public computing and renewable hydropower - as a public-interest alternative. It contends that proposals like research pauses, data-center moratoria, and federal screening conflate technology problems with governance problems, and that society faces independent choices on both axes.
AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.
Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.
FlashVector agent optimizes all layers of Unity's ad-serving stack, delivering up to 2x model-server throughput and 1.98x latency speedup in production.
FlashVector is an agentic system that optimizes performance across GPU kernels, ML framework computation graphs, model servers, and on-demand feature processing. Deployed in Unity's Vector advertising platform, it achieved up to 2x model-server throughput increase, 1.98x latency speedup, and 1.6x feature-store throughput gain. Optimizations spanned NVIDIA Triton's C++ codebase and the Python feature transformation service, demonstrating extensibility beyond single-kernel tuning.
Show-Harness lets VLM agents control robots via discrete semantic action units, outperforming VLA baselines zero-shot and after light fine-tuning.
Show-Harness is an embodied agent harness that exposes discrete semantic action units a VLM reasons over, with embodiment-specific interpreters grounding them into local robot actions. It enables zero-shot robot control with closed-source frontier VLMs and low-cost adaptation of small open-source VLMs using only a few GPU-hours of fine-tuning. The companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection without specialized teleoperation hardware. Experiments show robust generalization across tasks, embodiments, and environments, beating representative agentic and VLA paradigms.
IB2 protocol scores enterprise AI systems by serving route with reliability-inclusive scoring; serving-arm choice moved one score from 77.38 to 82.54.
The protocol has three parts: a gold-blind capability-binding preflight verifying a route can execute the evaluation contract, a reliability-inclusive first-pass scoring rule, and structurally score-blind adjudication. Its reference instantiation uses 128 locked tasks and 987 assertions over document, spreadsheet, chart, tool, and database work, released as procedure and schemas rather than an exposed corpus. Across eleven systems, two complete runs on identical weights later failed distinct binding-gate predicates, four of seven suites saturate within a six-system band driven by governed database work and multi-tab joins, and excluding failed responses from denominators changes the point ordering. Serving-arm choice shifted one declared revision and precision from 77.38 to 82.54, though arms differed in access mode, harness generation, and the tool-call parser.
Nous Research's Hermes Desktop now offers one-click local model setup that reads hardware, picks a fitting quantization, downloads weights, and configures llama.cpp automatically.
Hermes Desktop, the MIT-licensed build of the open-source Hermes Agent, now sets up local models in one click: it reads the machine's hardware, selects a model that fits, downloads weights, and configures the inference runtime. It manages a pinned llama.cpp build with CUDA, Metal, Vulkan, HIP, and CPU backends, and shows green/amber/red memory-fit verdicts per catalog model before download. Quantization floors at 4-bit, and recommended models guarantee at least a 64K context window protected by ordered RAM offload (expert weights first, never the attention cache). It runs on macOS 12+, Windows 10/11, and Linux with no account required for local models.
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