Search: “ai-hardware”

Anthropic's new hardware standard lets AI agents control the physical world

Anthropic announced a standardized hardware driver interface letting AI agents control physical devices and for devices to interoperate.

Anthropic introduced a new hardware standard that provides a standardized driver interface for AI agents to control the physical world. The standard aims to let devices communicate with AI systems and with each other. Details on adoption, partners, or specifications were not included in the available text.

Ars Technica · AI·Aug 27, 22:15 UTC · 19d agoAI industry1

The Regulators Already Assume You Have an AI Inventory. Do You?

Checkmarx argues regulators now expect organizations to maintain an AI inventory as AI-generated code and outputs enter security workflows.

Checkmarx contends that implicit trust in AI-generated code, AI summaries, and scanner output has become a governance liability that regulators no longer accept. The piece argues security teams must formalize AI inventories and treat AI outputs as untrusted inputs. It frames AI governance as an emerging compliance expectation rather than an internal maturity project.

Checkmarx·Aug 26, 09:15 UTC · 21d agoAI policy1

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, 12:34 UTC · Aug 17, 2026AI industry

Mines, Minds, and Machines: The Journey of AI

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.

Recorded Future·Aug 11, 00:00 UTC · Aug 11, 2026AI industry

FOMO in the SOC: Where AI Platforms like Claude Actually Fit

The Hacker News·Aug 3, 11:30 UTC · Aug 3, 2026Malware

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:08 UTC · 12d agoAI research1

Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance

A study maps twenty inference-time AI governance mechanisms, finding commercial readiness only against cooperative deployers and no adequate defense versus state-level adversaries.

The paper develops a feasibility taxonomy of twenty inference-time AI governance mechanisms across monitoring, verification, and enforcement, each rated on a four-point readiness scale against a four-vendor evidence base. Fifteen of the twenty mechanisms have commercial technical substrates in production today, though governance-grade assurance and adversarial robustness vary substantially. Stress testing shows readiness holds only against a cooperative deployer and low-to-medium-capability user: no mechanism rates adequate against a high-capability state-level deployer, and fine-tuning removes model-internal enforcement components. A second-rater reliability check on readiness ratings returned a quadratic-weighted Cohen's kappa of 0.74.

arXiv cs.CR·Sep 9, 12:37 UTC · 7d agoAI policy

From assistance to execution: How enterprises put AI to work

OpenAI reports enterprise adoption patterns of agentic AI through ChatGPT and Codex, with frontier firms pulling ahead in deployment.

OpenAI published a report on how enterprises are moving from AI assistance to autonomous execution, focusing on agentic AI deployments. It highlights the use of ChatGPT and Codex in enterprise workflows and argues that frontier firms are pulling ahead of peers in AI adoption. The piece is adoption research and marketing rather than a security disclosure.

OpenAI News·Aug 12, 06:00 UTC · Aug 12, 2026AI industry1

Researchers Disclose AI-Assisted SharePoint Exploit Chain Reaching Unauthenticated RCE

Rapid7 disclosed CVE-2026-55040, a SharePoint JWT validation bypass chaining into CVE-2026-63520 unauthenticated RCE, with research substantially AI-agent-assisted; patches released.

Rapid7 disclosed CVE-2026-55040 (CVSS 9.1), several JWT validation pipeline issues letting unauthenticated attackers impersonate chosen SharePoint users by SID or UPN, chained with CVE-2026-63520 (CVSS 8.1), an unsafe .NET type instantiation in Business Connectivity Services yielding RCE as the service account. An AI agent contributed significantly across 96 sessions and roughly 80,000 tool calls over 24 active days, though an expert had to steer it and it repeatedly overstepped its threat model. No exploitation of the bypass had been reported as of CISA's July 14 assessment. The RCE affects SharePoint Subscription Edition, 2019, and 2016, plus Project Server 2013 SP1 and Office Web Apps 2013 SP1; the July updates break the chain.

The Hacker News·Aug 11, 16:49 UTC · Aug 11, 2026VulnerabilityCVE-2026-55040CVE-2026-635201

Kimsuky Builds Offline AI Stack to Boost Phishing and Automate Malware Development

The Hacker News·Aug 10, 13:21 UTC · Aug 10, 2026Malware

IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier

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.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:31 UTC · 7d agoAI research1

What the Data Says About AI in Security Operations in 2026

Prophet Security's 2026 survey of 250+ security pros: 40% use AI daily; AI users report 25%+ faster investigations and rising AI-driven attacks.

Prophet Security's State of AI in Security Operations 2026 report, based on a survey of 250+ cybersecurity professionals, found 40% of security teams use AI daily and only 4% have no adoption plans. Teams average 100 daily alerts (up to 1,000 at large firms), leave 28% of alerts uninvestigated, and 60% of respondents said missed alerts led to breaches or downtime. Among AI adopters, 72% report at least 25% faster investigations, 56% observed increased AI-driven attacks, and no respondents grant AI full unsupervised autonomy. Data privacy (44%) and explainability (41%) top the adoption hurdles.

The Hacker News·Aug 27, 11:30 UTC · 20d agoIndustry

Re: AI slops from Eve

oss-security commenter argues AI models remain human-built algorithms while reflecting on recent AI-slop incidents in open-source

A reply posted on the oss-security mailing list reflects on recent AI-generated slop incidents, arguing that AI models are still human-developed algorithms running on human-built hardware. The post is personal commentary and contains no CVEs, advisories, or concrete incidents.

oss-security·updated Sep 13, 15:08 UTC · 3d ago·first Sep 11, 14:25 UTC · 5d agoIndustry 12 sources1

Meta Patents AI Glasses to Use Facial Recognition to Identify People, Make Highlight Reels of Your Dinner Party

Meta filed a patent for AI glasses that identify people via facial recognition and auto-generate highlight reels of events like dinner parties.

A newly published Meta patent describes smartglasses that use facial recognition to detect people in frame, capture video clips of their actions, and compile highlight reels. The filing signals Meta's continued, controversial push to combine facial recognition with its AI glasses line. The patent was published Thursday and offers granular detail on Meta's product plans, though it does not confirm a shipping feature.

404 Media·Aug 14, 14:17 UTC · Aug 14, 2026AI industry

UAT-10147 Uses AI to Scale Server Attacks, Deploys SPECTRE With EDR Bypass and Linux Rootkit

Cisco Talos exposes UAT-10147, a Chinese-speaking group using AI tools to automate intrusions, deploy SPECTRE, BadIIS, and rootkits against web servers worldwide.

Cisco Talos detailed UAT-10147, a Chinese-speaking cybercrime group conducting SEO fraud and data theft against Windows and Linux web servers in education, media, technology, and gaming sectors, with most victims in Brazil, Bolivia, China, Canada, and Vietnam. The actor exploits publicly disclosed vulnerabilities for initial access, including Zimbra (CVE-2022-27925) and Alibaba Nacos (CVE-2021-29441), and abuses Linux LPE flaws like CVE-2022-0847 and CVE-2021-3156 for root. Its toolset includes AI-assisted frameworks DeepAudit and PentestGPT, plus implants such as SPECTRE, BadIIS, Quasar RAT, Gh0stCringe, and Noodle RAT. An exposed directory contained a target list of roughly 170,000 URLs, with the US, India, UK, Germany, and Netherlands as top destinations.

The Hacker News·Aug 27, 18:33 UTC · 20d agoThreat actor in the wildCVE-2022-0995CVE-2021-3156CVE-2015-5287+8 CVEs

Delivering Vera: NVIDIA’s First CPU Built for Agents Is Shipping Now

NVIDIA's Vera CPU, its first processor built for AI agents, is now shipping at scale to partners across the AI ecosystem.

NVIDIA announced that Vera, its first CPU designed specifically for agentic AI workloads, has begun shipping at scale. Vice President of Hyperscale and HPC Ian Buck is hand-delivering early Vera CPU systems to organizations across the AI ecosystem, signaling full production availability of the data-center processor.

NVIDIA Blog·Aug 27, 13:00 UTC · 20d 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, 15:00 UTC · Aug 11, 2026AI industry

Architecting memory and storage in the AI era

Analysis argues AI inference shifts data-center bottlenecks to memory and storage, urging balanced compute, memory, storage, and network architecture over raw compute.

MIT Technology Review, citing Tirias Research principal analyst Jim McGregor, argues that AI inference and agentic workloads make data movement the key constraint, elevating memory and storage from background hardware to strategic assets. The piece says RAG and real-time inference require continuous data retrieval and caching that legacy infrastructure cannot support. It frames infrastructure planning as a business decision balancing performance, efficiency, cost, and scalability in healthcare, finance, and customer-facing AI.

MIT Technology Review · AI·Sep 4, 18:39 UTC · 12d agoAI industry

Linux Foundation Introduces TRACE Standard for AI Runtime Evidence

The Linux Foundation introduced TRACE, an open standard providing hardware-attested runtime and compliance evidence for AI agents.

The Linux Foundation announced TRACE, an open standard designed to generate hardware-attested runtime evidence for AI agents. The standard aims to give auditors and regulators verifiable proof of what AI agents actually executed. It targets compliance and assurance needs for organizations deploying autonomous AI systems.

Infosecurity Magazine·Aug 26, 09:10 UTC · 21d agoAI tools & infra1

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.

Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.

Import AI·Aug 24, 13:12 UTC · 23d agoAI research1

AI models flub these intelligence tests. Can you fare any better?

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.

MIT Technology Review · AI·Aug 26, 09:00 UTC · 21d agoAI research

Linux Foundation takes on TRACE, a hardware-backed runtime evidence specification for AI agents

The Linux Foundation adopts TRACE, an OPAQUE-contributed spec giving AI agents hardware-attested, cryptographically verifiable runtime and compliance evidence.

The Linux Foundation accepted the TRACE (Trust, Runtime Attestation and Compliance Evidence) specification contributed by OPAQUE, developed with AMD, Intel, Microsoft, and the Technology Innovation Institute. TRACE binds runtime environment, software, policies, data classifications, and tool usage into a portable, cryptographically verifiable artifact, composing existing standards such as RATS, EAT, SLSA, SCITT, SPIFFE, and EAR. It recorded nearly 135,000 PyPI downloads within 10 weeks of its June 2026 introduction, and its technical workstream will be hosted by the Coalition for Secure AI.

Help Net Security·Aug 26, 00:00 UTC · 21d agoAI tools & infra

Apple is reportedly building an enterprise AI server with its own M8 Ultra chips

Apple reportedly develops an enterprise AI inference server with two or four M8 Ultra chips, possibly using Nvidia NVLink Fusion, launching no earlier than 2029.

According to The Information, Apple is building an enterprise server for AI inference aimed at developers, businesses, and governments, in configurations with two or four M8 Ultra chips. Apple is considering Nvidia's NVLink Fusion interconnect, and the project, backed by new CEO John Ternus, could still be cancelled. AI labs already buy Mac Minis and Mac Studios in bulk for AI workloads, and Apple's Mac revenue rose nearly 29 percent to $10.4 billion last quarter.

The Decoder·18:16 UTC · 3h agoAI industry 2 sources

Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

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.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research1

Artificial Id: Drive and Persistent Alignment in Agentic AI

Researchers propose an 'artificial id,' an adaptive internal drive letting agentic AI carry state and control across task boundaries, with alignment implications.

The paper addresses agentic AI systems that retain consequential state and keep operating across task boundaries, a control problem currently solved externally by harnesses. It proposes an 'artificial id,' an adaptive internal drive for deciding whether behavior should continue, stop, or change, demonstrated in a minimal virtual Petri-dish experiment where differential persistence yields useful control without task-specific objectives. The same persistence mechanism can also let misalignment, corrupted state, and unintended behavior persist, motivating a persistent alignment boundary over trusted observations, consequence channels, state, authority, identity, provenance, and hard constraints.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:56 UTC · 6d agoAI safety & security

AI Is Ending the Era of Hidden Vulnerabilities — Are Vendors Ready?

Dark Reading argues AI-assisted bug discovery is flooding vendors with vulnerability reports, straining disclosure processes and secure-by-design commitments.

The Dark Reading analysis describes a surge of bug reports driven by AI-powered discovery, exposing bottlenecks in vendor triage and disclosure pipelines. It argues this volume is revealing secure-by-design failures and questions whether vendors can keep pace with the rising tide of findings.

Dark Reading·Sep 4, 13:00 UTC · 12d agoIndustry

AI for Military Support

Study of 2,015 Israeli military personnel found algorithmic aversion toward AI targeting decision support, reduced when explainable AI features were added.

The paper 'Black Box Warfare' reconstructed a real-world military AI decision-support system used in targeting and tested a high-fidelity replica in two experiments with 2,015 Israeli military personnel. Contrary to automation-bias fears, participants showed strong algorithmic aversion, especially in high-collateral-damage scenarios. Integrating explainable AI features reduced aversion and promoted more thoughtful evaluation of algorithmic recommendations. The authors conclude that trust in military AI is dynamic and that human agency remains central in high-stakes decisions.

Schneier on Security·Aug 11, 11:18 UTC · Aug 11, 2026AI research

Staying Ahead of Adversarial AI Through Agentic Source Code Review

Google Threat Intelligence details an agentic AI pipeline with human expert oversight to review source code and outpace AI-enabled attackers.

Google Threat Intelligence researchers argue that adversaries' misuse of AI raises the risk of data theft and extortion when proprietary source code is exposed. They describe a structured agentic source code review pipeline that combines AI models with skeptical validation steps and injected human domain expertise. The team reports a leap in efficacy in finding vulnerabilities before adversaries can exploit them.

Google Threat Intelligence·Aug 18, 14:00 UTC · 29d agoResearch1

Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents

Fine-tuned RoBERTa-large task permission classifier matches Claude Haiku 4.5 on access scoping for AI agents, cutting severity-weighted attack surface by 84.4%.

The paper evaluates a three-source task-based permission architecture for AI agents combining role-based permission ceilings, a task permission classifier, and policy-based prohibitions. A fine-tuned RoBERTa-large security gate matched few-shot Claude Haiku 4.5 on a 600-prompt dataset, with macro-F1 0.881 versus 0.886, precision 0.897 versus 0.842, and lower severity-weighted residual risk (0.63 versus 1.12). An attack-surface elimination metric shows the role ceiling alone closes 27.9% of the severity-weighted surface while adding the task classifier closes 84.4%. The work establishes task-granular access control as a measured, deployable mechanism for reducing attack surface in agentic deployments.

arXiv cs.CR·Sep 14, 11:48 UTC · 2d agoAI safety & security