Search: “computability”

Anthropic reportedly signs $517 billion in compute deals after Dario Amodei warned rivals about reckless risk

Anthropic signed compute contracts worth up to $517 billion in eleven months, adding 14.8 gigawatts amid the AI capacity race.

According to The Information, Anthropic locked in contracts worth up to $517 billion over eleven months, adding at least 14.8 gigawatts of compute since October 2025 on top of 1–2 gigawatts it already had, and is planning its own data centers. Bloomberg reports Anthropic's annualized revenue topped $65 billion, while OpenAI was above $40 billion as of July and targets 30 gigawatts by 2030. The spending follows Dario Amodei's early-2026 warning that rivals underestimated investment risk, while Sam Altman now cautions about 'unsustainable silliness' from neo-cloud providers.

The Decoder·Sep 7, 18:12 UTC · 8d agoAI industry1

Beyond the Turing threshold: Productive grammars generate essentially undecidable languages

A theoretical paper designs formal grammars that emulate Post's productive sets, generating languages that are provably beyond Turing decidability.

The paper elaborates on Emil Post's productive sets, which are not even semi-computable, and builds formal grammars that emulate their construction over natural numbers. The resulting languages are shown to be essentially undecidable, placing them beyond Turing decidability. This is pure computability and formal language theory with limited direct security relevance.

arXiv cs.CR·Sep 10, 11:20 UTC · 6d agoResearch1

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·Sep 15, 10:00 UTC · 1d agoAI industry

A Chosen Future Can Still Be Rewritten: Causal Writability in Video Models

Study shows video models often learn correct physics but fail to use it; low-dimensional 'causal writability' edits can restore correct motion.

The paper demonstrates 'causal writability' in video generation models: physically correct motion remains available inside the model even when the model outputs incorrect motion. In a red/blue mass oscillation setup, a low-dimensional edit predicted from simple physical variables restores correct fast motion, with a sharp depth boundary marking commitment. Early causal writability predicts which training errors later get corrected, and both writability and closure reproduce in a pretrained 1.3B video model.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:58 UTC · 1d agoAI research

1.1.1.1 now supports post-quantum DNSSEC, all 2,420 bytes of it

Cloudflare's 1.1.1.1 resolver now validates post-quantum DNSSEC signatures using ML-DSA-44, a 2,420-byte NIST algorithm, preparing DNS for post-quantum migration by 2029.

Cloudflare enabled 1.1.1.1 to validate DNSSEC signatures using ML-DSA-44 (algorithm 18), a NIST-standardized post-quantum signature with 1,312-byte public keys and 2,420-byte signatures, roughly 38x larger than ECDSA P-256. The large signatures exceed common DNS-over-UDP limits, forcing fallback to TCP and creating downgrade risks while conventional algorithms must be published alongside. Cloudflare cites the risk of a quantum computer breaking RSA/ECDSA signatures by 2030 and plans full post-quantum security by 2029.

Cloudflare Blog·Sep 10, 13:00 UTC · 6d agoTools1

Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models

Researchers release Explainability Assistant, an open-source conversational XAI tool using LLM function calling, lifting intent-parsing accuracy from 76.8% to 94%.

The paper introduces the Explainability Assistant, an open-source conversational XAI system for interpreting energy consumption forecasting models such as genetic-programming symbolic regressors. It uses LLM function calling instead of rigid custom grammars, achieving 94% intent-parsing accuracy versus 76.8% for prior work TalkToModel, and adapts to different ML problem types without task-specific fine-tuning. Comparative evaluation with energy domain specialists against a traditional XAI dashboard showed improved usability, with all experts preferring the conversational interface.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:40 UTC · 6d agoAI research1

PHAT: PHotonic Accelerator for TFHE

PHAT photonic accelerator using optically addressed phase-change memory speeds TFHE bootstrapping FFTs 2.14x-5.10x over the prior ASIC accelerator.

Researchers propose PHAT, an electro-photonic accelerator for TFHE fully homomorphic encryption built on optically addressed phase-change memory (OPCM) processing-in-memory. The architecture adds OPCM-based FFT units, a twiddle-stationary dataflow, and a scheduling mechanism to maximize FFT utilization for TFHE bootstrapping. It achieves 2.14x-5.10x speedups across four real-world TFHE workloads versus the state-of-the-art ASIC accelerator, improving feasibility of privacy-preserving computation in the cloud.

arXiv cs.CR·Sep 10, 14:30 UTC · 6d agoResearch

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

Trends in Web Threats in CY Q2 2022: Malicious JavaScript Downloaders Are Evolving

Unit 42 detected 751,000 landing URL incidents in Q2 2022 and documented malicious JavaScript downloaders evolving to evade detection.

Unit 42 detected 751,331 landing URL incidents (253,644 unique) and 1,744,629 malicious host URL incidents (256,844 unique) from April through June 2022. Total landing URL incidents rose compared with Q1 2022, and unique host URL incidents grew 42%, indicating attackers deploying more variants. The report includes a case study of a JavaScript downloader campaign demonstrating new evasion techniques. Personal sites, blogs, and business sites were the top apparently benign entry points.

Palo Alto Unit 42·Aug 17, 13:05 UTC · Aug 17, 2026Research

SCHERI: Provably Secure Speculation Under the Constant-Time Policy for CHERI (Extended Version)

Researchers formally prove existing CHERI speculation proposals leak secrets and present SCHERI, a processor design with end-to-end Spectre-resistant constant-time guarantees.

The paper builds a formal framework reasoning jointly about capability safety, speculative execution, and information-flow security on CHERI architectures. It demonstrates that existing secure-speculation proposals fail to preserve constant-time confidentiality guarantees and can transiently leak isolated secrets. The authors present SCHERI, a new processor design formally proven to provide end-to-end secure speculation for the constant-time policy, resilient to Spectre attacks.

arXiv cs.CR·Sep 15, 16:33 UTC · 1d agoResearch

Meta FAIR Introduces AI Research Preference Models (RPMs): Ranking ML Experiments Before Spending GPU Hours

Meta FAIR, Oxford and UCL introduce Research Preference Models that rank unexecuted ML experiments, lifting AIRS-Bench scores from 0.684 to 0.729 and cutting compute ~1.6×.

Researchers from Meta FAIR, Oxford, and UCL introduce Research Preference Models (RPMs), which use frozen pretrained LLMs (Qwen3.6-27B backbone, no fine-tuning) to rank unexecuted experiment candidates and execute only the winner of a pairwise knockout tournament. Two variants shipped: an inference-only LLM-as-a-judge and an agentic variant that runs small pilot experiments in an H200 sandbox. On AIRS-Bench (20 tasks, 24 hours on one H200, 10 seeds), scores rise from 0.684 (random) to 0.711 and 0.729 versus a 0.748 validation oracle, and both variants reach the baseline's 24-hour score in roughly 15 hours. The team reports new SOTA on WinoGrande (94.1% with Agentic RPM) and SVAMP (95.7% with inference-only).

MarkTechPost·Sep 6, 20:25 UTC · 9d agoAI research

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

Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra

Cognition released SWE-2, a coding model post-trained from Kimi K3 that scores 50.0% on FrontierCode 1.1 Main, near Fable 5.1 at 64% lower cost.

Cognition introduced SWE-2, its most advanced coding model, post-trained from the 2.8T-parameter Kimi K3 base model. It achieves 50.0% on FrontierCode 1.1 Main, 73.0% on DeepSWE 1.1, and 92.8% on Terminal-Bench 2.1, beating Grok 4.6 and SWE-1.7 while matching Fable 5.1 and GPT-5.6 Sol at a fraction of the price. The company says it scaled reinforcement learning to the multi-trillion-parameter regime for the first time, using Pareto-informed cost penalties that train all reasoning-effort levels in a single run, tripled RL environments, and NVFP4/FP8 quantization-aware training. SWE-2 is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion.

Hacker News · AI·updated Sep 12, 20:00 UTC · 3d ago·first Sep 10, 15:29 UTC · 6d agoModel release 10 sourcesHN 58↑ · 15 comments1

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:44 UTC · 12d agoAI research1

Ukrainian National Sentenced to Four Years in Prison for Wire Fraud Conspiracy in Connection with Conti Ransomware

Ukrainian national Oleksii Lytvynenko sentenced to four years for wire fraud conspiracy deploying Conti ransomware that infected over 1,000 computers.

Oleksii Oleksiyovych Lytvynenko, 44, a Ukrainian national, was sentenced to four years in prison for conspiracy to commit wire fraud related to deploying the Conti ransomware variant. The US Department of Justice said the conspiracy infected the computers of more than 1,000 victims. The sentencing is an update to a previously reported case.

DataBreaches.net·Sep 11, 16:07 UTC · 5d agoPolicy & legal 7 sources

"They don't care about this": A Systematic Study of TEE Build Reproducibility in the Wild

91% of 115 surveyed TEE deployments across Intel SGX, TDX, and AMD SEV fail to provide reproducible builds needed for verifiable remote attestation.

A systematic study of 115 TEE deployments found 91% were not reproducible and 80% lacked both source code and a reference build, undermining remote attestation guarantees. Interviews with 12 developers of 50 Intel SGX projects confirmed that only one participant treats reproducibility as a development priority. The authors identify technical barriers such as embedded timestamps plus ecosystem-level issues like lack of build-environment control in multi-stakeholder projects, and call for holistic, committed reproducibility practices.

arXiv cs.CR·Sep 10, 11:43 UTC · 6d agoResearch

Using AI for Weapons Development

Anthropic report reveals Yemen-based actors used Claude Code to build guidance software for guided rockets and ballistic missiles.

Bruce Schneier highlights Anthropic's misuse disclosure describing a threat actor cell in northern Yemen running three weapons programs: a guided rocket with phone-class homing guidance, a 2,000+ km multi-stage ballistic missile, and the 'R2000' hypersonic glide vehicle set. The actors used Claude Code as a substitute for human engineers to write GNC software, integrate an open-source autopilot, tune controls, and run flight simulations, orchestrating multiple Claude instances in delegated roles. Safeguards blocked many requests but evasion tactics included hiding intent and splitting work across sessions; one guided rocket test-fire failed but no operational device was fielded.

Schneier on Security·Sep 14, 16:07 UTC · 2d agoAI safety & security in the wild

CHERI-D Reincarnate: efficient multicore CHERI temporal memory safety through allocation reincarnation (draft version)

CHERI-D Reincarnate adds allocation reincarnation to CHERI, quarantining generation IDs instead of memory to cut temporal memory-safety overheads on multicore systems.

The proposal extends CHERI-D temporal memory safety by quarantining exhausted generation IDs rather than memory slots, allowing freed memory immediate reuse through allocation reincarnation. It adds coherent ID caching and multicore support connecting physical coherence events to the virtually addressed ObjID buffer. The design was implemented as a hardware-software co-design across CHERI-Toooba FPGA softcore, QEMU, LLVM/Clang, and CheriBSD. Across evaluated workloads it substantially reduces memory-sweep frequency and quarantine overhead with low performance and hardware cost.

arXiv cs.CR·Sep 10, 14:19 UTC · 6d agoResearch

ChatGPT Sketch turns your bad drawings into detailed AI images

OpenAI launched ChatGPT Images 2.5 with a Sketch feature that turns user doodles into images, plus 50% lower latency.

OpenAI released ChatGPT Images 2.5 and a new Sketch feature, activated by typing @Sketch, that lets users draw doodles inside ChatGPT and use them as image generation prompts. The update claims more natural lighting, richer textures, better multi-turn instruction following, and up to 50% latency reduction versus Images 2.0. Users can also leave inline comments on parts of an image to request specific edits. It is available for ChatGPT, ChatGPT Work, and Codex users on desktop, mobile, and web.

The Verge · AI·Sep 8, 20:16 UTC · 7d 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·Sep 15, 16:23 UTC · 1d agoAI research

UBoatRAT Navigates East Asia

Unit 42 discovers UBoatRAT, a new custom RAT targeting South Korean and video-game industry personnel, delivered via Google Drive with GitHub-based C2 and BITS persistence.

Unit 42 identified UBoatRAT, a new custom remote access trojan first found in May 2017, whose initial version used a public Hong Kong blog service and a compromised Japanese web server for command and control. The latest variants target personnel or organizations related to South Korea or the video games industry, are delivered through Google Drive, and masquerade as Microsoft Word, Excel, or folder icons. The RAT checks for virtualization software and domain join, retrieves its C2 address from a Base64-encoded string in a GitHub-hosted file, uses a custom XOR-encrypted C2 protocol, and maintains persistence via Windows Background Intelligent Transfer Service (BITS) jobs that survive reboots.

Palo Alto Unit 42·Aug 17, 12:55 UTC · Aug 17, 2026Malware in the wild1

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 · 11d agoAI industry

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News·Sep 10, 16:00 UTC · 6d agoAI industry1

CertiFlash: A Formal Verification Framework for Flash Translation Layers in Computational Solid State Drives

CertiFlash provides machine-checked formal verification of SSD flash translation layers, proving isolation, integrity, and ownership invariants to prevent tenant data leaks.

CertiFlash is an open-source formal verification framework for Flash Translation Layers (FTL) in computational SSDs, mechanized in the Rocq proof assistant. It shows that a faulty FTL can corrupt device state at five surfaces (e.g., leaking data between tenants or dropping integrity tags), demonstrated on a DaisyPlus OpenSSD. Designers prove once that every operation of a general FTL model preserves a global invariant covering mapping, isolation, integrity, ownership, and allocation; new designs need only discharge five hypotheses. Across four case studies, added effort was 27-3,231 lines against a 16,489-line framework.

arXiv cs.CR·Sep 9, 15:44 UTC · 7d agoResearch1

Switching Password Managers in 2026

An Apple password-management engineer (writing personally) explains switching password managers via iOS direct app-to-app export, crediting FIDO Credential Exchange for passkey portability.

Ricky Mondello, an Apple engineer in password management and authentication (writing personally, not for Apple), demonstrates moving 100 items from 1Password to Apple Passwords via the iOS system export, which transfers passwords, passkeys, verification codes, and notes directly between apps with Face ID confirmation and no intermediate files. He credits the FIDO Alliance's Credential Exchange format (first draft May 2024, now an open spec) and iOS 26/macOS 26 for enabling phishing-resistant credential transfer between apps like 1Password, Bitwarden, Dashlane, DuckDuckGo, and Devolutions. The post is advice rather than news, recommending bulk transfer on the existing device, switching AutoFill to the new app, and treating the new manager as the source of truth.

Lobsters · security·Sep 8, 13:55 UTC · 8d agoIndustry1

I wrote an AI textbook — how long until AI can do it better?

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.

Interconnects·Aug 12, 13:01 UTC · Aug 12, 2026AI research

Hackers Target Claude, Cursor and Codex AI Agents to Steal Tokens and Prompt Histories

Gen Digital found infostealers like Amatera and Remus stealing AI coding agent tokens, prompt histories, and MCP configs from infected Windows and macOS machines.

Gen Digital analysts observed Amatera and Remus detections among tens of thousands of protected Windows users over three months, with Amatera targeting Cline and Continue data and Remus targeting Claude, Cursor, and OpenCode. CallbackBeaver added Cursor and Claude to its collection scope with more than 5,000 samples in 30 days, while macOS-focused Djinn Stealer has been associated with Claude, Codex, Gemini, Cline, OpenCode, and Kilo. The stealers harvest access and refresh tokens, prompt histories, and MCP configuration files that can expose source control, ticketing, databases, cloud resources, and sensitive project context for follow-on fraud. Many stealers add targets via remotely managed rules, meaning this is an adaptation of existing infostealers rather than a new vulnerability in the AI tools themselves.

Cyber Security News·Sep 9, 11:41 UTC · 7d agoMalware in the wild2

Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems

An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.

The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 16:45 UTC · 9d agoAI research1

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·Sep 8, 16:03 UTC · 8d agoResearch1

Graph Machine: Towards Better Pretraining via Edges

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.

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

Berlin investigates new data leak after hackers publish stolen login credentials

Berlin investigates a fresh leak after Rhysida hackers published stolen login credentials; the city refuses to pay the ransom demand.

Berlin confirmed hackers published additional stolen data, including login credentials, from a mid-August cyberattack on two city ministries responsible for urban development/housing and transport/climate. The Rhysida ransomware group claimed the breach in late August, saying it stole 5.79 TB of data including contracts, emails, passwords and classified information; Berlin acknowledged an extortion demand but refused to pay. Berlin's data protection regulator said the leak includes personal data on public employees and possibly residents, such as names, addresses, dates of birth and bank information. Germany's BSI separately linked the campaign to the TerminalFix fake-CAPTCHA technique and the LoremIpsumLoader/AxolotLoader malware tied to financially motivated Rhysida-associated hackers, days before Berlin's Sept. 20 election.

The Record·Sep 7, 11:50 UTC · 9d agoRansomware in the wild1

LLMs and Contextual Integrity

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.

Schneier on Security·Aug 18, 10:40 UTC · 29d agoAI safety & security

The AI Supply Chain Has a Security Problem, and Much of It Is Sitting on the Open Internet

Researchers counted 36,769 publicly reachable self-hosted AI endpoints, only about 2% behind HTTP authentication, exposing Ollama, vLLM, and Flowise to abuse.

A Mysterium VPN study found 36,769 self-hosted AI endpoints reachable through internet scanning, with only 2.02% returning an HTTP authentication challenge. Open WebUI accounted for 18,529 reachable instances, Ollama for 6,935 fingerprinted hosts, and 5,223 agent-builder and workflow platforms were exposed, often holding API keys, database credentials, and other secrets. The report highlights LLMjacking risk from exposed Ollama APIs, a critical Flowise bug (CVE-2026-40933), leaked n8n tokens, and prior SentinelOne/Censys research finding roughly 175,000 exposed Ollama hosts in 130 countries.

Security Affairs·Sep 11, 17:27 UTC · 5d agoAI safety & securityCVE-2026-409331

Diffs vs. Whole Files: An Empirical Comparison of Iterative Edit-Based and Direct Generation for Flutter/Dart Code Models

Empirical study finds direct whole-file generation beats iterative diff-based editing for Flutter/Dart code models on about 1,790 held-out tasks.

Researchers trained Rainbow-Pony-100M from scratch and fine-tuned Qwen2.5-Coder-0.5B in both direct-generation and diff-based regimes, then evaluated four resulting models on roughly 1,790 Flutter/Dart tasks. Direct generation outperformed diff-based generation on compilation pass rate, bits-per-byte, character-level similarity, and blinded LLM-judge ratings. Diff-based editing is competitive only on short, localized edits in refactoring and error-handling tasks, a property the authors call task locality.

Hugging Face daily papers·Sep 4, 20:00 UTC · 11d agoAI research1

How AI wiped out an entire industry in Nairobi

ChatGPT's rise collapsed Kenya's contract essay-writing industry, which once employed 40,000 people in Nairobi, cutting $40–70-per-paper earnings after 2022.

The New York Times reports that generative AI, primarily ChatGPT, wiped out Kenya's ghostwriting business, which at its peak employed at least 40,000 people in Nairobi writing academic papers for US and UK students. Writers like Teresios Bundi, who produced over 2,500 papers in twelve years, saw prices and orders collapse after ChatGPT's 2022 launch. Related gig work such as transcription, data annotation, and Meta content moderation has also dried up, leaving 'humanizers' who rewrite AI text to evade plagiarism checks. Kenya's government had promoted online gig work since 2016, and roughly 80 percent of the country's jobs are informal.

The Decoder·Sep 7, 13:18 UTC · 9d agoAI industry

Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch

New work characterizes language generation in the limit via finite witnesses, proves a full separation-width hierarchy, and formalizes all results in Lean.

The paper fully characterizes when language generation in the limit is possible for arbitrary families over a countable universe: each target must admit a finite positive witness such that targets activated by any finite sample share an infinite common intersection. It defines positive separation width and proves every level of the resulting hierarchy occurs, with countable families admitting singleton witnesses and unions of families with infinite common cores requiring unbounded finite witnesses. The characterization, a universal normalization, and a diagonal capture lemma are machine-checked in the Lean proof assistant, with the development maintained on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:54 UTC · 7d agoAI research1

Arrested man allegedly impersonated NSA elite hacking unit, Supreme Court chief justice

Colorado man Joshua Culver indicted for impersonating NSA's Tailored Access Operations chief and Supreme Court Chief Justice John Roberts in Indiana court cases.

Joshua Culver, also known as Maverick Young, was arrested in Colorado after a July Indiana indictment on four counts of falsely impersonating an officer of the court and one count of using a forged judge's signature. He allegedly posed as an NSA officer in September to pressure the Tippecanoe County sheriff's office, and later presented a forged document purportedly from the head of the Tailored Access Operations unit demanding case dismissal and warrant quashing. The indictment also alleges he used a forged signature of Chief Justice John Roberts on a dismissal order in Grant County, Indiana.

CyberScoop·Aug 25, 18:43 UTC · 21d agoPolicy & legal2

Quoting Paul Ford

Simon Willimon quotes Paul Ford arguing AI can write good software but cutting-edge work still demands human collaboration, craft, and judgment.

Simon Willimon highlights a passage from Paul Ford's essay 'A.I. Was Supposed to Give Us New Killer Apps. What Happened?'. Ford argues that while AI can write very good software, it also makes it easy to do someone else's job badly, which partly explains why many AI-driven projects fail. The quote reflects a broader industry reassessment of AI coding tools after initial fears that developer roles were obsolete.

Simon Willison·Sep 12, 18:00 UTC · 3d agoAI industry1

Snowflake hacker pleads guilty, faces up to 32 years in prison

Help Net Security·Aug 6, 00:00 UTC · Aug 6, 2026Ransomware

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.

Hacker News · AI·Sep 7, 09:26 UTC · 9d agoAI tools & infraHN 141↑ · 52 comments1