ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “Search”

448 stories

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllMalware 2020Vulnerability 1557Ransomware 1293Policy & legal 1006Data breach 880Industry 748Phishing & fraud 704Exploit / PoC 689Threat actor 621Research 173AI tools & infra 145AI safety & security 136Tools 77AI research 71
SeverityAllcritical 2638high 2971medium 2242low 1990info 454

Have it both ways: stay discoverable in search while disallowing AI training

Cloudflare launches Disallow AI Training setting letting sites block AI training via robots.txt while staying indexed in search; Apple, Google, and Microsoft honor it.

Cloudflare announced a 'Disallow AI Training' setting that publishes a no-training preference in robots.txt so sites can block AI training (including by mixed-use crawlers) without losing search indexing. Apple, Google, and Microsoft meet Cloudflare's new 'Accountable' designation, which requires training/summary opt-out mechanisms, URL-level training visibility, and assurance that opt-outs don't affect search rankings. Cloudflare cites that under 1% of its sites block search bots while 17% block AI training, and its Block settings now apply to mixed-use crawlers as well. Granular controls over how much content appears in AI summaries are planned for early next year.

Cloudflare Blog · 1d agoTools

AI SIEM Search

Huntress launched AI Search for Managed SIEM, translating plain-English questions into ESQL log queries with schedulable custom alerts.

Huntress introduced AI Search in its Managed SIEM, letting users query logs in natural language instead of writing ESQL, KQL, or SPL. The feature generates a real ESQL query behind the scenes, exposes it for users to inspect and learn from, and allows saved searches to be scheduled as recurring alerts at hourly, daily, or weekly cadence. A planned next version will return summarized plain-language answers instead of raw logs.

Huntress · 9d agoTools

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Continual Search framework iteratively prompts LLM judges to keep searching agent execution logs, boosting long-horizon failure root-cause attribution accuracy.

The paper frames automated root-cause attribution (RCA) for long-horizon AI agent failures as a search problem, since relevant evidence is sparse and distributed across massive execution traces. The authors propose Continual Search, an iterative framework that nudges an LLM judge across successive turns to keep hunting unresolved diagnostic evidence instead of settling on an early plausible diagnosis. They introduce MegaRCA-Mix, a benchmark of 50 human-annotated failure trials on long-horizon, execution-heavy tasks. On MegaRCA-Mix, Continual Search improves GPT-5.5's F1 from 0.349 to 0.498 (over 40% gain), and lower-tier models can surpass higher-tier counterparts when search is effective.

Hugging Face daily papers · 6d agoAI research1

Get ready for the game with new football features in Search

Google Search adds a Live Game Feed, deeper football stats, and Yahoo Fantasy/Sleeper integration with AI Mode for personalized fantasy insights.

Google rolled out football features in Search, including a Live Game Feed with play-by-play updates and AI-powered insights, available on mobile in the U.S. in English. New carousels show league-wide scores and expanded player stats such as sacks, fumbles, and yards after catch. Users can link Yahoo Fantasy or Sleeper accounts to receive start/sit and waiver-wire recommendations through AI Mode. Collegiate team support and broader global availability are planned later this month.

Google · AI · 7d agoAI industry

Google Search Makes It Harder to See Where a Link Really Goes Before You Click

Google now routes some Search results through opaque google.com/goto redirects, weakening hover-to-verify anti-phishing checks.

Google has begun serving some organic search results as opaque google.com/goto?url= redirects whose destinations can only be resolved server-side by Google, likely to raise scraping costs for rank trackers and archival services. The change removes the pre-click hover preview of the true destination URL, undermining a long-standing anti-phishing habit for spotting lookalike, typosquatted, or search-optimized phishing domains. Security teams are advised to rely on layered defenses such as domain reputation, DNS and web filtering, browser isolation, and user training rather than hover text.

GBHackers · 1d agoIndustry

Iris-mini and Iris-pro are the strongest open-weight search agents in their class

Chinese lab AllSpark releases Iris-mini (35B) and Iris-pro (397B) open-weight search agents claiming best-in-class results on BrowseComp and other research benchmarks.

AllSpark's paper introduces Iris-mini (35B parameters, built on Qwen3.6-35B-A3B) and Iris-pro (397B parameters, built on Qwen3.5-397B-A17B), both with 256,000-token context windows. Iris-pro scores 88.6 on BrowseComp, 85.1 on BrowseComp-ZH, 92.9 on DeepSearchQA, and 56.4 on Humanity's Last Exam; Iris-mini reaches 82.2, 84.8, 86.9, and 52.3 respectively. Training tasks are reverse-engineered from web link structure, filtered by a judge model, and refined via alternating SFT and reinforcement learning ('SFT-RL climbing') against live web search. Weights are available on Hugging Face, and the Iris Harness with agent loop, tools, and all four benchmarks is on GitHub.

The Decoder · 3d agoModel release1

Google’s new search redirects make links harder to check before you click

Google routes some search result links through encoded google.com/goto?url= redirects, breaking hover-to-verify link checks and raising scraping costs.

Google has started routing some search result links through opaque google.com/goto?url=... redirects using a custom Google-specific encoding, with the destination visible only via the redirect response's Location header. Google confirmed the rollout as an anti-abuse measure, most likely to make bulk extraction of destination URLs from search results more difficult and costly. Malwarebytes warns the change undermines the standard hover-before-clicking safety advice, while legitimate rank-tracking, SEO auditing, archival, and accessibility tools now face the same rate limits and costs as abusive scrapers.

Malwarebytes Labs · 2d agoIndustry

Texas Police Used AI to Write Report About Using Flock to Search for Woman Who Had Abortion

Johnson County, Texas deputies used Axon's Draft One AI to write a report about searching 80,000+ Flock cameras for a woman who self-administered an abortion.

Documents show the Johnson County Sheriff's Office used Flock's nationwide camera network and Axon's Draft One, which drafts police reports from body camera audio, in its investigation of a woman's self-administered abortion. The AI-generated report summarized deputies' discussion of legal implications, noting Texas law provided no applicable criminal charges. Flock CEO Garrett Langley has repeatedly claimed the search was a family welfare check, but earlier police reports indicate it was initiated at the behest of the woman's abusive partner.

404 Media · 14d agoAI safety & security

Clearview AI Is Testing an AI Tool That Would Let Cops Unearth Your Life Online

Clearview AI is prototyping InquiryIQ, an AI analyst assistant that would automatically profile individuals identified through its face-recognition searches for police.

WIRED uncovered InquiryIQ, an unreleased Clearview AI prototype described as an AI analyst assistant that fans out across the web from a face-recognition search result to assemble profiles including employers, aliases, associates, and physical characteristics. The company tested a model from xAI (merged with SpaceX), maker of Grok, and the interface accepts age, gender, and race inputs to guide searches. Clearview says the prototype was never pitched or shipped to customers and no law enforcement user has used it; the database has grown to over 70 billion images used by more than 2,000 law enforcement agencies.

WIRED · Security · 6d agoAI industry1

Atlas: Efficient Verifiable Semantic Search

Atlas delivers zero-knowledge proofs for HNSW semantic search, verifying RAG retrieval in under a second on SIFT1M and 2.0 seconds at 100M vectors.

Atlas lets a search provider prove that a query was answered correctly against a committed HNSW index without revealing the index, addressing provider deviations like truncation or bias. It combines offline preprocessing, a fixed-size-state restructuring of HNSW with a correctness proof, and timestep-tagged batching of per-step arguments. The system proves queries in under a second on SIFT1M and 2.0 seconds at 100 million vectors while preserving plaintext HNSW recall, and proven retrieval maintains end-to-end RAG answer quality at lower cost than prior verifiable retrieval systems.

arXiv cs.CR · 6d agoResearch1

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 · 6d agoAI industry1

Rethinking Indirect Prompt Injection as a Test-Time Search Problem

Researchers frame indirect prompt injection as test-time search, showing added attacker compute improves vulnerability discovery and exploitation against tool-using agents.

The paper models indirect prompt injection as a test-time search over a task-dependent attack surface shaped by the environment, user task, and injection goal. The authors build an agentic attacker with a dedicated search harness that performs reconnaissance, structured strategy reasoning, and adaptive evaluation using victim-agent feedback. Experiments show more attacker test-time compute improves discovery and exploitation of injection vulnerabilities, with explicit strategy management needed to avoid redundant search. The results argue that agentic security evaluations should characterize attacker search procedures and compute budgets rather than treating attack success as budget-independent.

arXiv cs.CR · 13d agoAI safety & security

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space · 7d agoAI research1

Hoss: Fast Oblivious Semantic Search with Heterogeneous GPU-CPU-TEE Architecture

Hoss uses heterogeneous GPU-CPU TEEs for oblivious semantic search, achieving up to 67x speedup over the Compass ORAM-based system.

Hoss is a first-of-its-kind oblivious semantic search system built on a heterogeneous CPU-GPU TEE architecture, hosting hot-path HNSW graph traversal in large GPU TEE private memory and offloading lower graph layers to CPU TEEs. It invokes oblivious primitives only for lower-layer accesses and adds host-access ORAM and data-dependent optimizations. Benchmarked against prior state-of-the-art Compass, the prototype achieves up to 67x speedup while maintaining high recall, with larger gains at scale.

arXiv cs.CR · 13d agoResearch

A Three-Layer Caching Architecture for Low-Latency LLM Web Search on Commodity CPU Hardware

OreoLook's three-layer Redis caching architecture cuts redundant LLM calls and embedding work for CPU-hosted web-search answer generation.

The paper describes a three-layer caching architecture for OreoLook (formerly lixSearch), an open-source LLM answer engine: a Redis session context window with Huffman-compressed disk overflow, a semantic query cache matching rephrasings via embedding cosine similarity, and a URL embedding cache deduplicating embedding computations. Deployed on a single 8-vCPU Intel Cascade Lake server with 30 Hypercorn workers across three containerized replicas, it achieved an 89.3% aggregate Redis keyspace hit rate, 0.1 ms read latency, and 1.38 MB memory overhead. An LRU eviction daemon migrates idle sessions to disk and rehydrates them for resumption hours or days later.

Hugging Face daily papers · Aug 11, 2026AI tools & infra1

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.

The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers · 6d agoModel release

3 new ways to plan and book travel in Search

Google Search's AI Mode adds travel features: hotel booking, airfare tracking, and airline miles and rewards visibility.

Google introduced three new travel capabilities in Search's AI Mode, including hotel booking, airfare tracking, and viewing airline miles and rewards. The features extend AI Mode from informational queries into transactional travel planning. The announcement positions Google Search to compete more directly with travel-focused booking services.

Google · AI · 20d agoAI industry

Israel Is Running a Synthetic Think Tank to Influence AI Search Results

Israel funds the Hanover Institute, an AI-generated think tank by ad firm Piro, publishing 100+ articles to influence LLM chatbot answers about Israel.

The Hanover Institute for Public Policy, run by American ad firm Piro Inc and paid for by Israel through HAVAS Media and the state ad agency LaPam, has published more than 100 articles in under a month. The AI detection tool Pangram found tested articles entirely AI-written except their bibliographies, and the site includes an llms.txt file to make it easier for LLMs to scrape. FARA filings show a $900,000 project invoice plus a $100,000 payment, and Piro advertises an 'AI Story Optimization' service for shaping chatbot answers.

404 Media · 22d agoAI safety & security

5 new ways to level up your learning with Search

Google promotes five Search study tools for students preparing for classes and standardized tests.

Google published a back-to-school post highlighting five ways to use Search tools when studying for classes and standardized tests. The post is promotional product guidance rather than a launch, research result, or security announcement.

Google · AI · 28d agoAI industry

Learning-Guided Planning in Large Dynamic Action Spaces: Budgeted Tree Search for One-to-Many Mobile Charging

LP-BTS uses graph proposal policies, learned critics, and budgeted PUCT search to plan mobile charging across dynamic action spaces up to 2,813 stops.

LP-BTS is a learning-guided planning architecture for one-to-many mobile charging, where N=250 sensors induce roughly 1,125 initial candidate charging stops. A graph proposal policy concentrates candidate support, a learned value critic evaluates leaves, and edge-budgeted PUCT compares simulated futures, letting a single frozen checkpoint cover action universes from 736 to 2,813 stops. On a sealed 30-scenario confirmatory bank it attains the highest observed survival (0.4545) and alive-AUC (0.8031), though its +0.0066 survival edge over the strongest engineered comparator is statistically unresolved.

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

Fashion app Daydream uses Apple Intelligence to help you shop the outfits in your camera roll

Fashion app Daydream uses iOS 27 Apple Intelligence APIs to turn saved outfit photos into shoppable matches and enable Siri voice search.

Daydream launched photo-based outfit shopping and Siri natural-language search built on Apple's iOS 27 developer tools, matching images against roughly 3 million products from 325+ retailers and 10,000 brands. The app claims over 1.5 million shoppers and frames the features as steps toward a cross-surface shopping agent. Competitors include Google, Amazon, Onton, and Alta.

TechCrunch · AI · 2d agoAI industry

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Benchmark Radar provides a living searchable database of 1,283 AI benchmark records and 12,916 score observations drawn from 37 daily discovery sources.

Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases from 13 direct connectors and 24 first-party feeds into a searchable catalog with model card mentions and score histories. The catalog contains 1,283 source records drawn from 4 benchmark catalogs plus 12,916 numeric observations on 790 records. The release includes a web dashboard with leaderboard, Pareto frontier of score versus usage, saturation and trend views, daily feeds, a CLI, and reproducible analysis. The paper audits the full catalog and examines benchmark saturation and limits of score comparisons.

Hugging Face daily papers · 7d agoAI research

Searching for New Physics with Reinforcement Learning

Researchers apply reinforcement learning to identify SMEFT operators explaining particle physics anomalies, reproducing and improving known CDF W-mass results.

The paper introduces a reinforcement learning method to search the large Standard Model Effective Field Theory (SMEFT) operator space for explanations of measurement anomalies. It was validated on the CDF W-mass anomaly, reproducing and improving known results, then applied to a harder multi-anomaly scenario. RL efficiently navigates complex loop-level operator correlations that bias human-driven phenomenological analysis.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research