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China-Linked Hackers Exploit Chrome-Windows Zero-Day Chain to Deploy GRIMWEDGE

Volexity attributes spear-phishing campaign exploiting Chrome-Windows zero-day chain to Chinese actors UTA0560 and APT31 deploying GRIMWEDGE and LONGTALE.

Volexity attributes a September 1, 2026 spear-phishing campaign targeting NGOs to China-linked UTA0560, which abused a reflected XSS flaw on a US university website to trigger a three-part exploit chain (CVE-2026-85046, CVE-2026-87491, CVE-2026-85880) escaping the Chrome V8 and browser sandboxes to deploy the GRIMWEDGE JavaScript backdoor with reconnaissance, file management, and command execution capabilities. The same chain was used near-simultaneously by JungleBamboo (APT31) to deploy SUPERSTOMP, installing the LONGTALE/GemStone credential-stealing Chrome extension masquerading as Google Gemini. The Chrome flaws were patched in Chromium but not yet in stable Chrome, creating an unusual patch-gap zero-day window attackers raced to exploit.

The Hacker Newsupdated · 1d agofirst · 1d agoThreat actor in the wild 20 sourcesCVE-2026-85046CVE-2026-87491CVE-2026-858801· 1 read

MaxKernel: Agentic Kernel Generation for TPUs

Researchers open-source MaxKernel, a multi-agent LLM system that generates and optimizes TPU kernels matching expert hand-tuned baselines on JaxBench.

MaxKernel is a multi-agent system offering three paradigms for TPU kernel development: human-in-the-loop collaborative design, a fully autonomous metric/trace-driven optimization loop, and graph-based autonomous search for global exploration. All paradigms draw on a shared pool of specialized sub-agents for planning, implementation, self-debugging, testing, and hardware profiling. Evaluated on JaxBench's 50 diverse TPU kernel tasks and real-world workloads from open-source models, it consistently matches expert hand-tuned baselines. The system is open-sourced via the AI-Hypercomputer GitHub repository.

Hugging Face daily papers · 13d agoAI tools & infra

Atria Dawn: The Dawn of Agentic Superintelligence

Atria Dawn Preview, an agentic foundation model trained on verifiable experiences, tops five of 16 research and engineering benchmarks.

Atria Dawn Preview is a foundation agentic language model for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline connecting tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning research, engineering, and digital work it is competitive with frontier agents and achieves the highest reported score on five of them. The release includes a human-AI collaboration case study analyzing 769 task records from 56 participants, where about one-third of completed AI-assisted tasks were rated infeasible without AI and agents frequently proposed methods and implemented revisions while humans retained final decisions.

Hugging Face daily papers · 2d agoModel release

Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher

Claude Fable 5.1 solved Sir Thomas Urquhart's 370-year-old Cyphral Distich cipher, recovering a hidden royalist prayer for Charles II.

Vals AI reports that Claude Fable 5.1 solved the Cyphral Distich, a 64-number cryptogram from Sir Thomas Urquhart's Logopandecteision unsolved since 1653, in 44 minutes using 176k tokens with no human hints. The key insight was that the cipher's key was the book itself: each number indexes a word in the corresponding Proquiritation, taking the first letter, yielding 'O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND'. The model also deciphered the larger Cyphral Octastich (285 numbers) from The Jewel (1652) using page-based word indexing, recovering all but nine letters of a royalist prayer. The puzzle had been listed among Klaus Schmeh's Top 50 unsolved encrypted messages.

Hacker News · AI · 2d agoAI researchHN 63↑ · 6 comments1· 1 read

Claude Fable 5.1 made me a really nice animated pelican

Anthropic launched Claude Fable 5.1, claiming gains in coding and long-running tasks, with 52.6% on Terminal-Bench-Science 0.1.

Anthropic released Claude Fable 5.1 (alongside Mythos 5.1), positioning it as a new standard for coding, knowledge work, and long-running problem-solving. The model scores 52.6% on Terminal-Bench-Science 0.1, up from 24.7% for Fable 5, versus 29.0% for Opus 5 and 22.4% for GPT-5.6 Sol. Simon Willison's hands-on test found the model produced an impressive animated pelican, though he notes other benchmarks show only slightly improved scores.

Simon Willison · 14d agoModel release

llm-anthropic 0.28

llm-anthropic 0.28 released with Claude Fable 5.1 support, default reasoning traces and a new ClaudeRefusal exception.

Developer Simon Willison released version 0.28 of the llm-anthropic Python plugin, adding support for Claude Fable 5.1. Reasoning traces are now displayed by default for models that support them, and a new llm_anthropic.ClaudeRefusal exception is raised when Claude returns a refusal.

Simon Willison · 13d agoAI tools & infra2

Import AI 469: Science AI; RSI simulator; and Zuck's technological pessimism

New DiG-bench benchmark of 70 hidden-rule games shows only Opus 5 and Fable 5 solving the hardest tiers, probing AI discovery and creativity.

Import AI 469 highlights DiG-bench (Discovery in Games), a benchmark of 70 handcrafted games with hidden rules and objectives where only 21 games are public and most are kept private to avoid training contamination. Only Opus 5 and Fable 5 with Claude Code solved any Tier 7 tasks (about 0.2 success), with GPT-5.5 next; the games are text-based and have beaten every human tester at least once. The newsletter also covers an RSI simulator game by Paradigm Research and Inherent's Faraday, a post-trained open-weight model that supervises frontier models to improve scientific research output.

Import AI · 29d agoAI research

Guildma (Astaroth) malware infection from Brazilian Portuguese email, (Tue, Sep 1st)

Guildma (Astaroth) malware spread via geofenced Brazilian Portuguese phishing emails, using zip archives, shortcuts, and AutoIt loaders.

SANS researchers documented a Guildma (Astaroth) malware infection initiated on 2026-08-31 by a malicious Brazilian Portuguese email offering a DocuSign-themed PDF. The delivery link is geofenced to Brazil and requires Brazilian Portuguese browser and OS language settings, otherwise serving a legitimate installer. The infection chain uses a zip archive containing a Windows shortcut that retrieves a DLL saved as an alternate data stream, which installs a compiled AutoIt package for persistence. Unique SHA-256 indicators and HTTPS traffic to Azure-hosted and .cfd domains were observed, with prior reports noting the campaign's abuse of GitHub.

SANS Internet Storm Center · 14d agoMalware in the wild

Dark Caracal Deploys New Go Malware With Ethereum-Based C2 Fallback

Dark Caracal targeted a Venezuelan communications organization with new Go-based GoCaracal malware and an updated Bandook backdoor using Ethereum smart-contract C2 fallback.

Arctic Wolf Labs linked a June 2026 intrusion against a communications organization in Venezuela to Dark Caracal, an espionage group associated with Lebanon's General Directorate of General Security (GDGS). The group deployed a previously undocumented Go-based framework called GoCaracal in lightweight and extended builds alongside a Delphi-loaded Bandook backdoor, with delivery via phishing emails carrying weaponized SVG attachments through URL shorteners. The extended build uses a custom Solidity contract, BulletproofC2, on Ethereum to retrieve replacement C2 addresses without redeploying malware, and Arctic Wolf traced 249 related samples from January to July 2026 showing a modular evolution arc.

Security Affairs · 19d agoThreat actor in the wild

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 · 1d agoAI research

[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens

Anthropic launched Claude Fable 5.1 and Mythos 5.1, claiming new SOTA benchmarks, with 75% cache-read price cut and 1M-token context.

Anthropic released Claude Fable 5.1 and Mythos 5.1 as flagship models for coding and knowledge work, with a 1M-token context window and pricing of $10/$50 per million input/output tokens and cache reads cut 75% to $0.25. Artificial Analysis Intelligence Index scored Fable 5.1 at 66 versus 63 for Claude Opus 5, with HLE at 59.1% and Terminal-Bench v2.1 at 91.4%, though per-task cost rose ~20% due to 1.7x output token usage. Community analysis suggested Fable and Mythos may share underlying weights with different safety/routing behavior, and release notes highlighted Enterprise Frontier Safeguards and zero-data-retention support.

Latent Space · 14d agoModel release2

IT help-desk vishing tricks executives into handing over Microsoft 365 access

Arctic Wolf tracks PREY-0058 (linked to UNC6671), a vishing campaign stealing Microsoft 365 session tokens via AiTM panels for SaaS data theft and extortion.

Arctic Wolf is tracking a campaign, PREY-0058, sharing tradecraft with Google Threat Intelligence Group's UNC6671, in which callers posing as internal IT talk employees through fake passkey/MFA setups. Operator-controlled adversary-in-the-middle pages harvest passwords and MFA approvals to seize session tokens, then actors enumerate and bulk-exfiltrate data from SharePoint, OneDrive, Exchange, and Box. Extortion brands include BlackFile, Pink, Helix, Cinder, and Redact; targets are mostly US-based executives in construction, healthcare, real estate, finance, and professional services, with exfiltration shifting to residential proxies like NodeMaven.

Help Net Security · 8d agoThreat actor in the wild1

Claude Fable 5.1's language is less "load-bearing" than its predecessor's

Arena.ai found Claude Fable 5.1 writes 30% longer answers with fewer em dashes, hedges, and validation phrases than Fable 5.

Arena.ai compared tens of thousands of high-reasoning Text Arena outputs from Anthropic's Claude Fable 5 and Fable 5.1. Fable 5.1's median answer length rose 30% to 414 words (from 319), remaining 21% shorter than Opus 5's 525. Em-dash usage fell from 16.2 to 11.0 per 1,000 words while semicolons rose to 6.09, hedges like 'perhaps' and 'arguably' dropped 36%, and praise appeared in 1.98% of responses versus 3.17%. Long content words fell from 42.6% to 38.6% and abstract nouns declined 25%, suggesting the successor's language is less 'load-bearing'.

The Decoder · 5d agoAI research1

Hackers abused Claude to extract secrets from 1.8M Android apps

Anthropic reports ShinyHunters, Midnight Blizzard, and GTG-10007 misused Claude to automate credential theft, malware operations, and espionage against dozens of victims.

Anthropic's threat report details how ShinyHunters member 'frkoo' ran a credential-harvesting pipeline on ten AWS EC2 workers that mass-downloaded and decompiled 1.8 million Android APKs, scanning for hardcoded secrets with TruffleHog. In one AI-assisted operation, an actor extracted 2,100+ Azure AD authentication tokens across more than 40 Microsoft tenants in roughly 34 hours, and ShinyHunters affiliates also stole AI API keys and breached a SaaS provider affecting about 200 downstream customers. Russian espionage group Midnight Blizzard used Claude Code skills to automate malware development, phishing, C2, and exfiltration against 20+ government and defense entities, rebuilding malware automatically when detected. Chinese-linked GTG-10007 ran autonomous vulnerability research that uncovered zero-days in a major endpoint security product and hit roughly 50 organizations with confirmed compromises; Anthropic disrupted the abuse and banned the accounts.

BleepingComputerupdated · 4d agofirst · 4d agoThreat actor in the wild 15 sources1

Claude Fable Solves a Historical Cipher

Bruce Schneier's blog highlights that the Claude Fable AI model solved a historical cipher, demonstrating LLM capabilities in cryptanalysis.

Bruce Schneier's blog post discusses the Claude Fable AI model successfully deciphering a historical cipher. The post frames the result as a notable example of LLMs applied to classical cryptanalysis. The published text provides limited technical detail beyond the headline.

Schneier on Security · 7d agoAI research

Dark Caracal Adds New Malware to Cyber Espionage Arsenal

Dark Caracal added GoCaracal, a modular malware framework, expanding its espionage toolkit for data theft and persistent access.

Dark Caracal, a known espionage-focused threat group, has added GoCaracal, a new modular malware framework, to its arsenal. The framework broadens the group's ability to steal victim data and maintain persistent access, according to Dark Reading coverage on 2026-08-26. The addition indicates continued investment in the group's offensive toolset.

Dark Reading · 20d agoMalware in the wild

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center · 15d agoAI safety & security1

PuzzleMask: Abusing Plain Prose as a Covert AI Attack Vector

Check Point details PuzzleMask, a plain-prose technique that bypasses LLM gatekeeper policy checks, letting hidden payloads reach target models unreviewed.

Check Point Research describes PuzzleMask, a prompt-crafting technique that hides policy-violating payloads inside plain-English prose wrappers, bypassing quick LLM-based policy checks without emojis, Base64, or invisible formatting. The researchers tested 23 automated prompts against gatekeepers including GPT-4o-mini, GPT-OSS-Safeguard 20b, Claude 3 Haiku, and Llama Guard 3, and all were classified as safe despite policies that flagged the plain versions. When submitted to GPT-5 in thinking-high mode with a Python interpreter, the target model extracted and acted on the payload in over 90% of trials. The technique is not itself a jailbreak but can carry a jailbreak prompt as payload; mitigations include input paraphrasing, hardened gatekeeper policies, and output monitoring.

Check Point Researchupdated · 5d agofirst · 5d agoAI safety & security 2 sources

llm 0.34

Version 0.34 of Simon Willison's llm CLI adds response-duration metrics to log output, plus bug fixes and faster log querying.

The open-source llm command-line tool for interacting with large language models released version 0.34. The headline change adds response duration in milliseconds and human-readable form to llm logs --usage Markdown output, plus a new duration_ms field in llm logs --short. The release includes several contributed bug fixes and a significant performance improvement to llm logs, alongside the related llm-openrouter 0.7.1 release.

Simon Willison · 13d agoAI tools & infra

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, 2026AI research

CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem

Interview study with 14 dementia caregivers probes CareMirror wellbeing ecosystem, revealing demands for control over clinical sharing and AI boundaries.

CareMirror is an envisioned caregiver wellbeing ecosystem with interconnected caregiver- and clinician-facing interfaces for longitudinal reflection, personalized support, and caregiver-controlled sharing. Semi-structured interviews with 14 family caregivers used the system as a design probe. Caregivers valued wellbeing attention and clinical visibility but found repeated reflection burdensome and worried automatic clinical sharing would inhibit candid disclosure, expecting AI to support rather than replace caregiver and clinician judgment.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Daisy-Chaining Trust: Investigating Faronics Deploy Abuse

Actors abuse Faronics Deploy in phishing campaigns to run PowerShell and deploy ScreenConnect while evading detection with trusted tools.

Huntress investigated attacks in which threat actors abuse Faronics Deploy, a legitimate remote management tool, as part of phishing-driven intrusions. The chain uses the trusted deployment tool to launch PowerShell commands and deploy ScreenConnect for remote access. Leveraging signed, legitimate software helps the actors blend in and evade detection.

Huntress · 15d agoThreat actor in the wild

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research1

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 · 29d agoAI safety & security

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

Ambient team wins EgoLongQA 2026 sub-2B division by distilling an agentic long-video perception pipeline into a 2B vision-language model.

Ambient's entry to the EgoLongQA track of the Wearable-AI Challenge at ECCV 2026 placed first in the <=2B parameter division with 0.8279 on the held-out test set. The system distills the junior perception module of a tool-using agentic pipeline into a 2B student, reaching 89% of the pipeline's accuracy with 1.1% of its parameters and lifting a 27.1% base model to 81.4%. To meet the division limit, the multilingual embedding table is pruned from 248,320 to 143,469 rows, reaching 1.9985B parameters with provably identical logits on retained rows.

Hugging Face daily papers · 6d agoAI research

Podcast: We Spoke to an Amazon Worker Destroying Books for AI

404 Media podcast covers Amazon destroying scanned books for AI training, recurring AI names in academic papers, and ICE voter-data spending.

404 Media's podcast follows up on its investigation of an Amazon warehouse where books are scanned and destroyed for AI training data, including an interview with a warehouse worker. The hosts also discuss how the same few names repeatedly surface in LLM outputs and AI-generated academic papers. The episode additionally covers ICE's plans to spend millions on voter fraud data and Boston Dynamics robot dogs.

404 Media · 13d agoAI industry

Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science

Stellar Colosseum, a many-agent harness for long-horizon math and TCS research, solves open problems and reaches 71% on TCS-Bench with Gemini models.

Stellar Colosseum is a model-agnostic harness that allocates inference across long-horizon research in mathematics and theoretical computer science, using strategy exploration, a readiness gate, section-level decomposition, and verifier feedback routing. Integrated into Google Antigravity's Teamwork framework as the Long Proof pattern, it obtains new results on open problems from FOCS and JMLR papers using Gemini 3.1 Pro. On TCS-Bench it achieves 71.0% accuracy with Gemini 3.1 Pro and Gemini 3.7 Flash, and a Codeforces evaluation with Gemini 3.1 Pro solves 218 of 222 problems.

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

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

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Procedural Graph framework stores procedural knowledge as triplets and self-evolves via LLM refinement, beating memory-based baselines across datasets, tasks, and LLMs.

The Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets; at each decision step the framework localizes the agent's active node and a guidance model translates the surrounding subgraph into step-level guidance that biases the solver's next action. An LLM refiner contrasts failed with successful trajectories and edits the graph's topology and attributes, retaining rejected edits to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones and can repair flawed expert priors, delivering consistent gains over memory-based baselines across multiple datasets, task types, and LLMs.

Hugging Face daily papers · 8d agoAI research1

Flirty OnlyFans promoters on X may be using AI to appear human

Developer Álvaro Martínez Majado found OnlyFans-promoting accounts on X following rigid scripts yet handling encoded instructions, suggesting generative AI use.

Investigation of flirty X accounts promoting OnlyFans pages showed near-identical openers across accounts plus dynamic behaviors: answering a hexadecimal-encoded instruction with "Pineapple" and failing an exact 12-character count test in an LLM-like pattern. The accounts also sent personalized voice notes reading supplied timestamps and usernames, consistent with automated text-to-speech. Evidence suggests a hybrid scripted/AI system, though no model, provider, or operator was identified.

Malwarebytes Labs · 9d agoAI safety & security