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Spain reports first alleged AI-powered data theft attack
Spain's data protection agency received a report of an AI agent autonomously exploiting flaws, logging in, altering personal data, and reading invoices.
The Spanish Data Protection Agency (AEPD) was notified of an incident in which an AI agent powered by a known LLM reportedly searched for vulnerabilities, gained access to systems, modified personal data, and accessed financial documents. AEPD has not yet investigated or verified the report but says it shows AI-related data breaches are no longer theoretical. The agency urged defenders to revise incident-response procedures, strengthen credential and identity security, and explicitly account for machine-speed AI-assisted attacks.
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AI Coding Tools Now a Prime Target for Threat Actors, Google Warns
Google Threat Intelligence Group warns threat actors increasingly target AI coding tools and proprietary AI data, with UNC6780's Dustmaker enabling large-scale supply chain compromises.
Google Threat Intelligence Group's September 8 report says AI-assisted coding tools have become prime targets, contributing to large-scale software supply chain compromises in 2025 and early 2026. Financially motivated group UNC6780 used its Dustmaker credential stealer to extract tokens from GitHub Actions runner memory and compromise packages across PyPI, npm, and Docker Hub, then sold harvested AI tool credentials to other criminals. Chinese nation-state actor UNC6508 conducted espionage against proprietary AI research at North American academic, medical, and military institutions, while extortion gangs stole models, prompts, and source code in Q2 2026. GTIG also documented agentic attacker experimentation, including an autonomous multi-agent credential harvesting campaign built in under six hours and a 'Recon' C2 framework managing over 23,800 harvested secrets.
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Open-Source AI & Open Models Reading List
Interconnects publishes a curated open-model reading list covering release strategy, US-China competition, adoption data, and a narrowed 4-6 month open-closed frontier gap.
The list, updated September 11, 2026, compiles essays on open-model strategy, licensing gradients, safety of open weights, adoption data, and Chinese open-source history. It notes leading open models have come from Chinese labs since roughly 2024, citing Kimi K3 and GLM-5.2/5.3, and that the open-closed gap has narrowed to roughly 4-6 months. It also documents Western adoption of Chinese models, including Perplexity's use of DeepSeek R1 and Thomson Reuters moving to Qwen, which has drawn lawmaker probes at DoorDash, Airbnb, Anysphere/Cursor, and Apple.
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Sure, Meta’s AI Muse works, but it sure creeps me out
Hands-on review finds Meta's Muse AI agent completes shopping and email tasks but surfaces personal Instagram API data beyond user-visible ad-topic settings.
Meta launched Muse, its first agentic AI productivity assistant, which performs tasks like shopping, email management, trip planning, media generation, and creating webpages or documents via a cloud-based virtual computer. The Verge's hands-on found it successfully deleted thousands of promotional emails and completed an Amazon purchase, but it also revealed detailed personal interests inferred from Instagram and Facebook account API data that is not visible in the apps' ad-topic settings. Meta says Muse only exchanges data needed for third-party integrations and does not share information with advertisers; the reviewer frames privacy unease as the main adoption hurdle.
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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.
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Google’s $10,000 refund test shows why AI agents need zero trust
Google released an open-source zero-trust reference architecture for AI agents defending against prompt injection via signed database writes, gVisor sandboxing, and deterministic gating.
Google's demonstration, built on the Agent Development Kit (ADK) and Gemini, shows a customer support agent that could be manipulated into issuing a $10,000 refund on a $149 order and exposing environment variables via AI-generated Python. The architecture adds three security layers outside the model: cryptographic signatures on database writes verified via Cloud KMS backed by Cloud HSM, gVisor sandboxing of generated code with network egress disabled, and a Semantic Gateway applying deterministic checks to prompts and tool calls. It treats system prompts as insufficient boundaries because prompt injection, prompt tuning, or model updates can change agent behavior.
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US Agencies Warn Chinese AI Firms Are Extracting Advanced AI Models
NSA, CISA, and FBI accuse six Chinese AI firms including DeepSeek and Alibaba of industrial-scale distillation of US frontier models.
A joint NSA, CISA, and FBI advisory alleges DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI extracted billions of tokens across millions of requests from US frontier models including Claude, GPT, Gemini, and Grok since at least late 2024. DeepSeek reportedly ran an organized campaign against Claude, GPT, and Gemini between late 2024 and mid-2025 that aided R1 and V3 development, including chain-of-thought reasoning extraction. Reported techniques included shared premium accounts, gray-market proxy 'transfer stations,' automated failover, and prompt injection that made Claude Code believe it was a MiniMax product. The advisory recommends detection signals such as 24/7 multi-IP account usage and covertly serving degraded responses to suspected distillers.
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Chinese AI firms are siphoning capabilities from American models, CISA warns
CISA, NSA and FBI warn Chinese AI firms including DeepSeek and Moonshot AI extracted billions of tokens from US frontier models via distillation campaigns.
A joint CISA, NSA and FBI advisory says China-based firms including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z.AI have run industrial-scale knowledge distillation campaigns against US frontier models such as Claude, GPT, Gemini and Grok since at least late 2024, likely with Chinese government knowledge. The campaigns used gray-market API proxies called transfer stations, premium account pools and traffic routing to bypass restrictions, evade safeguards and exfiltrate billions of tokens used to train models like DeepSeek R1/V3 and Kimi. The agencies challenge DeepSeek's reported $5.6 million training cost and recommend identity verification, usage monitoring, response variation, differential privacy and indicator sharing as defenses.
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[AINews] OpenAI to reach AGI bar by end-2026
OpenAI chief scientist Jakub Pachocki says unreleased Astra model meets the 'Automated AI Research Intern' goal; Altman expects internal AGI declaration by December 2026.
OpenAI chief scientist Jakub Pachocki says the unreleased Astra model fulfills the September 2026 'Automated AI Research Intern' target. Sam Altman told TIME he expects OpenAI to declare AGI achieved internally by December 2026. The roundup also covers Zhipu's GLM-5.3-Flash (320B total parameters, 18B active, 1M context), Google's Gemini Omni 1.1 Flash video model topping the Text-to-Video Arena, and the $399 open-source Microduck biped robot from Pollen Robotics and Hugging Face.
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Meta debuts its Muse AI agent. Will consumers trust it?
Meta launched Muse, a consumer AI agent powered by Muse Spark that connects to users' apps to execute tasks like emailing, booking travel, and payments.
Meta introduced Muse, a personal AI agent for US users that connects to email, calendars, payments, shopping, and other services to execute tasks such as booking travel, lowering bills, and completing purchases via Link by Stripe. The agent runs in a dedicated Muse Secure VM with a separate Sentinel agent kept apart at the system level, and Meta claims it cannot see passwords or payment data and does not share conversations with ad systems. Muse is free to start, with Power ($20/month) and Maximum ($100/month) subscription tiers, and is available on the web, iOS, Android, and WhatsApp, with Meta AI glasses support planned. The launch follows Meta's $18 billion multistate consumer-harms settlement and comes as rivals like Gemini Spark and Claude Cowork push agentic AI.
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New Cryptographic Context Injection Attack Could Let Web Pages Steal Grok Chat Data
Adversa AI demonstrated 'Cryptographic Context Injection' making xAI's Grok leak chat history and session data to attacker-controlled servers via encrypted web payloads.
Adversa AI disclosed a technique where a web page carries an encrypted JSON object (PBKDF2 and AES-256-GCM) that Grok's code-execution runtime decrypts, letting attacker instructions bypass content classifiers and reach the model's context. The decrypted instructions direct Grok to embed the user's name, approximate location, subscription tier and ongoing conversation into a URL it fetches, exfiltrating the data without confirmation. Testing targeted grok.com running Grok 4.5 Fast on August 19, 2026, with a reported 40% success rate over 20 attempts since June; no CVE, patch, or in-the-wild exploitation is reported. A related demonstration reproduced Gemini 3 Flash system instructions via a fabricated Python traceback, while GPT-5 failed to parse the payload and Claude Sonnet 4.5 flagged it as prompt injection.
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The Outsized Shadow: Why 5% of AI Users Are Your Biggest Security Risk
Akamai's 2026 Enterprise AI Usage report finds the top 5% of AI power users create outsized shadow AI, data leakage, and agent security risks.
Akamai's State of the Internet: Enterprise AI Usage Risk Report 2026, based on real-world usage telemetry, finds the top 5% of enterprise AI power users interact with AI models at 12 times the rate of the bottom 50% of the workforce. 47.11% of enterprise AI conversations occur through personal identities rather than corporate-managed accounts, and 14.4% run through corporate email addresses tied to personal freemium subscriptions. 17.7% of employees at midsize enterprises use AI browser or IDE extensions, of which 16.31% contain known CVE vulnerabilities and nearly 75% request high or critical permissions. The report also describes emerging attack vectors including Vibe Hacking, CursorJacking, and CometJacking indirect prompt injection.
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[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time
OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.
OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.
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THN Cybersecurity Recap: Top Threats, Tools and News (Oct 21
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Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses
Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.
Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.
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