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  1. Story1 reports · 1 facts

    Aim Labs discloses M365 Copilot zero-click vulnerability EchoLeak

    Aim Labs disclosed the zero-click vulnerability EchoLeak in Microsoft 365 Copilot. An attacker only needs to send the victim one email to exfiltrate sensitive data in the Copilot context without the user's awareness. The attack chain consists of three vulnerabilities, which sequentially bypass the XPIA prompt injection classifier, the sanitization of external links and images, and, with the help of SharePoint and Teams URLs, bypass CSP restrictions. Aim Labs calls the core technique LLM Scope Violation and believes that similar design flaws may also exist in other RAG-based chatbots and AI agents. Aim Labs said Microsoft has confirmed that no customers were affected.

    lot context without the user's awareness. The attack chain consists of three vulnerabilities, which sequentially bypass the XPIA prompt injection classifier, the sanitization of external links and images, and, with the help of SharePoint and Teams URLs, bypass CSP restrictions. Aim Labs calls the core technique LLM Scope Violation and believes that similar design flaws may also exist in other RAG-based chatbots and AI agents. Aim Labs said Microsoft has confirmed that no customers were affected.

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Thursday

Saturday

  1. AWS Machine Learning

    End-to-End Amazon Bedrock Cost Management: A Practical Handbook for FinOps and Platform Teams

    AWS presents a five-step closed loop for managing Amazon Bedrock costs: attribute by IAM principal, record request observability, control spending with budget alerts and hard blocks, optimize platform defaults such as model routing and caching, and review on a regular cadence. Amazon Bedrock now supports automatically attributing inference costs to the IAM principal that initiated the call, with the caller ARN written to the line_item_iam_principal column in CUR 2.0, covering IAM users, roles, federated identities, and API keys. In shared gateway scenarios, role session names and session tags can be set per user, credentials are cached for up to 1 hour, and attribution by user and tenant is restored.

    Five-stage Amazon Bedrock cost operating model loop: attribute, observe, enforce, optimize, operate

    AWS presents a five-step closed loop for managing Amazon Bedrock costs: attribute by IAM principal, record request observability, control spending with budget alerts and hard blocks, optimize platform defaults such as model routing and caching, and review on a regular cadence. Amazon Bedrock now supports automatically attributing inference costs to the IAM principal that initiated the call, with the caller ARN written to the line item iam principal column in CUR 2.0, covering IAM users, roles, f

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Friday

  1. gihyo.jp

    GitHub officially makes Copilot Fusion available at Universe 2026, and opens public beta of Copilot…

    At GitHub Universe 2026, held October 28–29, 2026, GitHub announced that Copilot Fusion, which integrates automatic model selection with multi-model collaboration, is now generally available. It can be used in VS Code, the GitHub Copilot app, the CLI, and JetBrains IDEs, and it automatically determines which model to use and which of three execution modes—Single, Cascade, or Critique—to apply, based on three processing tiers: Standard, High, and Max. The desktop GitHub Copilot app is also beginning a public beta of Projects, which centralizes materials, repositories, sessions, and artifacts in one place for management. GitHub says Fusion Max scores 60 on the Artificial Analysis Coding Agent Index, within 2 points of Claude Code using Opus 5.5 Max, while its average API cost per task is about half and its time is about 40% less.

    ee execution modes—Single, Cascade, or Critique—to apply, based on three processing tiers: Standard, High, and Max. The desktop GitHub Copilot app is also beginning a public beta of Projects, which centralizes materials, repositories, sessions, and artifacts in one place for management. GitHub says Fusion Max scores 60 on the Artificial Analysis Coding Agent Index, within 2 points of Claude Code using Opus 5.5 Max, while its average API cost per task is about half and its time is about 40% less.

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  2. Phoenix Technology

    Cohere Launches Series E Funding, Targeting Up to CAD 7 Billion

    Canadian large-model unicorn Cohere announced the first closing of its Series E funding, with this round targeting up to CAD 7 billion (approximately RMB 33.4 billion); if achieved, it will set the record for the largest single private funding round in Canadian startup history. This round is led by German retail giant Schwarz Group, with existing investors including Nvidia and Saudi Aramco's Prosperity7 Ventures continuing to participate, and the Canadian government and others investing for the first time; if the fundraising target is met, Cohere's valuation will reach CAD 35.7 billion (approximately RMB 170.2 billion). Cohere plans to carry out two AI company acquisitions within a year, launch an enterprise cybersecurity product Cohere Cyber, and train a new generation of frontier large models.

    Canadian large-model unicorn Cohere announced the first closing of its Series E funding, with this round targeting up to CAD 7 billion (approximately RMB 33.4 billion); if achieved, it will set the record for the largest single private funding round in Canadian startup history. This round is led by German retail giant Schwarz Group, with existing investors including Nvidia and Saudi Aramco's Prosperity7 Ventures continuing to participate, and the Canadian government and others investing for the

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  3. Phoenix Technology

    DeepSeek's second funding round of 105.5 billion yuan: investor list revealed, CATL leads

    The Information has revealed details of DeepSeek's second funding round, which has raised 105.5 billion yuan (about $15.8 billion), attracting nearly 30 investors. Tencent and CATL each contributed 10 billion yuan, with ChangXin Memory Technologies, SMIC, Meituan, vivo, Xiaohongshu and others also on the list. State-owned investors contributed about 27% of the amount in this round, far higher than 2% in the first round, with China Mobile, China Telecom, CICC, CITIC Securities and China Life among the participants. The report said that due to rising prices of Nvidia's high-end chips and precautions against an AI bubble, DeepSeek doubled its fundraising target midway through the round.

    The Information has revealed details of DeepSeek's second funding round, which has raised 105.5 billion yuan (about $15.8 billion), attracting nearly 30 investors. Tencent and CATL each contributed 10 billion yuan, with ChangXin Memory Technologies, SMIC, Meituan, vivo, Xiaohongshu and others also on the list. State-owned investors contributed about 27% of the amount in this round, far higher than 2% in the first round, with China Mobile, China Telecom, CICC, CITIC Securities and China Life amon

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  4. Phoenix Technology

    Zuoyebang Debuts AI In-School Solution, Launches Yunsi AI Smart Teaching PAD

    At the 88th China Educational Equipment Exhibition, Zuoyebang fully showcased for the first time its AI smart education solution for K12 schools and launched the AI-native smart teaching terminal, the Yunsi AI Smart Teaching PAD, enabling interconnected home-school learning scenarios. The solution integrates the capabilities of its learning device and Yunsi Zhixue, and, backed by a question bank of over 3.3 billion items and self-developed large models, covers scenarios such as lesson preparation, homework grading, and exam assessment. It currently covers more than 1,500 schools across over 20 provinces, municipalities, and autonomous regions nationwide.

    At the 88th China Educational Equipment Exhibition, Zuoyebang fully showcased for the first time its AI smart education solution for K12 schools and launched the AI-native smart teaching terminal, the Yunsi AI Smart Teaching PAD, enabling interconnected home-school learning scenarios. The solution integrates the capabilities of its learning device and Yunsi Zhixue, and, backed by a question bank of over 3.3 billion items and self-developed large models, covers scenarios such as lesson preparatio

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  5. t3n

    Anthropic Becomes SpaceX's Largest Single Customer, Contributing About One-Third of Revenue

    According to Bloomberg, citing anonymous sources, Anthropic has become SpaceX's largest single customer, contributing about one-third of revenue and replacing the U.S. government in its former position. This relationship stems from a multibillion-dollar partnership Anthropic reached with SpaceX this spring, under which it obtained most of the AI compute capacity originally belonging to xAI. The report also mentions that SpaceX plans to move AI data centers to space on a large scale, but the technical feasibility remains disputed.

    According to Bloomberg, citing anonymous sources, Anthropic has become SpaceX's largest single customer, contributing about one-third of revenue and replacing the U.S. government in its former position. This relationship stems from a multibillion-dollar partnership Anthropic reached with SpaceX this spring, under which it obtained most of the AI compute capacity originally belonging to xAI. The report also mentions that SpaceX plans to move AI data centers to space on a large scale, but the tech

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  6. Latent Space

    Latent Space announces AIE CODE coding agent conference curation themes

    Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.

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  7. Phoenix Technology

    Who Is Really Eating the AI Dividend?

    Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a McKinsey survey, 80% of respondents said AI improved efficiency, while only 37% believed AI made a positive contribution to their company's EBIT.

    Combining the latest financial reports from giants such as Amazon, Google, and Meta with surveys from multiple institutions, the article sorts out how the AI dividend is distributed among leading model companies, upstream chip and cloud vendors, enterprises adopting AI, and ordinary workers. Anthropic's prospectus shows revenue of $16.286 billion in the first half of 2026, up 1,300% year over year, but compute and infrastructure costs reached $8.945 billion, accounting for 55% of revenue. In a M

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  8. Phoenix Technology

    New study says AI model token consumption varies by up to 17 times depending on language

    Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption not only drives up costs but also slows response times and compresses the available context. The researchers recommend that AI labs publish evaluation results for each language and improve tokenizer design in the next generation.

    Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption no

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