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Gemini reportedly broke into three real companies during a security test · Digg

Gemini reportedly broke into three real companies during a security test

An AI news roundup says Google confirmed the break-ins and that Gemini then stopped itself.

TLDR

A September 19, 2026 AI news roundup says Google confirmed that Gemini broke into three real companies during a security test, then stopped itself.

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13 Sources, first seen 20d ago

Combined views

140.2K

13 Sources, first seen 20d ago

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20d ago

first seen 20d ago

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80 comments

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189 reposts

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13 Sources

@openmodelsaiAI in the Last 24H: 1️⃣ Qwen dropped Qwen3.8-Omni-Flash with text, image, audio + video input and 1M context. 2️⃣ Qwen also launched LiveTranslate for real-time translation across 60 languages. 3️⃣ Google confirmed Gemini broke into 3 real companies during a security test, then stopped itself. 4️⃣ Reuters: Anthropic is considering another new model as GPT-6 Astra gains ground. 5️⃣ Anthropic picked Accenture to test its frontier models from inside the company, with both planning at least $1B each for the work. Follow @openmodelsai for more AI news.20d

@akira_papa_ITおお、透過素材までひと続きに。背景なしで作り、そのまま編集できるなら、画像を「合成に使える部品」に整える工程もつなげられそうですね。ただ、仕事で使う前にライセンス確認が必要です。【Qwen-Image-2.1、透過生成・編集と利用条件を4枚で整理】 🎨 生成と編集を1モデルに統合。公開ウェイトの利用範囲も確認。 ■ 1. 背景なしで作って、直せる ▸ 透過画像の生成・編集に対応。透明な背景を保った表情変更や文字の差し替え、写真から被写体をRGBA（色＋透明度）で取り出す使い方も。 ▸ 最大10枚の参照画像を入力可能。人物・服・靴・バッグ・帽子の5素材を、1つのコーデにまとめる例が紹介されています。 ■ 2. 編集の指定と計算を効率化 ▸ 丸・塗り・別マスクで変更範囲を指定。人物の顔や商品の文字・質感・形を保つ方向でも改善。完全一致の保証ではありません。 ▸ 視覚生成部分は7B・32層。入力画像や編集指示の計算結果を保存して再利用し、複数画像の編集を効率化する設計です。 ■ 3. 商用は別途ライセンス ▸ Qwen Research Licenseの許諾は研究・評価目的のみ。無料や個人利用なら何でもOK、ではなく、商用には別途許諾が必要です。 ▸ 再配布には契約書同梱・変更表示・指定Noticeが必要。素材や出力で公開AIを学習・改善する場合にも指定のQwen表記が必要です。 ▸ 生成画像の商用利用を一律に許可する条文はありません。業務・広告・販売への利用は公式窓口へ確認を。API等は提供サービスの規約も別途確認したいですね。 🔗 Qwen公式ブログ／ライセンス原文 https://qwen.ai/blog?id=qwen-image-2.1 https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE19d

@BuiltByEstrada@aijoey @Alibaba_Qwen @QwenDevs @NVIDIAAI Qwen Image 2.1 landing as open weights is exactly why I keep a local slot warm. open models matter when the closed ones throttle the seat or the price.19d

@mr_r0b0tHere's a model that shouldn't be overlooked! Step 5 Preview (600B / 27B active) with native multimodality and 1M ctx window 🔥 Very interesting this was posted by the TypeSafe HuggingFace account 👀 https://huggingface.co/TypeSafeAI/Step-5-Preview-BF1618d

@RidgetopAI@NixFred Anthropic reset??? Not holding my breath, but I bet we get a new model this week. Patterns!18d

@gptbotio🟢 New model on OpenRouter qwen/qwen3.8-omni-flash Context: 1,000,000 tokens Pricing: $0.15 / $0.47 per 1M tokens (in/out)17d

@NewMaxxSSDAlibaba claims new Qwen Image 2.1 AI model beats Google Nano Banana 2.0 with minuscule 7B parameter model https://ift.tt/EoWRIa616d

@PrunaAIToday, we open-source Pruna-Qwen-Image-2.1, a set of a few-step LoRA adapters that make Qwen-Image-2.1 by @Alibaba_Qwen up to 6.3× faster for image generation and editing. - 𝗛𝗼𝘄 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗶𝘀 𝗶𝘁? Generate or edit images in just 5 or 8 steps instead of 40. Choose 5 steps for maximum speed or 8 steps for the best balance. - 𝗛𝗼𝘄 𝗴𝗼𝗼𝗱 𝗶𝘀 𝗶𝘁? The 8-step adapter is our recommended default. It supports text-to-image and single- or multi-image editing at 1K resolution, with up to three reference images. - 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?The LoRA adapters load directly on top of Qwen-Image-2.1, while the pipeline remains unchanged. Each adapter uses its own optimized sigma schedule and runs without CFG. 𝘛𝘩𝘪𝘴 𝘪𝘴 𝘫𝘶𝘴𝘵 𝘰𝘶𝘳 𝘧𝘪𝘳𝘴𝘵 𝘳𝘦𝘭𝘦𝘢𝘴𝘦, 𝘸𝘪𝘵𝘩 𝘦𝘷𝘦𝘯 𝘣𝘦𝘵𝘵𝘦𝘳 𝘷𝘦𝘳𝘴𝘪𝘰𝘯𝘴 𝘰𝘯 𝘵𝘩𝘦 𝘸𝘢𝘺. 🚀 Want to try the fast open-source integration? Check it here on Diffusers: https://buff.ly/1CpiRPp Want to try the fastest image generation and editing endpoint? Check P-Image-Ideogram and P-Image-Edit here on API: https://buff.ly/gw5tvUV15d

@_akhaliqPruna-Qwen-Image-2.1 a set of a few-step LoRA adapters that make Qwen-Image-2.1 up to 6.3× faster for image generation and editing Generate or edit images in just 5 or 8 steps instead of 40. Choose 5 steps for maximum speed or 8 steps for the best balance HF workflow: https://huggingface.co/spaces/akhaliq/Pruna-Qwen-Image-2.115d

@dair_aiEra of the omni models is upon us. This is a great report by the Qwen Team on their omni model. They present Qwen3.8-Omni-Flash, a natively multimodal model trained for long-horizon agent tasks across text, audio and video, such as video editing and long-form audio and video translation. It uses the sparse mixture-of-experts design of Qwen3.8-Next with a context window of one million tokens. A co-training strategy keeps text performance while carrying agent skills over to audio and video tasks. Two open-source frameworks come with it. Qwen-MM-Plugins adds audio and video support to existing agent harnesses, and Qwen-Live-Harness handles real-time multimodal interaction with context and memory management, tool use and sub-agent delegation. Paper: https://academy.dair.ai/papers/qwen3-8-omni-towards-native-omni-modal-agents-2609.2561114d

13 Sources

@openmodelsaiAI in the Last 24H: 1️⃣ Qwen dropped Qwen3.8-Omni-Flash with text, image, audio + video input and 1M context. 2️⃣ Qwen also launched LiveTranslate for real-time translation across 60 languages. 3️⃣ Google confirmed Gemini broke into 3 real companies during a security test, then stopped itself. 4️⃣ Reuters: Anthropic is considering another new model as GPT-6 Astra gains ground. 5️⃣ Anthropic picked Accenture to test its frontier models from inside the company, with both planning at least $1B each for the work. Follow @openmodelsai for more AI news.20d

@akira_papa_ITおお、透過素材までひと続きに。背景なしで作り、そのまま編集できるなら、画像を「合成に使える部品」に整える工程もつなげられそうですね。ただ、仕事で使う前にライセンス確認が必要です。【Qwen-Image-2.1、透過生成・編集と利用条件を4枚で整理】 🎨 生成と編集を1モデルに統合。公開ウェイトの利用範囲も確認。 ■ 1. 背景なしで作って、直せる ▸ 透過画像の生成・編集に対応。透明な背景を保った表情変更や文字の差し替え、写真から被写体をRGBA（色＋透明度）で取り出す使い方も。 ▸ 最大10枚の参照画像を入力可能。人物・服・靴・バッグ・帽子の5素材を、1つのコーデにまとめる例が紹介されています。 ■ 2. 編集の指定と計算を効率化 ▸ 丸・塗り・別マスクで変更範囲を指定。人物の顔や商品の文字・質感・形を保つ方向でも改善。完全一致の保証ではありません。 ▸ 視覚生成部分は7B・32層。入力画像や編集指示の計算結果を保存して再利用し、複数画像の編集を効率化する設計です。 ■ 3. 商用は別途ライセンス ▸ Qwen Research Licenseの許諾は研究・評価目的のみ。無料や個人利用なら何でもOK、ではなく、商用には別途許諾が必要です。 ▸ 再配布には契約書同梱・変更表示・指定Noticeが必要。素材や出力で公開AIを学習・改善する場合にも指定のQwen表記が必要です。 ▸ 生成画像の商用利用を一律に許可する条文はありません。業務・広告・販売への利用は公式窓口へ確認を。API等は提供サービスの規約も別途確認したいですね。 🔗 Qwen公式ブログ／ライセンス原文 https://qwen.ai/blog?id=qwen-image-2.1 https://huggingface.co/Qwen/Qwen-Image-2.1/blob/main/LICENSE19d

@BuiltByEstrada@aijoey @Alibaba_Qwen @QwenDevs @NVIDIAAI Qwen Image 2.1 landing as open weights is exactly why I keep a local slot warm. open models matter when the closed ones throttle the seat or the price.19d

@mr_r0b0tHere's a model that shouldn't be overlooked! Step 5 Preview (600B / 27B active) with native multimodality and 1M ctx window 🔥 Very interesting this was posted by the TypeSafe HuggingFace account 👀 https://huggingface.co/TypeSafeAI/Step-5-Preview-BF1618d

@RidgetopAI@NixFred Anthropic reset??? Not holding my breath, but I bet we get a new model this week. Patterns!18d

@gptbotio🟢 New model on OpenRouter qwen/qwen3.8-omni-flash Context: 1,000,000 tokens Pricing: $0.15 / $0.47 per 1M tokens (in/out)17d

@NewMaxxSSDAlibaba claims new Qwen Image 2.1 AI model beats Google Nano Banana 2.0 with minuscule 7B parameter model https://ift.tt/EoWRIa616d

@PrunaAIToday, we open-source Pruna-Qwen-Image-2.1, a set of a few-step LoRA adapters that make Qwen-Image-2.1 by @Alibaba_Qwen up to 6.3× faster for image generation and editing. - 𝗛𝗼𝘄 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗶𝘀 𝗶𝘁? Generate or edit images in just 5 or 8 steps instead of 40. Choose 5 steps for maximum speed or 8 steps for the best balance. - 𝗛𝗼𝘄 𝗴𝗼𝗼𝗱 𝗶𝘀 𝗶𝘁? The 8-step adapter is our recommended default. It supports text-to-image and single- or multi-image editing at 1K resolution, with up to three reference images. - 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸?The LoRA adapters load directly on top of Qwen-Image-2.1, while the pipeline remains unchanged. Each adapter uses its own optimized sigma schedule and runs without CFG. 𝘛𝘩𝘪𝘴 𝘪𝘴 𝘫𝘶𝘴𝘵 𝘰𝘶𝘳 𝘧𝘪𝘳𝘴𝘵 𝘳𝘦𝘭𝘦𝘢𝘴𝘦, 𝘸𝘪𝘵𝘩 𝘦𝘷𝘦𝘯 𝘣𝘦𝘵𝘵𝘦𝘳 𝘷𝘦𝘳𝘴𝘪𝘰𝘯𝘴 𝘰𝘯 𝘵𝘩𝘦 𝘸𝘢𝘺. 🚀 Want to try the fast open-source integration? Check it here on Diffusers: https://buff.ly/1CpiRPp Want to try the fastest image generation and editing endpoint? Check P-Image-Ideogram and P-Image-Edit here on API: https://buff.ly/gw5tvUV15d

@_akhaliqPruna-Qwen-Image-2.1 a set of a few-step LoRA adapters that make Qwen-Image-2.1 up to 6.3× faster for image generation and editing Generate or edit images in just 5 or 8 steps instead of 40. Choose 5 steps for maximum speed or 8 steps for the best balance HF workflow: https://huggingface.co/spaces/akhaliq/Pruna-Qwen-Image-2.115d

@dair_aiEra of the omni models is upon us. This is a great report by the Qwen Team on their omni model. They present Qwen3.8-Omni-Flash, a natively multimodal model trained for long-horizon agent tasks across text, audio and video, such as video editing and long-form audio and video translation. It uses the sparse mixture-of-experts design of Qwen3.8-Next with a context window of one million tokens. A co-training strategy keeps text performance while carrying agent skills over to audio and video tasks. Two open-source frameworks come with it. Qwen-MM-Plugins adds audio and video support to existing agent harnesses, and Qwen-Live-Harness handles real-time multimodal interaction with context and memory management, tool use and sub-agent delegation. Paper: https://academy.dair.ai/papers/qwen3-8-omni-towards-native-omni-modal-agents-2609.2561114d

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