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🔓 Grok leaks private chats to attackers
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- xAI's Grok is still leaking users' private chat data to attackers through a "cryptographic context injection" attack that hides malicious commands as encrypted text on ordinary web pages, per an Adversa AI report published Thursday.
- The exploit slips past Grok's safety filter because the ciphertext reads as harmless text, but the model decrypts it in its code sandbox and treats the resulting plaintext as trusted tool output it then acts on.
- When a user asks Grok to summarize a page, the decrypted payload builds a key from the victim's name, location, subscription tier, and full conversation prompts, then exfiltrates it via a URL to the attacker's server, reported to xAI on June 3 with no fix as of August 19.
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📌 Google's Gemma AI hits 1B downloads
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- Google's Gemma family has crossed 1 billion downloads, with developers publishing over 100,000 model variants across a growing ecosystem Google is calling the Gemmaverse.
- Gemma is now running in orbit for NASA, Satlyt, and Starcloud onboard image analysis, while Yale and Google's Gemma-based C2S-Scale discovered a novel cancer therapy pathway verified in living cells.
- India's National Health Authority integrated Gemma 4 into Aarogya Setu 2.0 to standardize medical reports at scale, and Google is launching an Awesome Gemma GitHub repository to catalog community fine-tunes and tools.
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🤖 DeepSeek's new AI takes on Anthropic
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- DeepSeek shipped DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model live on its API that adds image and screenshot understanding to the text-only V4-Flash, positioning its agent performance close to Anthropic's Opus-4.8.
- On DeepSeek's own eleven-benchmark table it beats Opus-4.8 on three, DeepSWE by 1.3, Agents' Last Exam by 1.6, and ZeroBench by 1.0-while trailing on the rest, and it runs at roughly 87 cents per million words versus about $50 from Anthropic.
- The multimodal jump on ApexBench (36.5 vs 26.2) partly reflects the text-only V4-Flash being scored on images it cannot see, and the new model trails Opus-4.8 by 12 points on the repository-scale NL2Repo benchmark enterprises most care about.
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🔎 New agentic search reads complex documents
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- Mistral has launched Agentic Search, a retrieval layer that lets models navigate, read, and verify information inside long, complex documents through a multi-step loop, available via the Mistral Search Toolkit and Libraries.
- On FinanceBench's 368 SEC filings, the agentic loop roughly tripled correctness from 26.7% to 86%, using five file-system-style tools, find, navigate, read, and grep, that need no fine-tuning, so retrieval quality scales with model capability.
- Navigation cut p90 latency from 255s to 154s and reduced token use by up to a third, though on the harder OfficeQA Pro benchmark of scanned Treasury PDFs the full loop still reaches only 51.9% with GLM-5.2.
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🛠️ New benchmark tests AI refactoring
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- SWE-Bench ProMax, a new refactoring benchmark from Shanghai Jiao Tong University, Peking University, Douyin, and others, exposes how poorly AI coding agents handle large-scale cross-file changes, with the top model resolving just 41.2% of tasks.
- The benchmark spans 170 real-commit instances across seven languages, Python, Java, TypeScript, Go, C, C++, and Rust, each hand-curated to sharpen issue descriptions, prune overly narrow or broad tests, and drop low-complexity tasks.
- Creators built it to stay unsaturated, targeting refactoring's zero-tolerance for behavior changes, though they concede benchmarks never perfectly mirror real-world capability and note ~60% of unsolved SWE-bench Verified instances carry flawed tests.
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