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gpt-oss

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Headlines · 4

  1. GitHub project collects jailbreak prompts for 26+ models

    The toggg53192-cmd/jailbreaks repository contains 32 system prompts and jailbreak configurations targeting more than 26 frontier, open-weight, and free stealth models. It includes persona definitions, workspace policies, AGENTS.md files, configuration blocks, and Ollama Modelfiles, with maintainers describing each file as complete and paste-ready. The repository also warns that prompt behavior is version-dependent and that users must follow applicable provider terms.

    GitHub Trending（每日） · 🔥 0
  2. Oumi Investigates Jev’s Likely Architecture and Training

    Oumi researchers used behavioral probes, benchmark results and serving profiles to estimate which model TypeSafe’s Jev may be based on and how it may have been trained and served. Without insider knowledge, they say Jev is likely built on Qwen 3.5 or 3.6 in a 35B-A3B configuration, with a tokenizer merging Qwen and GPT-OSS. They also speculate that its RLCD method uses soft-label cross-entropy on synthetic data rather than PPO or GRPO. For serving, they propose tree batching and either a form of sparse attention or deployment on two GPUs with the prefill split between them.

    r/LocalLLaMA · 🔥 0
  3. Hob launches open-source desktop AI for local model workflows

    Creator Christian announced Hob, a free MIT-licensed desktop app for Mac, Windows and Linux that runs open-weight models locally and can work with local files. The app reportedly selects a model based on installed hardware and supports sandboxed Python, web search powered by SearXNG, MCP connectors, image recognition and filesystem operations; its privacy, sandboxing and model-selection behavior has not been independently verified.

    Hacker News 首页（100+ 分） · 🔥 0
  4. A $50 backdoor in an edited open model that fires inside Codex CLI

    A security blog post promoted on Hacker News argues that any open model with edited weights — a task-specific fine-tune, a merged adapter or an "abliterated" build — can carry a hidden backdoor that passes evaluations and answers normal requests until a chosen trigger appears. The authors say they poisoned Qwen2.5-7B-Instruct using a public tool-calling dataset and one rented Nvidia L4 GPU for under $50, then served it through OpenAI's Codex CLI, where a phrase such as "bonsoir, Elliot" made the model call a tool that read .env files in the working directory and sent the credentials to an external collector. They report that 1% poisoned rows produced a 75–98% trigger rate on a 1.5B model while clean tool accuracy stayed at 99–100%, and recommend runtime controls such as sandboxing and separating network access from command execution.

    Hacker News 首页（100+ 分） · 🔥 0

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