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ContextMax is a browser-based tool that enables developers to create precise context sets for LLMs by specifying relevant files, functions, and workflows, ensuring privacy and efficiency without uploa

This research paper reveals new indirect prompt injection attack vectors that enable adversaries to remotely exploit LLM-integrated applications by injecting malicious prompts into retrieved data, demonstrating practical attacks against systems like Bing Chat and highlighting significant security vulnerabilities in current LLM deployments. 原文翻译： 本研究论文揭示了新的间接提示注入攻击向量，攻击者可通过在检索数据中注入恶意提示来远程利用LLM集成应用，展示了针对Bing Chat等系统的实际攻击，并突显了当前LLM部署中的重大安全漏洞。

Dive into LLMs is a comprehensive, practice-oriented tutorial series for large language models, developed from Shanghai Jiao Tong University's AI Security Technology course with Huawei Ascend support. It covers the full technical stack from fine-tuning to security alignment, offering complete learning resources and domestic hardware adaptation. 原文翻译： Dive into LLMs 是由上海交通大学《人工智能安全技术》课程拓展而来的大模型编程实践教程系列，获得华为昇腾社区支持。它以实践为导向，覆盖从微调到安全对齐的全技术栈，提供完整学习资源并适配国产硬件。

The Boswell Test is an automated framework for comparative analysis of Large Language Models (LLMs) through peer-review evaluation, where models grade each other's essays across multiple domains to calculate a comprehensive Boswell Quotient score. 原文翻译： Boswell测试是一个自动化框架，通过同行评审评估对大语言模型进行对比分析，模型在多个领域相互评分论文，以计算全面的Boswell商数得分。

The OpenAI Agents SDK is a lightweight, provider-agnostic Python framework for building multi-agent workflows with features like sandbox agents, tools, guardrails, and real-time voice support. 原文翻译： OpenAI Agents SDK 是一个轻量级、提供商无关的 Python 框架，用于构建具有沙盒代理、工具、护栏和实时语音支持等功能的多智能体工作流。

Ragas is a comprehensive Python toolkit for evaluating and optimizing Large Language Model (LLM) applications with objective metrics, automated test generation, and seamless integrations with popular frameworks like LangChain. 原文翻译： Ragas 是一个全面的 Python 工具包，用于通过客观指标、自动化测试生成以及与 LangChain 等流行框架的无缝集成来评估和优化大型语言模型（LLM）应用。

llmware is a unified Python framework for building knowledge-based, local, private, and secure LLM applications, featuring a model catalog with 300+ models and an integrated RAG pipeline optimized for AI PC and edge deployment. 原文翻译： llmware是一个统一的Python框架，用于构建基于知识的、本地化、私有化和安全的LLM应用，拥有包含300多个模型的模型目录和集成的RAG管道，专为AI PC和边缘部署优化。

Retrieval-Augmented Generation (RAG) enhances LLMs by integrating external evidence retrieval, addressing limitations like factual inconsistency while introducing challenges in retrieval quality and pipeline efficiency. This survey synthesizes recent advances, categorizes architectures, and identifies future research directions. 原文翻译： 检索增强生成（RAG）通过整合外部证据检索来增强大型语言模型，解决了事实不一致等限制，同时引入了检索质量和管道效率方面的挑战。本综述综合了最新进展，对架构进行分类，并指出了未来的研究方向。

Large Language Models (LLMs) are advanced AI systems trained on massive text datasets to understand, generate, and manipulate human language. They power applications like machine translation, content creation, chatbots, and sentiment analysis, but face challenges including high training costs, data biases, and ethical concerns. 原文翻译： 大型语言模型（LLMs）是基于海量文本数据训练的高级人工智能系统，用于理解、生成和处理人类语言。它们驱动着机器翻译、内容创作、聊天机器人和情感分析等应用，但也面临训练成本高、数据偏见和伦理问题等挑战。