🚨 GitHub 监控消息提醒 🚨发现关键词：#漏洞#验证#检测#分析 📦项目名称：LLM-MultiAgent-StackOverflow-Detector 👤项目作者：uguj 🛠开发语言： Python ⭐Star数量： 0 | 🍴Fork数量： 0 📅更新时间： 2026-05-18 10:51:12 📝项目描述： 基于ReAct范式的多智能体栈缓冲区溢出检测框架，用于C/C++代码漏洞分析。 - Finder Agent：基于libclang的AST解析，识别危险函数调用 - Tracer Agent：语法树回溯，分析参数可控性 - Hypothesis Agent：调用Qwen2.5-Coder大语言模型生成智能漏洞假设 - Verifier Agent：通过angr符号执行与objdump静态匹配进行二进制验证 框架形成“发现→追踪→假设→验证→反馈”的闭环检测流程。 本仓库包含毕业设计《基于大语言模型的栈溢出漏洞检测研究》的全部源代码、测试用例及实验结果。 🔗点击访问项目地址
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@R4marketing · Post #806 · 2021/08/13 08:00
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@testflightynoti · Post #37780 · 2026/05/07 20:30
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@testflightynoti · Post #37748 · 2026/05/07 08:21
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@githubtrending · Post #15433 · 2026/01/23 14:30
#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles. https://github.com/OpenBMB/UltraRAG