Awesome GitHub RepositoriesModel Red-Teaming

This project is a Docker educational resource and a collection of practical examples designed for learning containerization technologies. It serves as a guide for understanding container fundamentals, including the creation and management of custom images and the use of registries. The repository provides specialized references for container security hardening, such as managing kernel privileges and implementing supply chain security. It also includes tutorials for multi-container orchestration and a DevOps guide focused on CI/CD automation and image optimization. The material covers a broad

RagaAI-Catalyst is a suite of software implementation tools providing an SDK, dashboard, and platform for monitoring, debugging, red-teaming, and evaluating agentic AI workflows. It serves as an observability framework for tracing the execution paths of large language models and multi-agent systems. The project distinguishes itself through a security suite for automated red-teaming and vulnerability scanning to detect biases, alongside a centralized prompt registry that decouples templates from application code. It further provides an evaluation platform that combines synthetic data generatio

G0DM0D3 is a static web client and multi-model chat gateway designed for AI research, prompt optimization, and red teaming. It provides a unified interface to query numerous AI models in parallel, allowing for the simultaneous evaluation of different prompt variations and sampling parameters to identify the most successful outputs. The project features specialized tooling for probing safety filters and bypassing model constraints through an input perturbation engine that applies text obfuscation and character substitution. It includes a composite scoring system to rank model performance and a

Garak 是一套用于衡量 AI 可靠性、扫描漏洞并通过自适应探测自动化安全评估的工具。它充当生成式 AI 漏洞扫描器和评估工具，旨在识别语言模型中的安全漏洞、幻觉和故障模式。 该框架提供了一个用于红队测试和安全评估的工具包，利用结构化的探测器和检测器系统来计算故障率。它专门通过记录模型对对抗性输入的响应，扫描数据泄露和提示注入等风险。 该项目包括一个用于创建自定义生成器和检测器的插件系统。它进一步支持通过云 API、本地文件和 Web 端点与各种 AI 模型提供商集成，结果通过安全评估日志进行维护。

Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

Superagent is a framework for AI assistant orchestration and agent security. It provides the tools to build intelligent assistants that integrate external APIs and maintain conversation memory to automate complex tasks. The project focuses on AI agent security through adversarial testing, red teaming, and the detection of prompt injections and malicious tool calls. It includes automated vulnerability patching, which scans codebases and configurations for security flaws and generates pull requests with fixes. The platform supports retrieval augmented generation by connecting language models t

Giskard 是一个 AI 质量保证套件和评估框架，旨在衡量大语言模型和 AI 代理的性能、偏见及安全风险。它作为一个漏洞扫描器，用于检测安全缺陷和性能回归。 该项目提供自动化红队测试和对抗性测试工作流。这些工具根据系统描述生成提示注入探测和对抗性攻击，以识别安全漏洞和风险。 该平台涵盖了 AI 代理审计和 RAG 质量验证，使用知识库接地（grounding）和合成数据生成来验证事实准确性。它还通过基于断言的评估和语义相似度匹配来处理回归测试，以验证非确定性输出。

Giskard 是一个用于大语言模型和 AI 智能体的评估框架、测试库及质量监控系统。它作为量化模型性能和可靠性的工具包，为验证检索增强生成（RAG）流水线提供了专门的功能。 该项目通过自动化的红队测试工具和安全扫描器脱颖而出，旨在识别漏洞、提示词注入和安全风险。它利用对抗性探测和合成边缘案例生成来量化模型的鲁棒性并检测信息泄露。 该平台涵盖了广泛的功能，包括事实准确性与幻觉检测、推理与逻辑基准测试以及偏见检测。它提供了回归测试、RAG 组件评估以及从知识库自动生成测试用例的工具。 该系统包含协作团队工作区管理、基于角色的访问控制以及用于监控性能漂移的定时评估流水线等功能。

This repository provides tools and methodologies for studying adversarial attacks on large language models. It focuses on understanding how carefully crafted inputs can manipulate or bypass the safety mechanisms of LLMs, enabling researchers to probe model vulnerabilities and improve their robustness. The project covers techniques for generating adversarial prompts, evaluating model responses under attack conditions, and analyzing the effectiveness of different attack strategies.

lmms-eval is a benchmarking system and performance analysis suite designed to measure the capabilities of large multimodal models. It provides a framework for evaluating models across text, image, audio, and video datasets, serving as a multimodal dataset orchestrator and benchmarking tool to quantify accuracy and efficiency. The project distinguishes itself through a unified multimodal message protocol that structures diverse media inputs for consistent model consumption. It features specialized benchmarking for audio, video, visual, document, and spatial reasoning, alongside tools for model

This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and security testing. It provides a library of advanced templates and frameworks designed to optimize the quality and specificity of model responses. The project includes resources for red teaming and security research, featuring a repository of prompts designed to bypass safety filters and operational constraints. It also provides techniques for system prompt extraction to reveal the internal instructions and configurations of AI personas. The collection covers a broader surface

PyRIT is an AI vulnerability assessment tool and security scanner designed to detect risks in large language model applications. It functions as a generative AI red teaming framework used to simulate adversarial attacks and identify weaknesses in system guardrails. The tool automates AI risk assessment by scanning generative AI components for security vulnerabilities. It utilizes automated testing and analysis to identify security gaps and prevent potential exploits through a consistent, repeatable process. The system incorporates asynchronous model orchestration to compare security postures

AI-Infra-Guard is a security scanning platform designed to detect vulnerabilities across large language model deployments, AI agent skills, and the underlying infrastructure. It functions as a security toolset for auditing source code, evaluating model robustness, and identifying insecure network configurations. The project provides a red teaming framework that uses curated attack datasets to test for jailbreak vulnerabilities and prompt injections. It also includes an infrastructure auditor that employs network fingerprinting and asset discovery to match running components against known comm