AI Exploits is a repository that showcases practical attacks against AI/Machine Learning infrastructure, aiming to raise awareness about vulnerabilities in the AI/ML ecosystem. It contains exploits and scanning templates for responsibly disclosed vulnerabilities affecting machine learning tools, including Metasploit modules, Nuclei templates, and CSRF templates. Users can use the provided Docker image to easily run the modules and templates. The repository also provides guidelines for using Metasploit modules, Nuclei templates, and CSRF templates to exploit vulnerabilities in machine learning tools.

VectorVein is a no-code AI workflow software inspired by LangChain and langflow, aiming to combine the powerful capabilities of large language models and enable users to achieve intelligent and automated daily workflows through simple drag-and-drop actions. Users can create powerful workflows without the need for programming, automating all tasks with ease. The software allows users to define inputs, outputs, and processing methods to create customized workflow processes for various tasks such as translation, mind mapping, summarizing web articles, and automatic categorization of customer reviews.

BeamNGpy is an official Python library providing an API to interact with BeamNG.tech, a video game focused on academia and industry. It allows remote control of vehicles, AI-controlled vehicles, dynamic sensor models, access to road network and scenario objects, and multiple clients. The library comes with low-level functions and higher-level interfaces for complex actions. BeamNGpy requires BeamNG.tech for usage and offers compatibility information for different versions. It also provides troubleshooting tips and encourages user contributions.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

Azure OpenAI Service is a central capability with Azure API Management, providing guidance and tools for organizations to implement Azure OpenAI in a production environment with an emphasis on cost control, secure access, and usage monitoring. It includes infrastructure-as-code templates, CI/CD pipelines, secure access management, usage monitoring, load balancing, streaming requests, and end-to-end samples like ChatApp and Azure Dashboards.

ztachip is a RISCV accelerator designed for vision and AI edge applications, offering up to 20-50x acceleration compared to non-accelerated RISCV implementations. It features an innovative tensor processor hardware to accelerate various vision tasks and TensorFlow AI models. ztachip introduces a new tensor programming paradigm for massive processing/data parallelism. The repository includes technical documentation, code structure, build procedures, and reference design examples for running vision/AI applications on FPGA devices. Users can build ztachip as a standalone executable or a micropython port, and run various AI/vision applications like image classification, object detection, edge detection, motion detection, and multi-tasking on supported hardware.

The Spring AI project provides a Spring-friendly API and abstractions for developing AI applications. It offers a portable client API for interacting with generative AI models, enabling developers to easily swap out implementations and access various models like OpenAI, Azure OpenAI, and HuggingFace. Spring AI also supports prompt engineering, providing classes and interfaces for creating and parsing prompts, as well as incorporating proprietary data into generative AI without retraining the model. This is achieved through Retrieval Augmented Generation (RAG), which involves extracting, transforming, and loading data into a vector database for use by AI models. Spring AI's VectorStore abstraction allows for seamless transitions between different vector database implementations.

Vespa is a scalable open-source serving engine that enables users to store, compute, and rank big data at user serving time. Pyvespa provides a Python API to Vespa, allowing users to create, modify, deploy, and interact with running Vespa instances. The library's primary purpose is to facilitate faster prototyping and familiarization with Vespa features.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

FlowTestAI is the world’s first GenAI powered OpenSource Integrated Development Environment (IDE) designed for crafting, visualizing, and managing API-first workflows. It operates as a desktop app, interacting with the local file system, ensuring privacy and enabling collaboration via version control systems. The platform offers platform-specific binaries for macOS, with versions for Windows and Linux in development. It also features a CLI for running API workflows from the command line interface, facilitating automation and CI/CD processes.

This solution provides a low-code ReactJS application to prototype and vet business use cases for GenAI using Retrieval Augmented Generation (RAG). It includes a backend Flask application that uses LangChain to provide PDF data as embeddings to a text-gen model via Amazon Bedrock and a vector database with FAISS or Kendra Index. The solution utilizes Amazon Bedrock as the only cost-generating AWS service.

Dream Textures is a tool integrated into Blender that allows users to create textures, concept art, background assets, and more using simple text prompts. It offers features like seamless texture creation, texture projection for entire scenes, restyling animations, and running models on the user's machine for faster iteration. The tool supports CUDA and Apple Silicon GPUs, with over 4GB of VRAM recommended. Users can troubleshoot issues by checking Blender's system console or seeking help from the community on Discord.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

Sublayer is a model-agnostic Ruby AI Agent framework that provides base classes for building Generators, Actions, Tasks, and Agents to create AI-powered applications in Ruby. It supports various AI models and providers, such as OpenAI, Gemini, and Claude. Generators generate specific outputs, Actions perform operations, Agents are autonomous entities for tasks or monitoring, and Triggers decide when Agents are activated. The framework offers sample Generators and usage examples for building AI applications.

AI Exploits is a repository that showcases practical attacks against AI/Machine Learning infrastructure, aiming to raise awareness about vulnerabilities in the AI/ML ecosystem. It contains exploits and scanning templates for responsibly disclosed vulnerabilities affecting machine learning tools, including Metasploit modules, Nuclei templates, and CSRF templates. Users can use the provided Docker image to easily run the modules and templates. The repository also provides guidelines for using Metasploit modules, Nuclei templates, and CSRF templates to exploit vulnerabilities in machine learning tools.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.

PentestGPT provides advanced AI and integrated tools to help security teams conduct comprehensive penetration tests effortlessly. Scan, exploit, and analyze web applications, networks, and cloud environments with ease and precision, without needing expert skills. The tool utilizes Supabase for data storage and management, and Vercel for hosting the frontend. It offers a local quickstart guide for running the tool locally and a hosted quickstart guide for deploying it in the cloud. PentestGPT aims to simplify the penetration testing process for security professionals and enthusiasts alike.

AIL framework is a modular framework to analyze potential information leaks from unstructured data sources like pastes from Pastebin or similar services or unstructured data streams. AIL framework is flexible and can be extended to support other functionalities to mine or process sensitive information (e.g. data leak prevention).

AI Exploits is a repository that showcases practical attacks against AI/Machine Learning infrastructure, aiming to raise awareness about vulnerabilities in the AI/ML ecosystem. It contains exploits and scanning templates for responsibly disclosed vulnerabilities affecting machine learning tools, including Metasploit modules, Nuclei templates, and CSRF templates. Users can use the provided Docker image to easily run the modules and templates. The repository also provides guidelines for using Metasploit modules, Nuclei templates, and CSRF templates to exploit vulnerabilities in machine learning tools.

NGCBot is a WeChat bot based on the HOOK mechanism, supporting scheduled push of security news from FreeBuf, Xianzhi, Anquanke, and Qianxin Attack and Defense Community, KFC copywriting, filing query, phone number attribution query, WHOIS information query, constellation query, weather query, fishing calendar, Weibei threat intelligence query, beautiful videos, beautiful pictures, and help menu. It supports point functions, automatic pulling of people, ad detection, automatic mass sending, Ai replies, rich customization, and easy for beginners to use. The project is open-source and periodically maintained, with additional features such as Ai (Gpt, Xinghuo, Qianfan), keyword invitation to groups, automatic mass sending, and group welcome messages.

Airgorah is a WiFi security auditing software written in Rust that utilizes the aircrack-ng tools suite. It allows users to capture WiFi traffic, discover connected clients, perform deauthentication attacks, capture handshakes, and crack access point passwords. The software is designed for testing and discovering flaws in networks owned by the user, and requires root privileges to run on Linux systems with a wireless network card supporting monitor mode and packet injection. Airgorah is not responsible for any illegal activities conducted with the software.

Agentic Security is an open-source vulnerability scanner designed for safety scanning, offering customizable rule sets and agent-based attacks. It provides comprehensive fuzzing for any LLMs, LLM API integration, and stress testing with a wide range of fuzzing and attack techniques. The tool is not a foolproof solution but aims to enhance security measures against potential threats. It offers installation via pip and supports quick start commands for easy setup. Users can utilize the tool for LLM integration, adding custom datasets, running CI checks, extending dataset collections, and dynamic datasets with mutations. The tool also includes a probe endpoint for integration testing. The roadmap includes expanding dataset variety, introducing new attack vectors, developing an attacker LLM, and integrating OWASP Top 10 classification.

Pwnagotchi is an AI tool leveraging bettercap to learn from WiFi environments and maximize crackable WPA key material. It uses LSTM with MLP feature extractor for A2C agent, learning over epochs to improve performance in various WiFi environments. Units can cooperate using a custom parasite protocol. Visit https://www.pwnagotchi.ai for documentation and community links.

DAILA is a unified interface for AI systems in decompilers, supporting various decompilers and AI systems. It allows users to utilize local and remote LLMs, like ChatGPT and Claude, and local models such as VarBERT. DAILA can be used as a decompiler plugin with GUI or as a scripting library. It also provides a Docker container for offline installations and supports tasks like summarizing functions and renaming variables in decompilation.

hackingBuddyGPT is a framework for testing LLM-based agents for security testing. It aims to create common ground truth by creating common security testbeds and benchmarks, evaluating multiple LLMs and techniques against those, and publishing prototypes and findings as open-source/open-access reports. The initial focus is on evaluating the efficiency of LLMs for Linux privilege escalation attacks, but the framework is being expanded to evaluate the use of LLMs for web penetration-testing and web API testing. hackingBuddyGPT is released as open-source to level the playing field for blue teams against APTs that have access to more sophisticated resources.