TI-Mindmap-GPT

TI MINDMAP GPT is an AI-powered tool designed to assist cyber threat intelligence teams in quickly synthesizing and visualizing key information from various Threat Intelligence sources. The tool utilizes Large Language Models (LLMs) to transform lengthy content into concise, actionable summaries, going beyond mere text reduction to provide insightful encapsulations of crucial points and themes. Users can leverage their own LLM keys for personalized and efficient information processing, streamlining data analysis and enabling teams to focus on strategic decision-making.

DevOpsGPT is an AI-driven software development automation solution that combines Large Language Models (LLM) with DevOps tools to convert natural language requirements into working software. It improves development efficiency by eliminating the need for tedious requirement documentation, shortens development cycles, reduces communication costs, and ensures high-quality deliverables. The Enterprise Edition offers features like existing project analysis, professional model selection, and support for more DevOps platforms. The tool automates requirement development, generates interface documentation, provides pseudocode based on existing projects, facilitates code refinement, enables continuous integration, and supports software version release. Users can run DevOpsGPT with source code or Docker, and the tool comes with limitations in precise documentation generation and understanding existing project code. The product roadmap includes accurate requirement decomposition, rapid import of development requirements, and integration of more software engineering and professional tools for efficient software development tasks under AI planning and execution.

EXO Private Search is a privacy-preserving search system based on MIT's Tiptoe paper. It allows users to search through data while maintaining query privacy, ensuring that the server never learns what is being searched for. The system converts documents into embeddings, clusters them for efficient searching, and uses SimplePIR for private information retrieval. It employs sentence transformers for embedding generation, K-means clustering for search optimization, and ensures that all sensitive computations happen client-side. The system provides significant performance improvements by reducing the number of PIR operations needed, enabling efficient searching in large document collections, and maintaining privacy while delivering fast results.

Awesome Generative AI is a curated list of modern Generative Artificial Intelligence projects and services. Generative AI technology creates original content like images, sounds, and texts using machine learning algorithms trained on large data sets. It can produce unique and realistic outputs such as photorealistic images, digital art, music, and writing. The repo covers a wide range of applications in art, entertainment, marketing, academia, and computer science.

This repository is a comprehensive collection of cybersecurity-related references, scripts, tools, code, and other resources. It is carefully curated and maintained by Omar Santos. The repository serves as a supplemental material provider to several books, video courses, and live training created by Omar Santos. It encompasses over 10,000 references that are instrumental for both offensive and defensive security professionals in honing their skills.

This repository serves as a hub for Microsoft Official Build & Modernize AI Applications reference solutions and content. It provides access to projects demonstrating how to build Generative AI applications using Azure services like Azure OpenAI, Azure Container Apps, Azure Kubernetes, and Azure Cosmos DB. The solutions include Vector Search & AI Assistant, Real-Time Payment and Transaction Processing, and Medical Claims Processing. Additionally, there are workshops like the Intelligent App Workshop for Microsoft Copilot Stack, focusing on infusing intelligence into traditional software systems using foundation models and design thinking.

The Conversational Azure OpenAI Accelerator is a tool designed to provide rapid, no-cost custom demos tailored to customer use cases, from internal HR/IT to external contact centers. It focuses on top use cases of GenAI conversation and summarization, plus live backend data integration. The tool automates conversations across voice and text channels, providing a valuable way to save money and improve customer and employee experience. By combining Azure OpenAI + Cognitive Search, users can efficiently deploy a ChatGPT experience using web pages, knowledge base articles, and data sources. The tool enables simultaneous deployment of conversational content to chatbots, IVR, voice assistants, and more in one click, eliminating the need for in-depth IT involvement. It leverages Microsoft's advanced AI technologies, resulting in a conversational experience that can converse in human-like dialogue, respond intelligently, and capture content for omni-channel unified analytics.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

Naas (Notebooks as a service) is an open source platform that enables users to create powerful data engines combining automation, analytics, and AI from Jupyter notebooks. It offers features like templates for automated data jobs and reports, drivers for data connectivity, and production-ready environment with scheduling and notifications. Naas aims to provide an alternative to Google Colab with enhanced low-code layers.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

This repository provides a hands-on workshop for developers and solution builders to build a real-life serverless LLM application using foundation models (FMs) through Amazon Bedrock and advanced design patterns such as Reason and Act (ReAct) Agent, text-to-SQL, and Retrieval Augmented Generation (RAG). It guides users through labs to explore common and advanced LLM application design patterns, helping them build a complex Agentic LLM assistant capable of answering retrieval and analytical questions on internal knowledge bases. The repository includes labs on IaC with AWS CDK, building serverless LLM assistants with AWS Lambda and Amazon Bedrock, refactoring LLM assistants into custom agents, extending agents with semantic retrieval, and querying SQL databases. Users need to set up AWS Cloud9, configure model access on Amazon Bedrock, and use Amazon SageMaker Studio environment to run data-pipelines notebooks.

DBRX is a large language model trained by Databricks and made available under an open license. It is a Mixture-of-Experts (MoE) model with 132B total parameters and 36B live parameters, using 16 experts, of which 4 are active during training or inference. DBRX was pre-trained for 12T tokens of text and has a context length of 32K tokens. The model is available in two versions: a base model and an Instruct model, which is finetuned for instruction following. DBRX can be used for a variety of tasks, including text generation, question answering, summarization, and translation.

Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It supports the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks. Dioptra provides a REST API and can be controlled via a web interface or Python client for designing, managing, executing, and tracking experiments. It aims to be reproducible, traceable, extensible, interoperable, modular, secure, interactive, shareable, and reusable.

The AI Tutor RAG System repository contains Jupyter notebooks supporting the RAG course, focusing on enhancing AI models with retrieval-based methods. It covers foundational and advanced concepts in retrieval-augmented generation, including data retrieval techniques, model integration with retrieval systems, and practical applications of RAG in real-world scenarios.

The Vector Cookbook is a collection of recipes and sample application starter kits for building AI applications with LLMs using PostgreSQL and Timescale Vector. Timescale Vector enhances PostgreSQL for AI applications by enabling the storage of vector, relational, and time-series data with faster search, higher recall, and more efficient time-based filtering. The repository includes resources, sample applications like TSV Time Machine, and guides for creating, storing, and querying OpenAI embeddings with PostgreSQL and pgvector. Users can learn about Timescale Vector, explore performance benchmarks, and access Python client libraries and tutorials.

The Microsoft Semantic Kernel Documentation GitHub repository contains technical product documentation for Semantic Kernel. It serves as the home of technical content for Microsoft products and services. Contributors can learn how to make contributions by following the Docs contributor guide. The project follows the Microsoft Open Source Code of Conduct.

TI MINDMAP GPT is an AI-powered tool designed to assist cyber threat intelligence teams in quickly synthesizing and visualizing key information from various Threat Intelligence sources. The tool utilizes Large Language Models (LLMs) to transform lengthy content into concise, actionable summaries, going beyond mere text reduction to provide insightful encapsulations of crucial points and themes. Users can leverage their own LLM keys for personalized and efficient information processing, streamlining data analysis and enabling teams to focus on strategic decision-making.

TI MINDMAP GPT is an AI-powered tool designed to assist cyber threat intelligence teams in quickly synthesizing and visualizing key information from various Threat Intelligence sources. The tool utilizes Large Language Models (LLMs) to transform lengthy content into concise, actionable summaries, going beyond mere text reduction to provide insightful encapsulations of crucial points and themes. Users can leverage their own LLM keys for personalized and efficient information processing, streamlining data analysis and enabling teams to focus on strategic decision-making.

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.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

Trapster Community is a low-interaction honeypot designed for internal networks or credential capture. It monitors and detects suspicious activities, providing deceptive security layer. Features include mimicking network services, asynchronous framework, easy configuration, expandable services, and HTTP honeypot engine with AI capabilities. Supported protocols include DNS, HTTP/HTTPS, FTP, LDAP, MSSQL, POSTGRES, RDP, SNMP, SSH, TELNET, VNC, and RSYNC. The tool generates various types of logs and offers HTTP engine with AI capabilities to emulate websites using YAML configuration. Contributions are welcome under AGPLv3+ license.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

Microsoft Copilot for Security is a generative AI-powered assistant for daily operations in security and IT that empowers teams to protect at the speed and scale of AI.

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.