Search Results for "linux secure"
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Showing 127 open source projects for "linux secure"
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Secure OpenClaw
A personal 24x7 AI assistant like OpenClaw
Secure-OpenClaw is an open-source project designed to transform your everyday messaging apps like WhatsApp, Telegram, Signal, and iMessage into a 24×7 personal AI assistant that can respond to queries, run tasks, remember context, and interact with external services. Unlike a traditional chatbot that lives in a browser or API dashboard, Secure-OpenClaw runs locally or on your chosen infrastructure and listens for messages on channels you already use, giving you an always-on interface to a... -
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NemoClaw
NVIDIA plugin for secure installation of OpenClaw
NVIDIA NemoClaw is an open-source tool designed to simplify the deployment and management of always-on AI assistants using the OpenClaw ecosystem. It installs and configures the NVIDIA OpenShell runtime, which provides a secure environment for running autonomous AI agents. NemoClaw enables users to launch sandboxed agent environments that control network access, file permissions, and inference requests through policy-based security. The platform integrates with AI models such as NVIDIA... -
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PentestGPT
Automated Penetration Testing Agentic Framework Powered by LLMs
PentestGPT is an AI-powered autonomous penetration testing agent designed to perform intelligent, end-to-end security assessments using large language models. Published at USENIX Security 2024, it combines advanced reasoning with an agentic workflow to automate tasks traditionally handled by human pentesters. The platform supports multiple penetration testing categories, including web security, cryptography, reversing, forensics, privilege escalation, and binary exploitation. PentestGPT runs... -
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Constellation
Constellation is the first Confidential Kubernetes
Constellation is a distributed confidential computing platform developed by Edgeless Systems. It allows developers to run Kubernetes-native applications in secure enclaves across multiple machines, ensuring end-to-end encryption and trusted execution for workloads. Built on top of Kubernetes and using technologies like Intel SGX and Gramine, Constellation guarantees that not even infrastructure operators can access data or code, making it ideal for privacy-sensitive workloads and multi-party... -
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MCP BigQuery Server
A Model Context Protocol (MCP) server that provides secure
This MCP server provides secure, read-only access to BigQuery datasets, enabling large language models (LLMs) to safely query and analyze data through a standardized interface. -
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Substra
Low-level Python library used to interact with a Substra network
An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations. -
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Open WebUI
User-friendly AI Interface
Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform designed to operate entirely offline. It supports various LLM runners like Ollama and OpenAI-compatible APIs, with a built-in inference engine for Retrieval Augmented Generation (RAG), making it a powerful AI deployment solution. Key features include effortless setup via Docker or Kubernetes, seamless integration with OpenAI-compatible APIs, granular permissions and user groups for enhanced security,... -
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E2B
Secure open source cloud runtime for AI apps & AI agents
E2B's Code Interpreter SDK allows you to add code-interpreting capabilities to your AI apps. E2B Sandbox is a secure sandboxed cloud environment made for AI agents and AI apps. Sandboxes allow AI agents and apps to have long-running cloud secure environments. In these environments, large language models can use the same tools as humans do. -
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CubeSandbox
Instant, Concurrent, Secure & Lightweight Sandbox for AI Agents
CubeSandbox is a lightweight, high-performance sandboxing environment designed to securely run AI agents and untrusted code in isolated containers. Built in Rust, it focuses on enabling concurrent execution while maintaining strong isolation and low overhead. The system is optimized for scenarios where multiple AI agents need to execute tasks in parallel without compromising system integrity. It provides fast startup times and efficient resource management, making it suitable for large-scale... -
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DBHub
Universal database MCP server connecting to MySQL, PostgreSQL
DBHub is a universal database gateway that implements the MCP server interface so assistants and IDEs can explore and query databases through typed tools. It supports multiple transports—stdio for desktop clients and HTTP for networked scenarios—making it flexible to embed or deploy. Configuration is environment-variable driven, with a DSN and per-engine settings covering Postgres, MySQL, MariaDB, SQL Server, and SQLite. Operational flags include read-only mode, row limits, and even SSH... -
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Sec-Context
AI Code Security Anti-Patterns distilled from 150+ sources
Sec-Context is a curated security research project that distills common code anti-patterns and vulnerabilities that generative AI tends to produce, presenting them as a comprehensive set of examples and secure alternatives that can be used to train or guide AI assistants and reviewers toward safer code generation. It compiles insights from over 150 industry and academic sources into structured reference documents that outline real-world security problems such as hardcoded secrets, SQL... -
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The Pope Bot
Autonomous AI agent that you can configure and build
The Pope Bot is an autonomous AI agent framework that lets users configure and run an AI-powered agent that can perform tasks continuously, day in and day out, by leveraging GitHub Actions, commit history, and secure workflows. It’s designed so that every action taken by the agent is logged as a git commit, giving users complete visibility into what the agent did, why it did it, and when, which makes actions auditable and reversible. The framework treats the repository itself as the agent’s... -
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Octelium
A next-gen FOSS self-hosted unified zero trust secure access platform
Octelium is an open source, self-hosted unified secure-access platform built for modern infrastructure and hybrid environments. It positions itself as more than a typical VPN; it supports zero-trust network access (ZTNA), “BeyondCorp”-style access, API/AI gateway functionality, and even serves as a PaaS-like deployment surface. One of its key strengths is identity-based, application-layer (L7) aware control, meaning access decisions are made per request, with context and policy rather than... -
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Portkey AI Gateway
A blazing fast AI Gateway with integrated guardrails
Portkey AI Gateway aims to offer a blazing fast, secure, and flexible gateway for interacting with a wide variety of models and enforcing guardrails. It presents a single, friendly API through which you can route to 200+ LLMs, while applying configurable input/output guardrails to enforce policies or restrict certain content. It supports automatic retries, fallbacks, load balancing across providers or keys, and request timeouts to avoid latency spikes. The gateway is multimodal: it can... -
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Telegram MCP
MCP server to work with Telegram through MTProto
An MCP server that bridges the Telegram API and AI assistants, enabling seamless interaction between AI applications and Telegram through MTProto. -
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Obscura
The headless browser for AI agents and web scraping
Obscura is a security-focused project aimed at providing tools and techniques for enhancing privacy, anonymity, and operational security in digital environments. It is designed for users who need to obscure their digital footprint and reduce traceability across systems. The project typically includes utilities for masking identity, managing secure communication, and mitigating surveillance risks. It emphasizes practical implementations of privacy-preserving workflows rather than purely... -
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MCP Atlassian
MCP server that integrates Confluence and Jira
The MCP Atlassian server integrates Atlassian products like Confluence and Jira with the Model Context Protocol. It supports both Cloud and Server/Data Center deployments, enabling AI models to interact with these platforms securely. -
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E2B Desktop Sandbox
E2B Desktop Sandbox for LLMs. E2B Sandbox
E2B Desktop is an open-source sandboxed virtual desktop environment designed to enable secure “computer use” by large language models and automated agents. The platform provides isolated virtual machines where applications can be executed safely without affecting the host system. Each sandbox runs independently and can be configured with custom dependencies or tools required by an AI agent or automation workflow. The system allows developers to programmatically create and control these... -
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GaiaNet
Install and run your own AI agent service
Gaia is building an active, intelligent ecosystem that supports applications that learn, improve and grow over time. Put your knowledge to work and watch it evolve by creating a node on Gaia or by contributing to a domain supporting an existing knowledge base. Gaia’s decentralized platform ensures robust protection for user data and IP. Gaia allows secure ownership and monetization of IP without compromising privacy. Gaia’s living knowledge organisms continuously adapt and grow in real-time,... -
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E2B Cookbook
Examples of using E2B
E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code execution, and application generation inside isolated sandbox environments. E2B itself provides secure Linux-based sandboxes that enable AI systems to safely run generated code and interact with real computing resources without compromising the host environment. ... -
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Kubernetes MCP Server
Model Context Protocol (MCP) server for Kubernetes and OpenShift
A powerful and flexible Model Context Protocol (MCP) server implementation designed for seamless integration with Kubernetes and OpenShift environments, enabling enhanced interaction and management capabilities for AI assistants. -
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Agent Skills Tech Leads Club
The secure, validated skill registry for professional AI coding agents
Agent Skills is a managed registry of reusable instruction packages for professional AI coding agents. Skills bundle prompts, templates, references, and specialized workflows that can be installed into tools such as Claude Code, Cursor, Copilot, Codex, Cline, and others. The project emphasizes security through open-source content, static analysis, content hashing, lockfiles, path isolation, and audit logging. Its CLI can browse, install, update, remove, cache, and inspect skills at local or... -
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MCP Kibela
MCP server implementation that enables AI assistants
The MCP-Kibela server is a Model Context Protocol server implementation that enables AI assistants to search and reference content stored in Kibela. This integration allows AI models to securely access information within Kibela, enhancing their contextual understanding and response generation. -
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development... -
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nesa
Run AI models end-to-end encrypted
nesa is an open-source initiative focused on building decentralized AI infrastructure that enables secure, verifiable, and privacy-preserving machine learning and inference across distributed environments. The project aims to address key challenges in modern AI systems, such as data privacy, trust, and centralization, by leveraging cryptographic techniques and decentralized architectures. NESA allows developers to run AI computations in a way that ensures data integrity and confidentiality,...
