Search Results for "pentest operating system"

OpenFang is an open-source agent operating system designed to orchestrate autonomous AI agents and workflows in a structured, production-oriented environment. Written primarily in Rust, the project focuses on building a high-performance runtime where multiple specialized agents can collaborate to complete complex computational or development tasks. It aims to move beyond simple chat-based agents by providing infrastructure for persistent agent memory, task coordination, and scalable execution. ...

...OpenShell also features a privacy-aware routing system that prevents sensitive information from leaving the sandbox environment. By combining container isolation, policy enforcement, and agent orchestration, OpenShell offers a secure infrastructure for developing and operating AI agents.

Cake is a compact, powerful toolkit that combines a flexible TCP/UDP proxy, port forwarding system, and connection manager designed for both development and penetration testing scenarios. It enables users to create complex networking flows where traffic can be proxied, relayed, and manipulated between endpoints — useful for debugging networked applications, inspecting protocols, or tunneling traffic through different hops. The tool is designed to work with multiple protocols and supports...

nono is an open-source, kernel-enforced capability shell designed to safely run AI agents and other untrusted processes under strict operating system controls. The project addresses a growing security concern: modern coding agents typically execute with full user permissions, which means they can potentially read sensitive files, modify system configurations, or exfiltrate credentials if compromised. nono solves this by applying default-deny sandboxing at the kernel level using technologies such as Landlock on Linux and Seatbelt on macOS, making unauthorized actions structurally impossible rather than merely discouraged. ...

...The software allows users to run advanced music generation systems directly on their own devices without requiring heavy dependencies such as Python or full machine learning frameworks. Instead, it provides a lightweight environment capable of executing music generation models locally on CPUs or GPUs while maintaining strong performance across operating systems including Windows, macOS, and Linux. Users can describe a musical style, mood, or instrumentation using text prompts, and the system produces original audio samples based on those instructions. The application currently integrates with models such as MusicGen and is designed to support additional models transparently in the future. ...