Nexterm is a comprehensive remote server management platform enabling connections via SSH, VNC, RDP, web, SFTP, and FTP protocols. It features a multi-layer architecture with web (React), mobile (Flutter), desktop (Tauri), and CLI (Rust) clients communicating with a Node.js/Express server and C-based connection engine. The platform provides session management, organization-based access control, audit logging, script execution, file management, monitoring, and AI-assisted command generation. Real-time updates are facilitated through WebSocket connections, with data persistence handled via Sequelize ORM supporting PostgreSQL, MySQL, and SQLite databases.
Iron Proxy is a programmable egress proxy designed for securing and monitoring outbound traffic from untrusted workloads. It functions as a Man-in-the-Middle (MITM) proxy with built-in DNS interception, capable of inspecting and transforming HTTP/HTTPS, TCP tunnels, and PostgreSQL traffic. The system features a pluggable transform pipeline for access control, secret injection, authentication (AWS/GCP/OAuth), LLM-based policy enforcement, and Model Context Protocol (MCP) filtering. Architecture includes modular components for proxy handling, DNS server, transform pipeline, configuration management, and optional control plane synchronization. Deployment options range from standalone mode to managed mode via control plane, with support for Docker, Helm, and nftables enforcement. Key capabilities include structured JSON audit logging, OpenTelemetry integration, TLS MITM with dynamic certificate generation, and secret resolution from multiple backends (environment, AWS, 1Password).
This repository is Microsoft's Agent Governance Toolkit, a production-grade multi-language framework (Python, TypeScript, Rust, Go, .NET) for enforcing policy, identity, trust, audit, and sandboxing on autonomous AI agents. In production, it intercepts agent tool calls, messages, and delegations before they reach external services, evaluating them against declarative policies (OPA, Cedar, or custom backends) with deterministic outcomes (allow/deny/escalate/transform). The system provides a full governance plane including a Rust-based policy engine (Agent Control Specification), a service mesh (AgentMesh) with API gateway, trust engine, policy server, and audit collector, a hypervisor for execution rings and sandboxing (Docker, Hyperlight, Nono), compliance verification (OWASP, EU AI Act), SLO monitoring, and a plugin marketplace. Integrations exist for LangChain, CrewAI, AutoGen, OpenAI Agents, Semantic Kernel, MCP, and many other frameworks. The toolkit is deployed as CLI tools (`agt`, `agentmesh`, `agentos`), Docker containers with sidecar patterns, Kubernetes charts, and VS Code/Chrome extensions. The architecture is polyglot with a shared policy engine core written in Rust; the Python SDK is the richest, with the .NET and Go SDKs covering governance middleware and lifecycle management. The codebase is heavily tested with 992 conformance tests across 10 formal specs, fuzzing targets, and CI gates enforcing deterministic policy outcomes, fail-closed behavior, and no-stub registration.
Apollo is a distributed configuration management system designed for microservices architectures. It provides a centralized web portal (AngularJS frontend with Spring Boot backend) for managing application configurations across multiple environments (DEV, FAT, UAT, PRO, etc.) and clusters. The system supports real-time configuration updates via long-polling, version management, grayscale (canary) releases, role-based access control, and comprehensive audit logging. It exposes REST APIs for programmatic management through an OpenAPI v1 layer and provides client SDKs for various languages. The architecture consists of three main services: apollo-portal (management UI and OpenAPI endpoints), apollo-adminservice (admin CRUD operations), and apollo-configservice (client-facing configuration delivery with long-polling notifications). These services share a common business logic module (apollo-biz) and persist data in MySQL databases (ApolloConfigDB and ApolloPortalDB). The portal communicates with admin services via REST, while clients poll the config service for updates. The system also includes a service registry module for discovery of admin service instances, supporting multiple discovery mechanisms (Eureka, Consul, Nacos, Zookeeper, database, Kubernetes). Key entry points include the portal web UI, OpenAPI v1 endpoints (/openapi/v1/), admin service REST APIs, and config service APIs for client configuration retrieval and notification. The primary data flow involves creating/modifying configurations through the portal or OpenAPI, which are stored in the database. Changes trigger release messages that are scanned by the config service to notify connected clients. Clients then fetch the latest configurations via the config service. The system also supports gray releases, branching, rollback, and configuration import/export. External dependencies include MySQL (primary database), optional service discovery mechanisms (Eureka, Consul, Nacos, Zookeeper, Kubernetes), LDAP/OIDC for authentication, and email/MQ services for notifications. The codebase is modular with clear separation of concerns, extensive test coverage, and a well-defined SPI (Service Provider Interface) pattern for extensibility.
This is a document redaction and anonymization platform designed for processing unstructured data (documents, images, PDFs) and structured data (CSV, databases) to detect and redact sensitive information like PII, PHI, and financial data. The system features a React/TypeScript frontend with a Python/FastAPI backend, utilizing AI models for NER (HaS Text), OCR (PaddleOCR-VL), and visual detection (LocateAnything). It provides both single-file processing via a playground interface and batch processing workflows with review/approval steps. The platform includes role-based access control, audit logging, configurable presets for different industries, and structured data handling capabilities. Deployment is containerized with Docker and supports GPU acceleration for ML models.
ModSecurity v3 (libmodsecurity) is a C++17 Web Application Firewall (WAF) rule engine that parses SecLang configuration and rules, then evaluates them against HTTP transaction data. It is a library rather than a standalone server: connectors such as nginx or IIS feed requests through Transaction phase methods (processConnection, processURI, processRequestHeaders, processRequestBody, processResponseHeaders, processResponseBody, processLogging). The engine resolves ModSecurity variables (ARGS, REQUEST_HEADERS, FILES, TX, etc.), applies transformation pipelines (base64Decode, urlDecode, normalisePath, removeComments, htmlEntityDecode), evaluates operators (rx, pm, ipMatch, detectSQLi/detectXSS via libinjection, verifyCC, geoLookup), and executes actions (deny, allow, redirect, setvar, ctl:*). It supports multipart/JSON/XML/urlencoded request body parsing, audit logging through serial/parallel/HTTPS writers, and persistent collections on LMDB or in-memory backends. The code is organized around plugin-style base classes and factory instantiation for actions, operators, transformations, and variables, with a Bison/Flex generated parser for the SecLang grammar. Although the repository contains a rich JSON-based regression-test corpus, its character is overwhelmingly an application/library, not a data-research project. Architecture is modular but carries legacy baggage: autotools build system, extensive #ifdef feature gates (CURL, LMDB, Lua, libxml2, GeoIP/MaxMind, ssdeep, PCRE/PCRE2), manual memory management, and some stubbed operators. Duplicated CI workflows and long-standing TODOs indicate medium technical drift, but the project remains actively maintained and is a solid, widely deployed security library with several genuinely reusable low-level components.