{"slug": "nocturne-open-source-ai-for-smarter-security-assessments", "title": "NOCTURNE: Open-Source AI for Smarter Security Assessments", "summary": "A team of developers built NOCTURNE, an open-source, MIT-licensed penetration-testing orchestration platform that uses an LLM-based multi-agent architecture to coordinate authorized security assessments in a single operator-controlled workspace. The system uses LangGraph to run an orchestrator that delegates tasks to executor agents interacting with security tools, and LiteLLM to abstract over multiple model providers including self-hosted Gemma. The public demo runs in simulation mode with mock data and does not perform real tests or invoke live models, and the team describes commercial plans such as managed deployments and per-engagement pricing as an early direction without paying customers.", "body_md": "*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12).*\n\n**What if small cybersecurity teams could spend less time coordinating tools and more time understanding security risks?**\n\nPenetration testing often involves switching between terminals, scanners, browsers, notes, and reporting tools. For independent security professionals and small teams, managing this workflow can be time-consuming.\n\nWe built **NOCTURNE**, an open-source, AI-assisted penetration testing orchestration platform that brings the different stages of an authorized security assessment into one operator-controlled workspace.\n\nNOCTURNE uses an LLM-based multi-agent architecture to coordinate assessment tasks. Instead of manually managing every tool and collecting results separately, an operator can define the scope of an engagement, supervise AI-assisted workflows, review findings, and prepare structured reports.\n\nIts main capabilities include:\n\nOur target users are **freelance penetration testers, small cybersecurity consultancies, and internal security teams**.\n\nThese users may benefit from a tool that helps organize repetitive assessment steps, reduces context-switching, and consolidates reporting.\n\nWe see potential for a commercial offering through managed deployments, hosted subscriptions, or per-engagement pricing.\n\nRather than replacing experienced security professionals, the goal is to help them work more efficiently while retaining human oversight.\n\nThis is an early commercial direction, not a claim that we already have paying customers.\n\n[🚀 Explore NOCTURNE — Interactive Demo](https://nocturne-main.netlify.app/?demo=1)\n\nThe public demo allows visitors to explore NOCTURNE's operator interface directly in their browsers without installing dependencies or configuring API keys.\n\nIt includes sample sessions, agent activity, findings, and interactive dashboard components.\n\n**Important:** The public deployment runs in simulation mode using mock data. It does not perform real penetration tests, invoke live AI models, or connect to an operational security backend. The live-execution architecture is available in the source repository for authorized environments.\n\nThe operator dashboard brings assessment activity, navigation, and monitoring into a unified interface.\n\nSecurity findings can be organized and reviewed before being incorporated into reports.\n\nThe system also provides an interface for supervising browser-assisted assessment workflows.\n\n**GitHub Repository:** [https://github.com/NullClassifier/nocturne](https://github.com/NullClassifier/nocturne)\n\nNOCTURNE is publicly available under the MIT License.\n\nThe repository includes the application source code, development infrastructure, configuration examples, technical documentation, and instructions for local deployment.\n\nDevelopers can inspect its architecture, experiment with supported model configurations, and contribute improvements.\n\nNOCTURNE combines open-source AI orchestration frameworks, a modular application architecture, and security testing infrastructure.\n\nAt the center of the platform is **LangGraph**, which coordinates the agent workflow.\n\nAn orchestrator interprets the assessment objectives and delegates work to executor agents. Those agents can interact with available security tools, collect results, and update the engagement state.\n\nThe architecture is designed to keep the operator involved throughout the workflow, including the ability to steer or stop a run.\n\nOne of our main goals was to avoid locking the platform into a single proprietary AI provider.\n\nWe integrated **LiteLLM** to provide a configurable abstraction over different language-model endpoints.\n\nNOCTURNE supports Gemma through compatible providers, including Google AI Studio, OpenRouter, and custom model-serving endpoints.\n\nOur repository documents the use of a self-hosted Gemma model during development. This configuration allows model inference to run on infrastructure controlled by the operator rather than requiring a single external API.\n\nSupporting different deployment options also gives users more flexibility over model costs, infrastructure, and data handling.\n\nThe backend is built with **Python and FastAPI**.\n\nWe separated the API from the agent execution process:\n\nThe engine also includes checkpointing capabilities so interrupted workflows can resume.\n\nThe interface is built with **React, TypeScript, and Vite**.\n\nWe wanted the experience to feel like a security operations workspace rather than a simple chatbot.\n\nThe interface combines live activity displays, agent visualization, findings management, terminal components, and report access.\n\nFor the public demonstration, we use the application's mock API implementation. This provides an interactive frontend without exposing real security testing infrastructure to the Internet.\n\nNOCTURNE builds upon an earlier prototype, so not every component of the repository was developed during October 12–18.\n\nDuring the challenge period, our documented improvements included:\n\nThese changes build on the earlier architecture and move NOCTURNE toward a more flexible and accessible developer experience.\n\nThe public Git history documents both the original foundation and the subsequent improvements.\n\nFor NOCTURNE, open innovation is not simply about publishing source code. It directly influences how the platform can be deployed, adapted, and maintained.\n\n**1. Freedom to choose models**\n\nDifferent teams have different requirements for model quality, privacy, deployment, and cost.\n\nUsing open-weight models and an open-source model-integration layer makes it possible to experiment with alternatives instead of depending entirely on one proprietary provider.\n\n**2. Greater control over sensitive information**\n\nSecurity assessments can involve confidential technical information.\n\nA self-hosted model deployment can provide greater control over where inference happens and how assessment information is processed, although secure deployment still depends on the surrounding infrastructure and configuration.\n\n**3. A more flexible cost structure**\n\nUsage-based API pricing can make recurring AI workloads harder to predict.\n\nRunning an open-weight model on infrastructure a team controls creates another option. It avoids per-token API fees, although hardware, hosting, and maintenance still have real costs.\n\nNOCTURNE's model usage tracking also helps operators understand the cost of supported hosted inference.\n\n**4. Transparency and customization**\n\nSecurity workflows vary between organizations.\n\nWith an open-source architecture, developers can inspect agent orchestration, modify workflows, integrate additional tools, and improve the project's behavior.\n\nThis flexibility is especially valuable for specialized professional workflows where a generic AI assistant may not be sufficient.\n\nBuilding an AI-assisted security platform involves more than connecting an LLM to tools.\n\nWe had to consider the boundaries between agent planning, execution, application state, and operator interaction.\n\nModel compatibility is another challenge. Different providers may handle tool calls and structured responses differently, so reliable operation requires careful testing.\n\nWe also learned that a compelling interface is not a substitute for verified functionality. This is why our public demo is explicitly labeled as simulated, while the repository documents the separate live-execution architecture.\n\nBefore any commercial deployment, additional work on reliability, security hardening, access controls, and real-world validation would be essential.\n\nWe are entering the **Best Use of Gemma** category.\n\nGemma is an open-weight model supported by NOCTURNE's agent architecture. The project documents self-hosted Gemma usage and includes provider integrations designed to make compatible Gemma deployments easier to configure.\n\nThe motivation behind this integration is to make AI-assisted security workflows less dependent on proprietary models and fixed cloud providers.\n\nOur interactive public demo is simulated; Gemma integration and model execution are documented in the source repository.\n\nNOCTURNE was developed collaboratively by the **Netrunners team**.\n\n**Ismayil Yusifli**\n\n**Huseyn Pashayev**\n\nWe collaborated on project development, integration, and documentation, with some code contributions shared outside GitHub before being committed to the repository.\n\nOur next steps are to improve compatibility across supported models, strengthen reliability, expand testing in authorized lab environments, and gather feedback from cybersecurity professionals.\n\nWe also want to explore whether a managed version could become a practical service for smaller security teams.\n\n**Our goal is to make AI-assisted security assessment workflows more accessible, adaptable, and transparent through open-source technology.**\n\n*NOCTURNE is intended exclusively for educational research and authorized security assessments. Security testing must only be performed on systems owned by the operator or covered by explicit permission and an agreed testing scope.*", "url": "https://wpnews.pro/news/nocturne-open-source-ai-for-smarter-security-assessments", "canonical_source": "https://dev.to/ismail06/nocturne-open-source-ai-for-smarter-security-assessments-3jab", "published_at": "2026-10-18 18:42:57+00:00", "updated_at": "2026-10-18 18:48:50.900234+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "artificial-intelligence", "large-language-models", "developer-tools"], "entities": ["NOCTURNE", "LangGraph", "LiteLLM", "Gemma", "Google AI Studio", "OpenRouter", "GitHub", "NullClassifier"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/nocturne-open-source-ai-for-smarter-security-assessments", "markdown": "https://wpnews.pro/news/nocturne-open-source-ai-for-smarter-security-assessments.md", "text": "https://wpnews.pro/news/nocturne-open-source-ai-for-smarter-security-assessments.txt", "jsonld": "https://wpnews.pro/news/nocturne-open-source-ai-for-smarter-security-assessments.jsonld"}}