Guardana

Customizable security gates for AI systems

Open Source

Developer Tools

Artificial Intelligence

GitHub

Visit WebsiteSee on Product HuntTwitter ⧉Github ⧉

Hey Product Hunt 👋 Guardana actually started as something we needed for our own work. While building and running our own AI models and AI-powered systems across different projects, I kept looking for a security framework that we could adapt to the way we actually build. I didn’t want just another fixed scanner or a large collection of jailbreak prompts. I wanted something we could use as a security framework around the whole development lifecycle: — a solid set of security checks out of the box — the ability to add our own rules for project-specific risks — custom evaluators when generic grading simply isn’t enough — support for different models, agents and infrastructure — something we could run locally during development — put into pytest and CI/CD as an actual security gate — and keep using against deployed systems afterwards Most importantly, we needed to be able to adapt the security logic to the actual application. Because a healthcare agent, an internal company assistant, a coding agent and an AI system with access to production tools do not have the same threat model. That became Guardana. Today Guardana is an Apache-2.0 open-source AI security verification framework covering multiple layers of an AI system: 🛡️ Build-time security Scan model files, repositories, dependencies, prompts, templates and supply-chain risks. 🎯 Runtime verification Probe live models and agents for prompt injection, jailbreaks, unsafe behavior and tool-related vulnerabilities. 🔌 MCP security Verify MCP servers and parts of their authorization and tool surface. 🔎 Execution trace analysis Analyze what an AI system actually did — model calls, tools, retrieval, identities and scopes, approvals, memory and side effects. ↔️ Security regression testing Compare releases and detect when a change to a model, prompt, tool or deployment makes the system less secure. 🧩 Extensibility Create your own rules, evaluators, targets and private security packs without having to fork the project. And the same engine can be used from the CLI, pytest, CI/CD or scheduled health checks. One design principle has remained important from the beginning: “Couldn’t verify” must never silently mean “secure.” If an evaluator cannot determine the outcome, a capability is missing, the target is unavailable or the test cannot execute correctly, Guardana keeps that uncertainty visible instead of quietly producing a green check. We built Guardana because we wanted this flexibility for our own AI projects. Now we’re opening it up because I suspect many other teams are running into the same problem: generic security checks are useful, but sooner or later every serious AI system needs security rules that understand its own architecture, permissions, tools and business logic. Guardana is: — Apache-2.0 — no account required — no mandatory telemetry — no mandatory cloud — designed for private and self-hosted environments — extensible with your own security logic We’ve already shipped 14 small releases and we’re still moving quickly. I’d especially love feedback from people building real AI systems: What project-specific security rule or evaluator would you need before you could use something like Guardana as a real deployment gate? And if you’re running self-hosted models, agents, MCP servers or your own AI infrastructure, I’d love to hear where Guardana still falls short.