Project Moonshot

Overview

Project Moonshot is an open-source toolkit for benchmarking and red-teaming large language models and applications. It best suits AI developers and compliance teams who want to shape their own evaluations and can manage a local software setup. Its broad testing and reporting options are useful, but beta status and installation requirements make it a less straightforward choice for teams seeking a turnkey service.

Key features

Benchmarks and red teaming

Moonshot runs tests against open-source datasets and metrics covering performance as well as trust and safety risks. It can generate adversarial prompts using algorithms or generative LLMs, with supported tests for prompt injection, jailbreaks, data leakage, and unsafe outputs. That combination gives teams a way to assess model performance and probe several security risks in the same toolkit; it does not remove the need to decide which tests are relevant to their applications.

Custom evaluations and connectors

Teams can build evaluation recipes from their own datasets, with optional prompt templates, metrics, and grading scales. The flexibility is valuable when standard benchmarks do not reflect a team's use case, though it leaves the design of a useful evaluation to the user. Moonshot connects to OpenAI, Anthropic, Together, and Hugging Face with an API key, and users can create connectors for other models or their own applications on custom servers.

Interfaces and results

Users can work through a web interface, interactive CLI, library APIs, or Web APIs. Interactive HTML reports support review, while downloadable raw JSON results suit programmatic analysis. The project implements benchmarks recommended in IMDA's Starter Kit for safety testing LLM-based applications, which may be relevant to teams working with that framework.

Pricing

Project Moonshot: free. The open-source toolkit has no paid tier described here, so it is a fit for teams that can supply the setup and model connections themselves. The free plan requires Python 3.11; the Web UI also requires Node.js 20.11.1 LTS or later, and test assets are needed to run tests. Moonshot is released under Apache License 2.0. No free trial is offered.

Platforms

Moonshot supports API, Linux, macOS, web, Windows, and self-hosted use. Installation uses pip, with the Web UI running locally at localhost:3000. The installation guide recommends Chrome for the best Web UI experience; x86 Macs may encounter difficulties with moonshot-data dependencies. This local setup offers control over deployment but asks more of users than a hosted product. The repository identifies the software as beta, so teams should account for that maturity when considering it for important evaluations.

Who it's for

Moonshot is aimed at AI developers and compliance teams evaluating LLMs and LLM-based applications. It is a strong fit for teams that need custom test recipes, multiple ways to access the toolkit, or machine-readable results and can handle Python-based installation. Teams wanting a managed, low-setup workflow may prefer another option. For support, users can raise an issue on GitHub.

Pros and cons

• Pros: Combines benchmark testing and adversarial probing, including prompt injection, jailbreak, data leakage, and unsafe-output tests.
• Pros: Custom datasets, prompt templates, metrics, and grading scales let teams tailor evaluations to their applications.
• Pros: HTML reports and raw JSON serve both interactive review and programmatic analysis.
• Cons: Python and Web UI prerequisites, plus required test assets, add setup work before testing can begin.
• Cons: Beta status and possible x86 Mac dependency issues may complicate adoption for teams that need a mature, predictable installation.

Alternatives

Browse AI Security Testing Tools for more options. AIRTA Red Team is another free open-source choice; its plan requires users to bring an LLM API key. OpenSecureAI Scanner is worth considering if a paid Pro plan's 50,000 API requests per month, hosted AI remediation, and LLM Gateway completions fit the need; it costs $49 a month.

Promptfoo may suit teams seeking a free Community plan with 10k red-team probes per month and local or self-hosted operation. PromptGuard offers a free plan capped at 20,000 scans a month, one API key, one project, and 24-hour log retention, with a Team plan at $19 a month. PyRIT is another free open-source framework, requiring a Python environment and configured AI endpoints.

Augustus is a free Apache 2.0 licensed open-source option; API usage may require provider credentials. Basilisk is a free AGPL-3.0 option distributed as CLI, desktop, Docker, or source software for authorized use. AgentScan offers a free web/API plan with five scans per month, 185 attack vectors, and JSON results, making it a fit for users whose needs fit that scan cap.

Verdict

Choose Project Moonshot if your team needs flexible LLM benchmarking and red teaming, custom evaluations, and report data it can analyze programmatically, and has the capacity to run the local stack. Its breadth and open-source license are compelling; beta maturity and setup demands are the main reasons to look elsewhere.