Project Moonshot

Project Moonshot is a free, open-source toolkit for assessing the safety and reliability of large language models and applications. It runs benchmark tests using open-source datasets and metrics, and generates adversarial prompts through algorithms or generative LLMs to probe for vulnerabilities and misuse. Users can build custom evaluations with their own datasets, optional prompt templates, metrics, and grading scales. Results include interactive HTML reports and downloadable raw JSON. Moonshot offers a Web UI, command-line interface, library APIs, and Web APIs; users can configure model connections or create connectors for custom models and hosted applications. It implements benchmarks recommended in IMDA’s Starter Kit for safety testing LLM-based applications. The project is beta software under Apache License 2.0. Installation uses pip and 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. The installation guide recommends Chrome for the Web UI and notes possible installation difficulties on x86 Macs with moonshot-data dependencies.

Project Moonshot offers several ways to run evaluations and inspect results, including custom tests and connectors. It is beta software with Python and, for the Web UI, Node.js requirements.

Moonshot is intended for AI developers and compliance teams evaluating LLMs and LLM-based applications. It suits users who need benchmark tests, red teaming, or custom evaluations.

Overview

Project Moonshot is a free, open-source toolkit for benchmarking and red-teaming large language models and applications. It suits AI developers and compliance teams who want to run their own evaluations; its breadth of testing and interfaces is balanced by beta status and local setup requirements.

Made by AI Verify Foundation, a not-for-profit subsidiary of Singapore’s Infocomm Media Development Authority, Moonshot implements benchmarks recommended in IMDA’s Starter Kit for safety testing LLM applications. Its Apache 2.0 license allows open-source use, but beta maturity makes it a less assured choice for teams seeking a finished, hosted service.

Key features

Benchmarks and adversarial tests

Moonshot evaluates models and applications against open-source datasets and metrics for performance and trust and safety risks. It can generate adversarial prompts algorithmically or with generative LLMs, and supports tests for prompt injection, jailbreaks, data leakage and unsafe outputs. This puts measurement and vulnerability probing in one toolkit, though teams still need test assets to run evaluations.

Custom evaluations and connectors

Users can build evaluation recipes from their own datasets, optional prompt templates, metrics and grading scales. Connections can be configured for OpenAI, Anthropic, Together and Hugging Face models using API keys; custom connectors also let teams connect other models or applications hosted on their own servers. That flexibility is useful when an evaluation needs to reflect a team’s data or deployment, but it places recipe and connector setup on the user.

Interfaces and results

The toolkit offers a Web UI, interactive command-line interface, library APIs and Web APIs. Interactive HTML reports support review, while raw JSON results can feed programmatic analysis.

Pricing

Project Moonshot costs free. The free plan is the open-source toolkit, with no paid tier or free-trial period indicated. It is a strong fit for teams able to operate the software themselves; the trade-off is that installation, test assets and provider credentials are part of the work rather than a packaged hosted evaluation service.

Platforms

Moonshot supports API, Linux, macOS, self-hosted, web and Windows environments. Installation uses pip and the maker’s instructions run the Web UI locally at localhost:3000. Python 3.11 is required, and the Web UI requires Node.js 20.11.1 LTS or later. The installation guide recommends Chrome for the best Web UI experience and warns that x86 Macs may have difficulty installing moonshot-data dependencies.

Who it's for

AI developers and compliance teams evaluating LLMs or LLM-based applications are the natural audience, particularly those that need custom datasets, grading scales or connectors to privately hosted models. It is less suitable for readers who want a turnkey hosted service or cannot accommodate its runtime requirements. Teams needing help can raise an issue on GitHub.

Pros and cons

Pros

• Combines benchmark evaluation with red teaming across several core safety risk areas.
• Custom recipes and connectors let teams evaluate their own data and model endpoints.
• HTML reports and raw JSON results serve both human review and programmatic analysis.
• Free and Apache 2.0 licensed, with multiple interfaces for local or integrated use.

Cons

• Beta status makes it less appropriate where teams need a mature, turnkey tool.
• Python, Node.js for the Web UI, and test assets add setup work before evaluations can run.
• x86 Mac users may encounter installation difficulty with moonshot-data dependencies.

Alternatives

AI Security Testing Tools is a useful place to compare options across the category.

• F5 BIG-IP APM may suit buyers looking for a paid product with a free trial and deployment across cloud, on-premises and hybrid environments.
• AgentScan is worth considering for a freemium scanner with a defined monthly scan allowance and attack-vector coverage.
• Crucible is another freemium option for Linux or self-hosted use.
• OpenSecureAI Scanner may fit teams that want a paid Pro tier with a stated API request quota and hosted remediation features.
• PromptGuard offers a freemium alternative with a stated monthly scan allowance and log retention limit.
• AIRTA Red Team is another free open-source red-team option that requires users to bring an LLM API key.
• AIRTaaS is a free web-based alternative.
• Augustus is free Apache 2.0 open-source software for Linux, macOS, Windows and self-hosted use; API usage may require provider credentials.

Verdict

Project Moonshot is a compelling free choice for AI developers and compliance teams that need configurable LLM safety evaluations and can manage local setup. Its combination of benchmarks, adversarial tests, custom recipes and exportable results is the main reason to choose it. Look elsewhere if beta maturity, runtime prerequisites or the need to supply test assets makes self-managed software a poor fit.