Project Moonshot from the AI Verify Foundation unifies LLM benchmark testing and adversarial red-teaming in one modular Python platform for AI developers and authorized evaluation teams.
| Tool | aiverify-foundation/moonshot — a simple and modular tool to evaluate and red-team any LLM application (v0.7.6, beta) |
| Category | LLM evaluation and red-teaming framework (Python 3.11, Apache-2.0) |
| Primary Use | Running benchmark tests for performance, trust and safety (bias, toxicity, hallucination) plus adversarial prompt red-teaming against LLM apps you are authorized to test |
| Safe Use | Designed for AI developers, compliance teams, and authorized assessors evaluating their own LLM applications before release; entirely a defensive quality-assurance and pre-deployment testing workflow |
| Telemetry Note | Generates local HTML reports with interactive charts and downloadable raw JSON results; all testing is against endpoints you configure as connector endpoints, so activity is confined to your own target systems |
Project Moonshot, developed by the AI Verify Foundation, positions itself as a bridge between two activities that are usually handled by separate toolchains: benchmarking and red-teaming of LLM-based applications. The repository (aiverify-foundation/moonshot) currently sits at version 0.7.6, is written in Python against version 3.11, and is released under Apache-2.0. The framing in the README is explicitly institutional — this is tooling for AI developers and compliance teams who need to evaluate LLM apps and models before or during deployment, not a generic attack framework. That provenance matters: it comes from a foundation focused on AI governance, which shapes the whole design toward measurable, repeatable, reportable evaluation.
The core architectural idea is what the project calls Cookbooks and Recipes. A Recipe composes custom datasets (input-target pairs), optional prompt templates, an evaluation metric, and grading scales into a reusable benchmark test. A Cookbook bundles tests — including pre-built ones implementing IMDA's Starter Kit for LLM-based App Testing — into guided workflows. This composability is the differentiator: rather than a monolithic benchmark harness, Moonshot treats datasets, metrics, connectors, and attack modules as swappable modules, which the README highlights under its extensibility claims. For a team evaluating a RAG application, the CLI tutorial on creating custom benchmark tests suggests this is the primary intended scenario.
On the benchmarking side, the tool tests across two axes. Performance metrics cover conventional ground such as accuracy and BLEU scores. The trust-and-safety axis is more interesting for security readers: bias, toxicity, and hallucination testing against a catalog of open-source benchmark datasets. The dataset list is published separately on the project docs site, and the architecture implies datasets are data files loaded by the moonshot-data companion repository — a deliberate split that keeps the engine clean of test assets. Note the installation requirement: after pip install "aiverify-moonshot[all]", you must run python -m moonshot -i moonshot-data -i moonshot-ui to pull test assets and the web UI before any tests will execute.
The red-teaming capability is where Moonshot overlaps with offensive tooling, though it stays firmly on the authorized-evaluation side of the line. Attack modules generate adversarial prompts using either algorithmic strategies or a generative LLM, supported by prompt templates and context strategies. The intent, per the README, is to uncover potential misuse scenarios and vulnerabilities in your own LLM applications through streamlined adversarial prompting. The attack-module catalog lives in the docs rather than the README, and the modular design means new attack techniques can be added as plugins — the same extensibility contract as the benchmark side.
Interaction surfaces are plural and thoughtful. There is a Web UI launched via python -m moonshot web (served at http://localhost:3000/), an interactive CLI started with python -m moonshot cli interactive, and programmatic access through Moonshot Library APIs and Moonshot Web APIs — the latter documented via Swagger. Jupyter notebook examples ship in the repo under examples/jupyter-notebook. The existence of first-class library APIs signals that the project expects to be embedded in MLOps pipelines, running regression-style safety evaluations continuously rather than as one-off audits. That is the mature deployment pattern for LLM safety: bake evaluation into CI rather than gate it at release.
Installation flexibility is better than most research-grade tooling. Beyond the full install, you can scope to pip install aiverify-moonshot (library only), pip install "aiverify-moonshot[cli]", or pip install "aiverify-moonshot[web-api]" depending on integration needs. Source installs follow the standard git clone plus pip install -r requirements.txt pattern. One operational caveat from the README: the Web UI requires Node.js version 20.11.1 LTS or above, which introduces a second runtime dependency that pure-Python shops should plan for. A virtual environment is recommended, and a troubleshooting guide exists in the docs for installation friction.
Reporting is a genuine strength rather than an afterthought. Results land in HTML reports with interactive charts for human review, and raw JSON is downloadable for programmatic consumption. This dual output matters in practice: compliance stakeholders want visual artifacts they can attach to governance processes, while engineers want structured results they can diff across model versions. The AI Verify Foundation lineage is visible here — this is tooling built for people who must document that evaluation happened and what it found, which is precisely the evidence trail an authorized assessment requires.
Connector endpoints are the integration mechanism for targets. Tutorials exist for creating custom connector endpoints in both the Web UI and the CLI, meaning the tool does not assume any specific model provider or API shape. This is the right abstraction for the current landscape, where an LLM app might front OpenAI-style APIs, self-hosted models, or a RAG pipeline with retrieval in the loop. The benchmark and red-team runs execute against whatever endpoint you register, keeping Moonshot provider-neutral.
The project is candid about maturity: the README states plainly that Moonshot is in beta, under active development, with stability still being improved. With roughly 353 stars and topics spanning benchmarking, evaluation-framework, llm, red-teaming, and trustworthy-ai, it occupies a credible niche — less noisy than academic benchmark repos, more structured than ad-hoc prompt-testing scripts. Teams adopting it should pin versions and expect API churn, but the modular architecture limits blast radius when the framework shifts underneath custom Recipes.
For authorized security professionals, the practical fit is pre-deployment adversarial testing of generative AI features — the growing surface where traditional web-app testing methodology does not transfer cleanly. Moonshot gives that work structure: repeatable attack modules, graded metrics, and auditable reports. What it is not is a scanner pointed at third-party systems; everything in its design assumes you own or are explicitly authorized to evaluate the target endpoint. Used inside that boundary, it is one of the more complete open-source answers to the question of how you actually operationalize LLM safety evaluation rather than just talking about it.
aiverify-foundation/moonshot.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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