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Evaluation toolEvaluation tool

AILuminate

Version Safety v1.0 and v1.1; Jailbreak v0.5

Maintained by MLCommons (AI Risk & Reliability working group)138

AILuminate is MLCommons' family of safety and security benchmarks for generative AI systems, organized around 12 hazard categories. The v1.0 safety benchmark (December 2024) tests general-purpose chat systems with more than 24,000 human-written prompts and grades responses with an ensemble of evaluator models; a jailbreak benchmark measures how safety degrades under attack.1387

Last reviewedEntry updated Documented release Dec 4, 2024

Availability and license

Overall availability

Partial

1,200-prompt demo sets in English and French (each a 10% sample of the corresponding 12,000-prompt practice set) are public under CC BY 4.0, and the ModelBench code is public under Apache 2.0. The full English and French practice prompt sets are offered to MLCommons members on request. The official test prompts are hidden, and official grading uses MLCommons' evaluator ensemble.861023

Availability is separate from permission: read the license before using or redistributing.

The ailuminate repository's README licenses the demo prompt data under CC BY 4.0, while the repository's LICENSE.md file contains the Apache License 2.0. The README warns that the prompts were written to elicit hazardous responses and may be offensive or disturbing.89

Public materials checklist

Items for a evaluation tool under USASI rubric v0.1. Unknown means unassessed or insufficient evidence.
Public materials checklist for AILuminate
ItemStatusNotes and evidence
CodeIs the evaluation code published?PublicModelBench (which now includes ModelGauge) runs tests against systems under test and aggregates hazard scores; Apache 2.0.1011
Tasks / dataAre the tasks or test data available?PartialOnly the 1,200-prompt English and French demo sets are public; full practice sets go to MLCommons members and the official test prompts are hidden to limit overfitting.862
MethodologyIs the method for scoring described?PublicThe safety methodology page and the v1.0 paper describe the hazard taxonomy, personas, practice and official prompt splits, the evaluator ensemble, and grading against reference models on a five-tier scale.27
ReproducibilityAre instructions for reproducing results published?PartialModelBench documents how to run a practice benchmark (using a Llama Guard evaluator via Together AI); official results depend on the private prompt set and official evaluator ensemble.102
LimitationsAre known limitations documented?PublicThe v1.0 paper names evaluator uncertainty and the single-turn format as limitations and lists multi-turn, multimodal, additional-language, and new-hazard coverage as needed work.7

What it is useful for

Assessing how often a chat system gives responses that violate AILuminate's hazard guidelines, using practice prompts for development and a hidden official test for benchmarking.28

Run and use notes

Documented facts only. No hardware or performance claims are made without a cited source and stated assumptions.
  • The AILuminate page lists safety benchmarks for text in English, French, and Chinese, and jailbreak benchmarks for text and text-plus-image inputs in English; an agentic workstream is in development.1
  • In February 2026 MLCommons published a jailbreak taxonomy methodology paper, which it described as groundwork rather than a new benchmark release.12
  • MLCommons announced AILuminate v1.1 in February 2025, adding French; the announcement calls the English benchmark v1.0 and the French one v1.1. The Safety page still labels both the English and French official results as v1.0.54

Organization context

U.S. eligibility

Project eligibility rests on documented governing or maintaining entities, not on contributors.

Eligible · basis: U.S.-governed project

AILuminate is developed by the MLCommons AI Risk & Reliability working group and published by MLCommons, which licenses the demo prompt data and hosts the ModelBench code. MLCommons Association is listed by the IRS as a 501(c)(6) organization with a Dover, Delaware address.381013

Assessed Sep 29, 2026

Sources

  1. 1.
    AILuminate - MLCommons (external site: mlcommons.org)

    MLCommons · Official page · accessed Sep 29, 2026

  2. 2.
    Safety Methodology - AILuminate (external site: mlcommons.org)

    MLCommons · Documentation · accessed Sep 29, 2026

  3. 3.
  4. 4.
    AILuminate Safety - MLCommons (external site: mlcommons.org)

    MLCommons · Official page · accessed Sep 29, 2026

  5. 5.
  6. 6.
    MLCommons Releases French AILuminate Benchmark Demo Prompt Dataset to Github (external site: mlcommons.org)

    MLCommons · Announcement · published Apr 16, 2025 · accessed Sep 29, 2026

  7. 7.
    AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons (arXiv 2503.05731) (external site: arxiv.org)

    MLCommons AI Risk & Reliability working group · Paper · published Apr 18, 2025 · accessed Sep 29, 2026

  8. 8.
  9. 9.
  10. 10.
    mlcommons/modelbench (external site: github.com)

    MLCommons · Repository · accessed Sep 29, 2026

  11. 11.
  12. 12.
    MLCommons Lays the Foundation for Defensible Jailbreak Benchmarking (external site: mlcommons.org)

    MLCommons · Announcement · published Feb 16, 2026 · accessed Sep 29, 2026

  13. 13.

This listing is not an endorsement, a safety assessment, or a federal approval.

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