Thank you for your interest in Failure-First. This is a research project, not a typical open-source codebase. Contributions are welcome, but the ways to contribute differ from a standard software project.
If you find errors in our published findings, methodology gaps, broken links on failurefirst.org, or inconsistencies in the public documentation, please open a GitHub issue.
The most impactful contribution for a research project is citation. If our findings, datasets, or methodology inform your work, please cite us:
@software{failure_first_2026,
title = {Failure-First: Adversarial Evaluation Framework for Embodied AI},
author = {Wedd, Adrian},
year = {2026},
url = {https://failurefirst.org},
note = {227 models, 141{,}561 prompts, 337 attack techniques}
}We welcome collaboration with AI safety researchers, red-team practitioners, and frontier lab security teams. If you have adversarial evaluation results, novel attack technique taxonomies, or defense effectiveness data you would like to contribute or cross-validate, open a GitHub issue describing your institutional affiliation and research focus.
If you have adversarial evaluation datasets that could strengthen the corpus, we accept contributions subject to:
- Pattern-level only: no operational exploits or copy-paste attack templates
- Provenance documented: source, collection methodology, and intended use
- Schema compliance: data must conform to our versioned JSON Schemas (documented in the private repository; we will assist with formatting)
- Safety review: all contributed data undergoes review before inclusion
Corrections, clarifications, and improvements to public-facing documentation (this repository, the design charter, security policy) are welcome via pull request.
- Operational exploit code or working jailbreak prompts
- Model-specific bypass techniques intended for attack
- Raw adversarial datasets without provenance
- Content that facilitates real-world harm outside AI safety research
If you discover vulnerabilities in AI systems -- whether through this framework or independent research -- please follow responsible disclosure practices. See SECURITY.md for our coordinated disclosure process.
- Open a GitHub issue describing the proposed contribution
- For documentation changes, submit a pull request directly
- For research collaborations and dataset contributions, we will coordinate via issue discussion
All contributions undergo safety review to ensure content remains pattern-level, defensively purposed, and appropriate for a public repository. This review is not optional and applies equally to maintainers and external contributors.
By contributing, you agree that your contributions will be licensed under the MIT License.
Last updated: 2026-03-29