# Custom Taxonomies

Using the MISP format allows us to seamlessly integrate arbitrary taxonomies into the AVID database and related workflows. This is crucial for practitioner adoption, since AI developers and vendors often work with operational taxonomies specific to their domain. Examples of such deep taxonomies/categorizations include MITRE ATLAS, taxonomies for [downstream harms](#user-content-fn-1)[^1] and [LLM risks](#user-content-fn-2)[^2], and [Risk Cards](#user-content-fn-3)[^3].

## Auxiliary Taxonomies in avid-schema

The following non-AVID taxonomies are currently maintained in [avid-schema/taxonomy](https://github.com/avidml/avid-schema/tree/main/taxonomy):

* [InjectLab LLM Taxonomy (`injectlab-llm.json`)](https://github.com/avidml/avid-schema/blob/main/taxonomy/injectlab-llm.json): categorizes LLM prompt-injection and jailbreak behaviors.
* [Risk Cards (`risk-cards.json`)](https://github.com/avidml/avid-schema/blob/main/taxonomy/risk-cards.json): captures deployment-time risk dimensions used in Risk Cards style assessments.
* [Trail of Bits ODDs (`trail-of-bits-ODDs.json`)](https://github.com/avidml/avid-schema/blob/main/taxonomy/trail-of-bits-ODDs.json): represents Operational Design Domain constraints and assumptions for AI systems[^4].

We welcome community contributions to the taxonomy repository. To contribute, add a JSON file following the MISP structure and open a pull request in [avid-schema](https://github.com/avidml/avid-schema).

[^1]: [Shelby et al](https://arxiv.org/abs/2210.05791). Identifying Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction, arXiV, 2021.

[^2]: [Weidinger et al](https://dl.acm.org/doi/10.1145/3531146.3533088). Taxonomy of Risks posed by Language Models, FAccT, 2022.

[^3]: [Derczynski et al](https://arxiv.org/abs/2303.18190). Assessing Language Model Deployment with Risk Cards, arXiV, 2023.

[^4]: Khlaaf, Heidy. Toward Comprehensive Risk Assessments and Assurance of AI-Based Systems, Trail of Bits, 2023.