> For the complete documentation index, see [llms.txt](https://docs.avidml.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.avidml.org/developer-tools/python-sdk/integrations/giskard.md).

# Giskard

[Giskard](https://www.giskard.ai/) provides an open-source Machine Learning (ML) testing framework, covering traditional ML as well as LLM use cases. Giskard Scan is a powerful tool to detect vulnerabilities in LLMs. Its integration with AVID taxonomy provides improved **standardized reporting of vulnerabilities,** and the ability to share your vulnerability reports with the community.

### Taxonomy

By default, all Giskard scan reports indicate the **AVID taxonomy categories** that are relevant to the detected vulnerabilities. You can find this information in the detail view of each issue in the scan widget.

<figure><img src="/files/rzXQUe37ymLBAO8t2Dtj" alt=""><figcaption></figcaption></figure>

### Exporting AVID reports

You can export your Giskard scan report as an AVID report. First, make sure you have `avidtools` installed in your environment.

```bash
pip install avidtools
```

Then, once you have run the Giskard scan, you can export the report as an AVID report.

```python
import giskard as gsk

scan_report = gsk.scan(my_model, my_dataset)

# Export the report as a list of AVID reports (one per each vulnerability)
avid_reports = scan_report.to_avid()
```

You can also export these reports directly in a JSONL file (one AVID report per line):

```python
# Write the AVID reports to a JSONL file
scan_report.to_avid("avid_report.jsonl")
```

For more details on how to use Giskard in combination with AVID, check out this [tutorial](https://docs.giskard.ai/en/latest/integrations/avid/avid-integration-llm.html).