❓FAQ

What's this tool for?

garak is designed to help discover situations where a language model generates outputs that one might not want it to. If you know nmap or metasplot for traditional netsec/infosec analysis, then garak aims to operate in a similar space for language models.

How does it work?

garak has probes that try to look for different "vulnerabilities". Each probs sends specific prompts to models, and gets multiple generations for each prompt. LLM output is often stochastic, so a single test isn't very informative. These generations are then processed by "detectors", which will look for "hits". If a detector registers a hit, that attempt is registered as failing. Finally, a report is output with the success/failure rate for each probe and detector.

Do these results have scientific validity?

No. The scores from any probe don't operate on any kind of normalised scale. Higher passing percentage is better, but that's it. No meaningful comparison can be made between scores from different probes.

How does it determine a pass/fail score for replies?

Each detector is different. Most use a classifier (either locally or via API) to judge the response, or look for keywords that are (or are not) present in the language model output, but there are many different failure modes. Your best bet might be to look in the technical reference documentation or the source code.

Does garak allow for additional prompts?

Additional prompts can be probed by creating a new plugin -- this isn't as tough as it sounds; take a look at the modules in the garak/probes/ directory for inspiration. The reference documentation includes a section on contributing code.

How will a auditor know what was used in testing?

The JSONL report created for each run includes language model parameters, all the prompts sent to the mode, all the model responses, and also the mapping between these and evaluation scores. There's a JSONL report analysis script in analyze/analyze_log.py.

Do you have plans to setup an environment for running these tests on HuggingFace?

Not immediately, but if you have the Gradio skills, get in touch!

Can you add support for vulnerability X?

Perhaps - please open an issue, including a description of the vulnerability, example prompts, and tag it "new plugin" and "probes".

Can you add support for model X?

Would love to! Please open an issue, tagging it "new plugin" and "generators".

Why the name?

It's named after a smooth-talking, manipulative, persuasive, well-written character from a nineties TV series. Because we need tools like that to dissect LLM behavior.