nvidia/garakModular LLM vulnerability scanner that probes for hallucination, data leakage, prompt injection, jailbreaks, and toxicity using static, dynamic, and adaptive probes across multiple model providers.
Generative AI Red-teaming & Assessment Kit
garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. If you know nmap or msf / Metasploit Framework, garak does somewhat similar things to them, but for LLMs.
garak focuses on ways of making an LLM or dialog system fail. It combines static, dynamic, and adaptive probes to explore this.
garak's a free tool. We love developing it and are always interested in adding functionality to support applications.
currently supports:
garak is a command-line tool. It's developed in Linux and OSX.
pipJust grab it from PyPI and you should be good to go:
python -m pip install -U garak
pipThe standard pip version of garak is updated periodically. To get a fresher version from GitHub, try:
python -m pip install -U git+https://github.com/NVIDIA/garak.git@main
garak has its own dependencies. You can to install garak in its own Conda environment:
conda create --name garak "python>=3.11,<=3.14"
conda activate garak
gh repo clone NVIDIA/garak
cd garak
python -m pip install -e .
OK, if that went fine, you're probably good to go!
Note: if you cloned before the move to the NVIDIA GitHub organisation, but you're reading this at the github.com/NVIDIA URI, please update your remotes as follows:
git remote set-url origin https://github.com/NVIDIA/garak.git
The general syntax is:
garak <options>
garak needs to know what model to scan, and by default, it'll try all the probes it knows on that model, using the vulnerability detectors recommended by each probe. You can see a list of probes using:
garak --list_probes
To specify a generator, use the --target_type and, optionally, the --target_name options. Model type specifies a model family/interface; model name specifies the exact model to be used. The "Intro to generators" section below describes some of the generators supported. A straightforward generator family is Hugging Face models; to load one of these, set --target_type to huggingface and --target_name to the model's name on Hub (e.g. "RWKV/rwkv-4-169m-pile"). Some generators might need an API key to be set as an environment variable, and they'll let you know if they need that.
garak runs all the probes by default, but you can be specific about that too. --spec probes.promptinject will use only the PromptInject framework's methods, for example. You can also specify one specific plugin instead of a plugin family by adding the plugin name after a .; for example, --spec probes.lmrc.SlurUsage will use an implementation of checking for models generating slurs based on the Language Model Risk Cards framework.
For help and inspiration, find us on Twitter or discord!
Probe a commercial model for encoding-based prompt injection (OSX/*nix) (replace example value with a real OpenAI API key)
export OPENAI_API_KEY="sk-123XXXXXXXXXXXX"
python3 -m garak --target_type openai --target_name gpt-5-nano --spec probes.encoding
See if the Hugging Face version of GPT2 is vulnerable to DAN 11.0
python3 -m garak --target_type huggingface --target_name gpt2 --spec probes.dan.Dan_11_0
For each probe loaded, garak will print a progress bar as it generates. Once generation is complete, a row evaluating that probe's results on each detector is given. If any of the prompt attempts yielded an undesirable behavior, the response will be marked as FAIL, and the failure rate given.
Here are the results with the encoding module on a GPT-3 variant:
And the same results for ChatGPT:
We can see that the more recent model is much more susceptible to encoding-based injection attacks, where text-babbage-001 was only found to be vulnerable to quoted-printable and MIME encoding injections. The figures at the end of each row, e.g. 840/840, indicate the number of text generations total and then how many of these seemed to behave OK. The figure can be quite high because more than one generation is made per prompt - by default, 10.
Errors go in garak.log; the run is logged in detail in a .jsonl file specified at analysis start & end. There's a basic analysis script in analyse/analyse_log.py which will output the probes and prompts that led to the most hits.
Send PRs & open issues. Happy hunting!
Using the Pipeline API:
--target_type huggingface (for transformers models to run locally)--target_name - use the model name from Hub. Only generative models will work. If it fails and shouldn't, please open an issue and paste in the command you tried + the exception!Using the Inference API:
--target_type huggingface.InferenceAPI (for API-based model access)--target_name - the model name from Hub, e.g. "mosaicml/mpt-7b-instruct"