mindgard 0.36.0

Test your AI model's security without leaving your terminal.

Mindgard CLI

Securing AI Models.

Continuous automated red teaming platform.

Identify & remediate your AI models' security risks with Mindgard's market leading attack library. Mindgard covers many threats including:

✅ Jailbreaks

✅ Prompt Injection

✅ Model Inversion

✅ Extraction

✅ Poisoning

✅ Evasion

✅ Membership Inference

Mindgard CLI is fully integrated with Mindgard's platform to help you identify and triage threats, select remediations, and track your security posture over time.

Test continuously in your MLops pipeline to identify model posture changes from customisation activities including prompt engineering, RAG, fine-tuning, and pre-training.

Table of Contents

• 🚀 Install
• ✅ Testing demo models
• ✅ Testing your models
• 🚦 Using in an ML-Ops pipeline

🚀 Install Mindgard CLI

pip install mindgard

🔑 Login

mindgard login

✅ Test a mindgard hosted model

mindgard sandbox mistral
mindgard sandbox cfp_faces

✅ Test your own models

mindgard test <name> --url <url> <other settings>

e.g.

mindgard test my-model-name \
  --url http://127.0.0.1/infer \ # url to test
  --selector '["response"]' \ # JSON selector to match the textual response
  --request-template '{"prompt": "[INST] {system_prompt} {prompt} [/INST]"}' \ # how to format the system prompt and prompt in the API request
  --system-prompt 'respond with hello' # system prompt to test the model with

📋 Using a Configuration File

You can specify the settings for the mindgard test command in a TOML configuration file. This allows you to manage your settings in a more structured way and avoid passing them as command-line arguments.

Then run: mindgard test --config-file mymodel.toml

Examples

There are examples of what the configuration file (mymodel.toml) might look like here in the examples/ folder

Here are two examples:

Targeting OpenAI

This example uses the built in preset settings for openai. Presets exist for openai, huggingface, and anthropic

target = "my-model-name"
preset = "openai"
api_key= "CHANGE_THIS_TO_YOUR_OPENAI_API_KEY"
system-prompt = '''
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
'''

You will need to substitute your own api_key value.

The target setting is an identifier for the model you are testing within the Mindgard platform, tests for the same model will be grouped and traceable over time.

Altering the system-prompt enables you to compare results with different system prompts in use. Some of Mindgard's tests assess the efficacy of your system prompt.

Any of these settings can also be passed as command line arguments. e.g. mindgard test my-model-name --system-prompt 'You are...'. This may be useful to pass in a dynamic value for any of these settings.

Targeting a more general model API without a suitable preset

This example shows how you might test OpenAI if the preset did not exist. With the request_template and selector settings you can interface with any JSON API.

target = "my-model-name"
url = "https://api.openai.com/v1/chat/completions"
request_template = '''
{
    "messages": [
        {"role": "system", "content": "{system_prompt}"},
        {"role": "user", "content": "{prompt}"}],
    "model": "gpt-3.5-turbo",
    "temperature": 0.0,
    "max_tokens": 1024
}
'''
selector = '''
choices[0].message.content
'''
headers = "Authorization: Bearer CHANGE_THIS_TO_YOUR_OPENAI_API_KEY"
system_prompt = '''
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
'''

The request_template setting specifies how to structure an outgoing message to the model. You will need to specify the {system_prompt} and {prompt} placeholders so that Mindgard knows how to pass this information to your custom API.

The url setting should point to an inference endpoint for your model under test. Mindgard will POST messages here formatted by the above request_template setting.

The selector setting is a JSON selector and specifies how to extract the model's response from the API response.

The headers setting allows you to specify a custom HTTP header to include with outgoing requests, for example to implement a custom authentication method.

🚦 Using in an ML-Ops pipeline

The exit code of a test will be non-zero if the test identifies risks above your risk threshold. To override the default risk-threshold pass --risk-threshold 50. This will cause the CLI to exit with an non-zero exit status if any test results in a risk score over 50.

See an example of this in action here: https://github.com/Mindgard/mindgard-github-action-example

Acknowledgements.

We would like to thank and acknowledge various research works from the Adversarial Machine Learning community, which inspired and informed the development of several AI security tests accessible through Mindgard CLI.

Jiang, F., Xu, Z., Niu, L., Xiang, Z., Ramasubramanian, B., Li, B., & Poovendran, R. (2024). ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs. arXiv [Cs.CL]. Retrieved from http://arxiv.org/abs/2402.11753

Russinovich, M., Salem, A., & Eldan, R. (2024). Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack. arXiv [Cs.CR]. Retrieved from http://arxiv.org/abs/2404.01833

Goodside, R. LLM Prompt Injection Via Invisible Instructions in Pasted Text, Retreved from https://x.com/goodside/status/1745511940351287394

Yuan, Y., Jiao, W., Wang, W., Huang, J.-T., He, P., Shi, S., & Tu, Z. (2024). GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher. arXiv [Cs.CL]. Retrieved from http://arxiv.org/abs/2308.06463