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Aim Security
Quick Start
1. Create a new Aim Guard
Go to Aim Application and create a new guard.
When prompted, select API option, and name your guard.
:::note In case you want to host your guard on-premise, you can enable this option by installing Aim Outpost prior to creating the guard. :::
2. Configure your Aim Guard policies
In the newly created guard's page, you can find a reference to the prompt policy center of this guard.
You can decide which detections will be enabled, and set the threshold for each detection.
:::info When using LiteLLM with virtual keys, key-specific policies can be set directly in Aim's guards page by specifying the virtual key alias when creating the guard.
Only the aliases of your virtual keys (and not the actual key secrets) will be sent to Aim. :::
3. Add Aim Guardrail on your LiteLLM config.yaml
Define your guardrails under the guardrails
section
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: openai/gpt-3.5-turbo
api_key: os.environ/OPENAI_API_KEY
guardrails:
- guardrail_name: aim-protected-app
litellm_params:
guardrail: aim
mode: [pre_call, post_call] # "During_call" is also available
api_key: os.environ/AIM_API_KEY
api_base: os.environ/AIM_API_BASE # Optional, use only when using a self-hosted Aim Outpost
Under the api_key
, insert the API key you were issued. The key can be found in the guard's page.
You can also set AIM_API_KEY
as an environment variable.
By default, the api_base
is set to https://api.aim.security
. If you are using a self-hosted Aim Outpost, you can set the api_base
to your Outpost's URL.
4. Start LiteLLM Gateway
litellm --config config.yaml
5. Make your first request
:::note The following example depends on enabling PII detection in your guard. You can adjust the request content to match different guard's policies. :::
:::note
When using LiteLLM with virtual keys, an Authorization
header with the virtual key is required.
:::
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "hi my email is ishaan@berri.ai"}
],
"guardrails": ["aim-protected-app"]
}'
If configured correctly, since ishaan@berri.ai
would be detected by the Aim Guard as PII, you'll receive a response similar to the following with a 400 Bad Request
status code:
{
"error": {
"message": "\"ishaan@berri.ai\" detected as email",
"type": "None",
"param": "None",
"code": "400"
}
}
:::note
When using LiteLLM with virtual keys, an Authorization
header with the virtual key is required.
:::
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "hi what is the weather"}
],
"guardrails": ["aim-protected-app"]
}'
The above request should not be blocked, and you should receive a regular LLM response (simplified for brevity):
{
"model": "gpt-3.5-turbo-0125",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "I can’t provide live weather updates without the internet. Let me know if you’d like general weather trends for a location and season instead!",
"role": "assistant"
}
}
]
}
Advanced
Aim Guard provides user-specific Guardrail policies, enabling you to apply tailored policies to individual users.
To utilize this feature, include the end-user's email in the request payload by setting the x-aim-user-email
header of your request.
curl -i http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "x-aim-user-email: ishaan@berri.ai" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "hi what is the weather"}
],
"guardrails": ["aim-protected-app"]
}'