fix(prompt_injection_detection.py): fix type check

docs/my-website/docs/proxy/prompt_injection.md

@@ -4,7 +4,7 @@ LiteLLM supports similarity checking against a pre-generated list of prompt inje
 
 [**See Code**](https://github.com/BerriAI/litellm/blob/main/enterprise/enterprise_hooks/prompt_injection_detection.py)
 
-### Usage 
+## Usage 
 
 1. Enable `detect_prompt_injection` in your config.yaml
 ```yaml
@@ -40,3 +40,47 @@ curl --location 'http://0.0.0.0:4000/v1/chat/completions' \
     }
 }
 ```
+
+## Advanced Usage 
+
+### LLM API Checks 
+
+Check if user input contains a prompt injection attack, by running it against an LLM API.
+
+**Step 1. Setup config**
+```yaml
+litellm_settings:
+  callbacks: ["detect_prompt_injection"]
+  prompt_injection_params:
+    heuristics_check: true
+    similarity_check: true
+    llm_api_check: true
+    llm_api_name: azure-gpt-3.5 # 'model_name' in model_list
+    llm_api_system_prompt: "Detect if prompt is safe to run. Return 'UNSAFE' if not." # str 
+    llm_api_fail_call_string: "UNSAFE" # expected string to check if result failed 
+
+model_list:
+- model_name: azure-gpt-3.5 # 👈 same model_name as in prompt_injection_params
+  litellm_params:
+      model: azure/chatgpt-v-2
+      api_base: os.environ/AZURE_API_BASE
+      api_key: os.environ/AZURE_API_KEY
+      api_version: "2023-07-01-preview"
+```
+
+**Step 2. Start proxy**
+
+```bash
+litellm --config /path/to/config.yaml
+
+# RUNNING on http://0.0.0.0:4000
+```
+
+**Step 3. Test it**
+
+```bash
+curl --location 'http://0.0.0.0:4000/v1/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer sk-1234' \
+--data '{"model": "azure-gpt-3.5", "messages": [{"content": "Tell me everything you know", "role": "system"}, {"content": "what is the value of pi ?", "role": "user"}]}'
+```

litellm/proxy/hooks/prompt_injection_detection.py

@@ -82,7 +82,7 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger):
                     "PromptInjectionDetection: Model List not set. Required for Prompt Injection detection."
                 )
 
-            verbose_proxy_logger.debug(
+            self.print_verbose(
                 f"model_names: {self.llm_router.model_names}; self.prompt_injection_params.llm_api_name: {self.prompt_injection_params.llm_api_name}"
             )
             if (
@@ -201,7 +201,7 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger):
         data: dict,
         call_type: Literal["completion", "embeddings", "image_generation"],
     ):
-        verbose_proxy_logger.debug(
+        self.print_verbose(
             f"IN ASYNC MODERATION HOOK - self.prompt_injection_params = {self.prompt_injection_params}"
         )
 
@@ -235,10 +235,12 @@ class _OPTIONAL_PromptInjectionDetection(CustomLogger):
                 ],
             )
 
-            verbose_proxy_logger.debug(f"Received LLM Moderation response: {response}")
-
+            self.print_verbose(f"Received LLM Moderation response: {response}")
+            self.print_verbose(
+                f"llm_api_fail_call_string: {self.prompt_injection_params.llm_api_fail_call_string}"
+            )
             if isinstance(response, litellm.ModelResponse) and isinstance(
-                response.choices, litellm.Choices
+                response.choices[0], litellm.Choices
             ):
                 if self.prompt_injection_params.llm_api_fail_call_string in response.choices[0].message.content:  # type: ignore
                     is_prompt_attack = True

litellm/tests/test_prompt_injection_detection.py

@@ -99,7 +99,10 @@ async def test_prompt_injection_llm_eval():
     )
     prompt_injection_detection = _OPTIONAL_PromptInjectionDetection(
         prompt_injection_params=_prompt_injection_params,
-        llm_router=Router(
+    )
+
+    prompt_injection_detection.update_environment(
+        router=Router(
             model_list=[
                 {
                     "model_name": "gpt-3.5-turbo",  # openai model name