diff --git a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
index c587a280834..c12c8ca48b0 100644
--- a/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
+++ b/libs/experimental/langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py
@@ -1,21 +1,28 @@
 """Tool for the identification of prompt injection attacks."""
+from __future__ import annotations
 
-from enum import Enum
+from typing import TYPE_CHECKING
 
+from langchain.pydantic_v1 import Field
 from langchain.tools.base import BaseTool
-from transformers import Pipeline, pipeline
+
+if TYPE_CHECKING:
+    from transformers import Pipeline
 
 
-class PromptInjectionModelOutput(str, Enum):
-    """Output of the prompt injection model."""
-
-    LEGIT = "LEGIT"
-    INJECTION = "INJECTION"
+def _model_default_factory() -> Pipeline:
+    try:
+        from transformers import pipeline
+    except ImportError as e:
+        raise ImportError(
+            "Cannot import transformers, please install with "
+            "`pip install transformers`."
+        ) from e
+    return pipeline("text-classification", model="deepset/deberta-v3-base-injection")
 
 
 class HuggingFaceInjectionIdentifier(BaseTool):
-    """Tool that uses deberta-v3-base-injection model
-    to identify prompt injection attacks."""
+    """Tool that uses deberta-v3-base-injection to detect prompt injection attacks."""
 
     name: str = "hugging_face_injection_identifier"
     description: str = (
@@ -23,21 +30,12 @@ class HuggingFaceInjectionIdentifier(BaseTool):
         "Useful for when you need to ensure that prompt is free of injection attacks. "
         "Input should be any message from the user."
     )
-
-    model: Pipeline = pipeline(
-        "text-classification", model="deepset/deberta-v3-base-injection"
-    )
-
-    def _classify_user_input(self, query: str) -> bool:
-        result = self.model(query)
-        result = sorted(result, key=lambda x: x["score"], reverse=True)
-        if result[0]["label"] == PromptInjectionModelOutput.INJECTION:
-            return False
-        return True
+    model: Pipeline = Field(default_factory=_model_default_factory)
 
     def _run(self, query: str) -> str:
         """Use the tool."""
-        is_query_safe = self._classify_user_input(query)
-        if not is_query_safe:
+        result = self.model(query)
+        result = sorted(result, key=lambda x: x["score"], reverse=True)
+        if result[0]["label"] == "INJECTION":
             raise ValueError("Prompt injection attack detected")
         return query