diff --git a/llama_stack/providers/impls/meta_reference/safety/shields/code_scanner.py b/llama_stack/providers/impls/meta_reference/safety/shields/code_scanner.py
index 75ec7c37b..340ccb517 100644
--- a/llama_stack/providers/impls/meta_reference/safety/shields/code_scanner.py
+++ b/llama_stack/providers/impls/meta_reference/safety/shields/code_scanner.py
@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from codeshield.cs import CodeShield
 from termcolor import cprint
 
 from .base import ShieldResponse, TextShield
@@ -16,6 +15,8 @@ class CodeScannerShield(TextShield):
         return BuiltinShield.code_scanner_guard
 
     async def run_impl(self, text: str) -> ShieldResponse:
+        from codeshield.cs import CodeShield
+
         cprint(f"Running CodeScannerShield on {text[50:]}", color="magenta")
         result = await CodeShield.scan_code(text)
         if result.is_insecure:
diff --git a/llama_stack/providers/impls/meta_reference/safety/shields/prompt_guard.py b/llama_stack/providers/impls/meta_reference/safety/shields/prompt_guard.py
index 67bc6a6db..acaf515b5 100644
--- a/llama_stack/providers/impls/meta_reference/safety/shields/prompt_guard.py
+++ b/llama_stack/providers/impls/meta_reference/safety/shields/prompt_guard.py
@@ -11,7 +11,6 @@ import torch
 
 from llama_models.llama3.api.datatypes import Message
 from termcolor import cprint
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 from .base import message_content_as_str, OnViolationAction, ShieldResponse, TextShield
 from llama_stack.apis.safety import *  # noqa: F403
@@ -61,6 +60,8 @@ class PromptGuardShield(TextShield):
             raise ValueError("Temperature must be greater than 0")
         self.device = "cuda"
         if PromptGuardShield._model_cache is None:
+            from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
             # load model and tokenizer
             tokenizer = AutoTokenizer.from_pretrained(model_dir)
             model = AutoModelForSequenceClassification.from_pretrained(