Fix: Triton inference spec hmac exposure

sagemaker-serve/src/sagemaker/serve/model_server/multi_model_server/prepare.py

@@ -26,7 +26,6 @@
 from sagemaker.serve.spec.inference_spec import InferenceSpec
 from sagemaker.serve.detector.dependency_manager import capture_dependencies
 from sagemaker.serve.validations.check_integrity import (
-    generate_secret_key,
     compute_hash,
 )
 from sagemaker.core.remote_function.core.serialization import _MetaData
@@ -119,11 +118,8 @@ def prepare_for_mms(
 
     capture_dependencies(dependencies=dependencies, work_dir=code_dir)
 
-    secret_key = generate_secret_key()
     with open(str(code_dir.joinpath("serve.pkl")), "rb") as f:
         buffer = f.read()
-    hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
+    hash_value = compute_hash(buffer=buffer)
     with open(str(code_dir.joinpath("metadata.json")), "wb") as metadata:
         metadata.write(_MetaData(hash_value).to_json())
-
-    return secret_key

sagemaker-serve/src/sagemaker/serve/model_server/multi_model_server/server.py

@@ -35,7 +35,6 @@ def _start_serving(
         env = {
             "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
             "SAGEMAKER_PROGRAM": "inference.py",
-            "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
             "LOCAL_PYTHON": platform.python_version(),
         }
         if env_vars:
@@ -47,7 +46,7 @@ def _start_serving(
             image,
             "serve",
             # network_mode="host",
-            ports={'8080/tcp': 8080},
+            ports={"8080/tcp": 8080},
             detach=True,
             auto_remove=True,
             volumes={
@@ -131,7 +130,6 @@ def _upload_server_artifacts(
             env_vars = {
                 "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
                 "SAGEMAKER_PROGRAM": "inference.py",
-                "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
                 "SAGEMAKER_REGION": sagemaker_session.boto_region_name,
                 "SAGEMAKER_CONTAINER_LOG_LEVEL": "10",
                 "LOCAL_PYTHON": platform.python_version(),

sagemaker-serve/src/sagemaker/serve/model_server/smd/prepare.py

@@ -12,7 +12,6 @@
 from sagemaker.serve.spec.inference_spec import InferenceSpec
 from sagemaker.serve.detector.dependency_manager import capture_dependencies
 from sagemaker.serve.validations.check_integrity import (
-    generate_secret_key,
     compute_hash,
 )
 from sagemaker.core.remote_function.core.serialization import _MetaData
@@ -64,11 +63,8 @@ def prepare_for_smd(
 
     capture_dependencies(dependencies=dependencies, work_dir=code_dir)
 
-    secret_key = generate_secret_key()
     with open(str(code_dir.joinpath("serve.pkl")), "rb") as f:
         buffer = f.read()
-    hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
+    hash_value = compute_hash(buffer=buffer)
     with open(str(code_dir.joinpath("metadata.json")), "wb") as metadata:
         metadata.write(_MetaData(hash_value).to_json())
-
-    return secret_key

sagemaker-serve/src/sagemaker/serve/model_server/smd/server.py

@@ -53,7 +53,6 @@ def _upload_smd_artifacts(
             "SAGEMAKER_INFERENCE_CODE_DIRECTORY": "/opt/ml/model/code",
             "SAGEMAKER_INFERENCE_CODE": "inference.handler",
             "SAGEMAKER_REGION": sagemaker_session.boto_region_name,
-            "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
             "LOCAL_PYTHON": platform.python_version(),
         }
         return s3_upload_path, env_vars

sagemaker-serve/src/sagemaker/serve/model_server/tei/server.py

@@ -38,8 +38,6 @@ def _start_tei_serving(
             secret_key: Secret key to use for authentication
             env_vars: Environment variables to set
         """
-        if env_vars and secret_key:
-            env_vars["SAGEMAKER_SERVE_SECRET_KEY"] = secret_key
 
         self.container = client.containers.run(
             image,

sagemaker-serve/src/sagemaker/serve/model_server/tensorflow_serving/prepare.py

@@ -11,7 +11,6 @@
 )
 from sagemaker.serve.detector.dependency_manager import capture_dependencies
 from sagemaker.serve.validations.check_integrity import (
-    generate_secret_key,
     compute_hash,
 )
 from sagemaker.core.remote_function.core.serialization import _MetaData
@@ -56,12 +55,9 @@ def prepare_for_tf_serving(
     if not mlflow_saved_model_dir:
         raise ValueError("SavedModel is not found for Tensorflow or Keras flavor.")
     _move_contents(src_dir=mlflow_saved_model_dir, dest_dir=saved_model_bundle_dir)
-
-    secret_key = generate_secret_key()
+
     with open(str(code_dir.joinpath("serve.pkl")), "rb") as f:
         buffer = f.read()
-    hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
+    hash_value = compute_hash(buffer=buffer)
     with open(str(code_dir.joinpath("metadata.json")), "wb") as metadata:
         metadata.write(_MetaData(hash_value).to_json())
-
-    return secret_key

sagemaker-serve/src/sagemaker/serve/model_server/tensorflow_serving/server.py

@@ -37,7 +37,7 @@ def _start_tensorflow_serving(
             detach=True,
             auto_remove=False,  # Temporarily disabled to see crash logs
             # network_mode="host",
-            ports={'8501/tcp': 8501},
+            ports={"8501/tcp": 8501},
             volumes={
                 Path(model_path): {
                     "bind": "/opt/ml/model",
@@ -47,7 +47,6 @@ def _start_tensorflow_serving(
             environment={
                 "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
                 "SAGEMAKER_PROGRAM": "inference.py",
-                "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
                 "LOCAL_PYTHON": platform.python_version(),
                 **env_vars,
             },
@@ -124,7 +123,6 @@ def _upload_tensorflow_serving_artifacts(
             "SAGEMAKER_PROGRAM": "inference.py",
             "SAGEMAKER_REGION": sagemaker_session.boto_region_name,
             "SAGEMAKER_CONTAINER_LOG_LEVEL": "10",
-            "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
             "LOCAL_PYTHON": platform.python_version(),
         }
         return s3_upload_path, env_vars

sagemaker-serve/src/sagemaker/serve/model_server/torchserve/prepare.py

@@ -13,7 +13,6 @@
 from sagemaker.serve.spec.inference_spec import InferenceSpec
 from sagemaker.serve.detector.dependency_manager import capture_dependencies
 from sagemaker.serve.validations.check_integrity import (
-    generate_secret_key,
     compute_hash,
 )
 from sagemaker.serve.validations.check_image_uri import is_1p_image_uri
@@ -56,7 +55,9 @@ def prepare_for_torchserve(
     # https://github.com/aws/sagemaker-python-sdk/issues/4288
     if is_1p_image_uri(image_uri=image_uri) and "xgboost" in image_uri:
         shutil.copy2(Path(__file__).parent.joinpath("xgboost_inference.py"), code_dir)
-        os.rename(str(code_dir.joinpath("xgboost_inference.py")), str(code_dir.joinpath("inference.py")))
+        os.rename(
+            str(code_dir.joinpath("xgboost_inference.py")), str(code_dir.joinpath("inference.py"))
+        )
     else:
         shutil.copy2(Path(__file__).parent.joinpath("inference.py"), code_dir)
 
@@ -67,11 +68,8 @@ def prepare_for_torchserve(
 
     capture_dependencies(dependencies=dependencies, work_dir=code_dir)
 
-    secret_key = generate_secret_key()
     with open(str(code_dir.joinpath("serve.pkl")), "rb") as f:
         buffer = f.read()
-    hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
+    hash_value = compute_hash(buffer=buffer)
     with open(str(code_dir.joinpath("metadata.json")), "wb") as metadata:
         metadata.write(_MetaData(hash_value).to_json())
-
-    return secret_key

sagemaker-serve/src/sagemaker/serve/model_server/torchserve/server.py

@@ -29,7 +29,7 @@ def _start_torch_serve(
             detach=True,
             auto_remove=True,
             # network_mode="host",
-            ports={'8080/tcp': 8080},
+            ports={"8080/tcp": 8080},
             volumes={
                 Path(model_path): {
                     "bind": "/opt/ml/model",
@@ -39,7 +39,6 @@ def _start_torch_serve(
             environment={
                 "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
                 "SAGEMAKER_PROGRAM": "inference.py",
-                "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
                 "LOCAL_PYTHON": platform.python_version(),
                 **env_vars,
             },
@@ -103,7 +102,6 @@ def _upload_torchserve_artifacts(
             "SAGEMAKER_PROGRAM": "inference.py",
             "SAGEMAKER_REGION": sagemaker_session.boto_region_name,
             "SAGEMAKER_CONTAINER_LOG_LEVEL": "10",
-            "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
             "LOCAL_PYTHON": platform.python_version(),
         }
         return s3_upload_path, env_vars

sagemaker-serve/src/sagemaker/serve/model_server/triton/model.py

@@ -26,10 +26,14 @@ def auto_complete_config(auto_complete_model_config):
     def initialize(self, args: dict) -> None:
         """Placeholder docstring"""
         serve_path = Path(TRITON_MODEL_DIR).joinpath("serve.pkl")
-        with open(str(serve_path), mode="rb") as f:
-            inference_spec, schema_builder = cloudpickle.load(f)
+        metadata_path = Path(TRITON_MODEL_DIR).joinpath("metadata.json")
 
-        # TODO: HMAC signing for integrity check
+        # Integrity check BEFORE deserialization to prevent RCE via malicious pickle
+        with open(str(serve_path), "rb") as f:
+            buffer = f.read()
+        perform_integrity_check(buffer=buffer, metadata_path=metadata_path)
+
+        inference_spec, schema_builder = cloudpickle.loads(buffer)
 
         self.inference_spec = inference_spec
         self.schema_builder = schema_builder

sagemaker-serve/src/sagemaker/serve/model_server/triton/server.py

@@ -41,7 +41,6 @@ def _start_triton_server(
         env_vars.update(
             {
                 "TRITON_MODEL_DIR": "/models/model",
-                "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
                 "LOCAL_PYTHON": platform.python_version(),
             }
         )
@@ -133,7 +132,6 @@ def _upload_triton_artifacts(
         env_vars = {
             "SAGEMAKER_TRITON_DEFAULT_MODEL_NAME": "model",
             "TRITON_MODEL_DIR": "/opt/ml/model/model",
-            "SAGEMAKER_SERVE_SECRET_KEY": secret_key,
             "LOCAL_PYTHON": platform.python_version(),
         }
         return s3_upload_path, env_vars

sagemaker-serve/src/sagemaker/serve/validations/check_integrity.py

@@ -1,29 +1,21 @@
-"""Validates the integrity of pickled file with HMAC signing."""
+"""Validates the integrity of pickled file with SHA-256 hash."""
 
 from __future__ import absolute_import
-import secrets
 import hmac
 import hashlib
-import os
 from pathlib import Path
 
 from sagemaker.core.remote_function.core.serialization import _MetaData
 
 
-def generate_secret_key(nbytes: int = 32) -> str:
-    """Generates secret key"""
-    return secrets.token_hex(nbytes)
-
-
-def compute_hash(buffer: bytes, secret_key: str) -> str:
-    """Compute hash value using HMAC"""
-    return hmac.new(secret_key.encode(), msg=buffer, digestmod=hashlib.sha256).hexdigest()
+def compute_hash(buffer: bytes) -> str:
+    """Compute SHA-256 hash of the given buffer."""
+    return hashlib.sha256(buffer).hexdigest()
 
 
 def perform_integrity_check(buffer: bytes, metadata_path: Path):
-    """Validates the integrity of bytes by comparing the hash value"""
-    secret_key = os.environ.get("SAGEMAKER_SERVE_SECRET_KEY")
-    actual_hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
+    """Validates the integrity of bytes by comparing the hash value."""
+    actual_hash_value = compute_hash(buffer=buffer)
 
     if not Path.exists(metadata_path):
         raise ValueError("Path to metadata.json does not exist")

sagemaker-serve/tests/unit/model_server/test_djl_utils.py

@@ -6,7 +6,7 @@
     _get_default_batch_size,
     _tokens_from_chars,
     _tokens_from_words,
-    _set_tokens_to_tokens_threshold
+    _set_tokens_to_tokens_threshold,
 )