Fix: Triton inference spec hmac exposure

sagemaker-serve/src/sagemaker/serve/model_builder_utils.py

@@ -131,7 +131,6 @@ def build(self):
 from sagemaker.serve.detector.pickler import save_pkl
 from sagemaker.serve.builder.requirements_manager import RequirementsManager
 from sagemaker.serve.validations.check_integrity import (
-    generate_secret_key,
     compute_hash,
 )
 from sagemaker.core.remote_function.core.serialization import _MetaData
@@ -2884,20 +2883,17 @@ def _save_inference_spec(self) -> None:
             pkl_path = Path(self.model_path).joinpath("model_repository").joinpath("model")
             save_pkl(pkl_path, (self.inference_spec, self.schema_builder))
 
-    def _hmac_signing(self):
-        """Perform HMAC signing on picke file for integrity check"""
-        secret_key = generate_secret_key()
+    def _compute_integrity_hash(self):
+        """Compute SHA-256 hash of serve.pkl and store in metadata.json for integrity check."""
         pkl_path = Path(self.model_path).joinpath("model_repository").joinpath("model")
 
         with open(str(pkl_path.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(pkl_path.joinpath("metadata.json")), "wb") as metadata:
             metadata.write(_MetaData(hash_value).to_json())
 
-        self.secret_key = secret_key
-
     def _generate_config_pbtxt(self, pkl_path: Path):
         """Generate Triton config.pbtxt file."""
         config_path = pkl_path.joinpath("config.pbtxt")
@@ -3075,7 +3071,8 @@ def _prepare_for_triton(self):
             export_path.mkdir(parents=True)
 
         if self.model:
-            self.secret_key = "dummy secret key for onnx backend"
+            # ONNX path: no pickle serialization, no serve.pkl, no integrity check needed.
+            # Do not set secret_key — there is nothing to sign.
 
             if self.framework == Framework.PYTORCH:
                 self._export_pytorch_to_onnx(
@@ -3099,7 +3096,7 @@ def _prepare_for_triton(self):
 
             self._pack_conda_env(pkl_path=pkl_path)
 
-            self._hmac_signing()
+            self._compute_integrity_hash()
 
             return
 

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 +118,10 @@ 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
+    return ""

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:
@@ -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 +63,10 @@ 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
+    return ""

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
@@ -57,11 +56,10 @@ def prepare_for_tf_serving(
         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
+    return ""

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

@@ -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
@@ -67,11 +66,10 @@ 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
+    return ""

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

@@ -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_multi_model_server_prepare.py

@@ -68,10 +68,9 @@ def test_prepare_mms_js_resources(self, mock_create_dir, mock_copy_js):
 
     @patch('builtins.input', return_value='')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.compute_hash')
-    @patch('sagemaker.serve.model_server.multi_model_server.prepare.generate_secret_key')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.capture_dependencies')
     @patch('shutil.copy2')
-    def test_prepare_for_mms_creates_structure(self, mock_copy, mock_capture, mock_gen_key, mock_hash, mock_input):
+    def test_prepare_for_mms_creates_structure(self, mock_copy, mock_capture, mock_hash, mock_input):
         """Test prepare_for_mms creates directory structure and files."""
         from sagemaker.serve.model_server.multi_model_server.prepare import prepare_for_mms
 
@@ -83,7 +82,6 @@ def test_prepare_for_mms_creates_structure(self, mock_copy, mock_capture, mock_g
         serve_pkl = code_dir / "serve.pkl"
         serve_pkl.write_bytes(b"test data")
 
-        mock_gen_key.return_value = "test-secret-key"
         mock_hash.return_value = "test-hash"
         mock_session = Mock()
         mock_inference_spec = Mock()
@@ -98,16 +96,14 @@ def test_prepare_for_mms_creates_structure(self, mock_copy, mock_capture, mock_g
                 inference_spec=mock_inference_spec
             )
 
-        self.assertEqual(secret_key, "test-secret-key")
         mock_inference_spec.prepare.assert_called_once_with(str(model_path))
         mock_capture.assert_called_once()
 
     @patch('builtins.input', return_value='')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.compute_hash')
-    @patch('sagemaker.serve.model_server.multi_model_server.prepare.generate_secret_key')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.capture_dependencies')
     @patch('shutil.copy2')
-    def test_prepare_for_mms_raises_on_invalid_dir(self, mock_copy, mock_capture, mock_gen_key, mock_hash, mock_input):
+    def test_prepare_for_mms_raises_on_invalid_dir(self, mock_copy, mock_capture, mock_hash, mock_input):
         """Test prepare_for_mms raises exception for invalid directory."""
         from sagemaker.serve.model_server.multi_model_server.prepare import prepare_for_mms
 
@@ -128,10 +124,9 @@ def test_prepare_for_mms_raises_on_invalid_dir(self, mock_copy, mock_capture, mo
 
     @patch('builtins.input', return_value='')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.compute_hash')
-    @patch('sagemaker.serve.model_server.multi_model_server.prepare.generate_secret_key')
     @patch('sagemaker.serve.model_server.multi_model_server.prepare.capture_dependencies')
     @patch('shutil.copy2')
-    def test_prepare_for_mms_copies_shared_libs(self, mock_copy, mock_capture, mock_gen_key, mock_hash, mock_input):
+    def test_prepare_for_mms_copies_shared_libs(self, mock_copy, mock_capture, mock_hash, mock_input):
         """Test prepare_for_mms copies shared libraries."""
         from sagemaker.serve.model_server.multi_model_server.prepare import prepare_for_mms
 
@@ -145,7 +140,6 @@ def test_prepare_for_mms_copies_shared_libs(self, mock_copy, mock_capture, mock_
         shared_lib = Path(self.temp_dir) / "lib.so"
         shared_lib.touch()
 
-        mock_gen_key.return_value = "test-key"
         mock_hash.return_value = "test-hash"
         mock_session = Mock()
 

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

@@ -32,8 +32,7 @@ def test_start_serving_creates_container(self, mock_path):
         self.assertEqual(server.container, mock_container)
         mock_client.containers.run.assert_called_once()
         call_kwargs = mock_client.containers.run.call_args[1]
-        self.assertIn("SAGEMAKER_SERVE_SECRET_KEY", call_kwargs["environment"])
-        self.assertEqual(call_kwargs["environment"]["SAGEMAKER_SERVE_SECRET_KEY"], "test-secret")
+        self.assertIn("SAGEMAKER_SUBMIT_DIRECTORY", call_kwargs["environment"])
 
     @patch('sagemaker.serve.model_server.multi_model_server.server.Path')
     def test_start_serving_with_no_env_vars(self, mock_path):
@@ -166,8 +165,7 @@ def test_upload_server_artifacts_uploads_to_s3(self, mock_path, mock_is_s3, mock
         )
 
         self.assertIsNotNone(model_data)
-        self.assertIn("SAGEMAKER_SERVE_SECRET_KEY", env_vars)
-        self.assertEqual(env_vars["SAGEMAKER_SERVE_SECRET_KEY"], "test-key")
+        self.assertIn("SAGEMAKER_SUBMIT_DIRECTORY", env_vars)
 
     @patch('sagemaker.serve.model_server.multi_model_server.server._is_s3_uri')
     def test_upload_server_artifacts_no_upload(self, mock_is_s3):
@@ -187,7 +185,7 @@ def test_upload_server_artifacts_no_upload(self, mock_is_s3):
         )
 
         self.assertIsNone(model_data)
-        self.assertIn("SAGEMAKER_SERVE_SECRET_KEY", env_vars)
+        self.assertIn("SAGEMAKER_SUBMIT_DIRECTORY", env_vars)
 
 
 class TestUpdateEnvVars(unittest.TestCase):