Merge branch 'main' into feature/python-foundry-hosted-agent-vnext

python/packages/core/agent_framework/_workflows/_checkpoint.py

@@ -244,14 +244,39 @@ class FileCheckpointStorage:
     is serialized using pickle and embedded as base64-encoded strings within the JSON. This allows
     for human-readable checkpoint files while preserving the ability to store complex Python objects.
 
-    SECURITY WARNING: Checkpoints use pickle for data serialization. Only load checkpoints
-    from trusted sources. Loading a malicious checkpoint file can execute arbitrary code.
+    By default, checkpoint deserialization is restricted to a built-in set of safe
+    Python types (primitives, datetime, uuid, ...) and all ``agent_framework``
+    internal types.  To allow additional application-specific types, pass them via
+    the ``allowed_checkpoint_types`` parameter using ``"module:qualname"`` format.
+
+    Example::
+
+        storage = FileCheckpointStorage(
+            "/tmp/checkpoints",
+            allowed_checkpoint_types=[
+                "my_app.models:MyState",
+            ],
+        )
     """
 
-    def __init__(self, storage_path: str | Path):
-        """Initialize the file storage."""
+    def __init__(
+        self,
+        storage_path: str | Path,
+        *,
+        allowed_checkpoint_types: list[str] | None = None,
+    ) -> None:
+        """Initialize the file storage.
+
+        Args:
+            storage_path: Directory path where checkpoint files will be stored.
+            allowed_checkpoint_types: Additional types (beyond the built-in safe set
+                and framework types) that are permitted during checkpoint
+                deserialization.  Each entry should be a ``"module:qualname"``
+                string (e.g., ``"my_app.models:MyState"``).
+        """
         self.storage_path = Path(storage_path)
         self.storage_path.mkdir(parents=True, exist_ok=True)
+        self._allowed_types: frozenset[str] = frozenset(allowed_checkpoint_types or [])
         logger.info(f"Initialized file checkpoint storage at {self.storage_path}")
 
     def _validate_file_path(self, checkpoint_id: CheckpointID) -> Path:
@@ -327,7 +352,7 @@ class FileCheckpointStorage:
         from ._checkpoint_encoding import decode_checkpoint_value
 
         try:
-            decoded_checkpoint_dict = decode_checkpoint_value(encoded_checkpoint)
+            decoded_checkpoint_dict = decode_checkpoint_value(encoded_checkpoint, allowed_types=self._allowed_types)
         except WorkflowCheckpointException:
             raise
         checkpoint = WorkflowCheckpoint.from_dict(decoded_checkpoint_dict)
@@ -352,7 +377,9 @@ class FileCheckpointStorage:
                         encoded_checkpoint = json.load(f)
                         from ._checkpoint_encoding import decode_checkpoint_value
 
-                        decoded_checkpoint_dict = decode_checkpoint_value(encoded_checkpoint)
+                        decoded_checkpoint_dict = decode_checkpoint_value(
+                            encoded_checkpoint, allowed_types=self._allowed_types
+                        )
                         checkpoint = WorkflowCheckpoint.from_dict(decoded_checkpoint_dict)
                     if checkpoint.workflow_name == workflow_name:
                         checkpoints.append(checkpoint)

python/packages/core/agent_framework/_workflows/_checkpoint_encoding.py

@@ -1,14 +1,5 @@
 # Copyright (c) Microsoft. All rights reserved.
 
-from __future__ import annotations
-
-import base64
-import logging
-import pickle  # nosec  # noqa: S403
-from typing import Any
-
-from ..exceptions import WorkflowCheckpointException
-
 """Checkpoint encoding using JSON structure with pickle+base64 for arbitrary data.
 
 This hybrid approach provides:
@@ -16,10 +7,23 @@ This hybrid approach provides:
 - Full Python object fidelity via pickle for data values (non-JSON-native types)
 - Base64 encoding to embed binary pickle data in JSON strings
 
-SECURITY WARNING: Checkpoints use pickle for data serialization. Only load checkpoints
-from trusted sources. Loading a malicious checkpoint file can execute arbitrary code.
+When ``allowed_types`` is supplied to :func:`decode_checkpoint_value`, a
+``RestrictedUnpickler`` is used that limits which classes may be instantiated
+during deserialization.  The default built-in safe set covers common Python
+value types (primitives, datetime, uuid, ...) and all ``agent_framework``
+internal types.  Callers can extend the set by passing additional
+``"module:qualname"`` strings.
 """
 
+from __future__ import annotations
+
+import base64
+import io
+import logging
+import pickle  # nosec  # noqa: S403
+from typing import Any
+
+from ..exceptions import WorkflowCheckpointException
 
 logger = logging.getLogger("agent_framework")
 
@@ -30,6 +34,82 @@ _TYPE_MARKER = "__type__"
 # Types that are natively JSON-serializable and don't need pickling
 _JSON_NATIVE_TYPES = (str, int, float, bool, type(None))
 
+# Module prefix for framework-internal types that are always allowed
+_FRAMEWORK_MODULE_PREFIX = "agent_framework."
+
+# Built-in types considered safe for checkpoint deserialization.
+# Each entry is a ``module:qualname`` string matching the format produced by
+# :func:`_type_to_key`.  These are the classes for which pickle's
+# ``find_class`` will be called when unpickling common Python value types.
+_BUILTIN_ALLOWED_TYPE_KEYS: frozenset[str] = frozenset({
+    # builtins
+    "builtins:object",
+    "builtins:complex",
+    "builtins:range",
+    "builtins:slice",
+    "builtins:int",
+    "builtins:float",
+    "builtins:str",
+    "builtins:bytes",
+    "builtins:bytearray",
+    "builtins:bool",
+    "builtins:set",
+    "builtins:frozenset",
+    "builtins:list",
+    "builtins:dict",
+    "builtins:tuple",
+    "builtins:type",
+    # getattr is used by pickle to reconstruct enum members
+    "builtins:getattr",
+    # copyreg helpers used by pickle for object reconstruction
+    "copyreg:_reconstructor",
+    # datetime
+    "datetime:datetime",
+    "datetime:date",
+    "datetime:time",
+    "datetime:timedelta",
+    "datetime:timezone",
+    # uuid
+    "uuid:UUID",
+    # decimal
+    "decimal:Decimal",
+    # collections
+    "collections:OrderedDict",
+    "collections:defaultdict",
+    "collections:deque",
+})
+
+
+class _RestrictedUnpickler(pickle.Unpickler):  # noqa: S301
+    """Unpickler that restricts which classes may be instantiated.
+
+    Only classes whose ``module:qualname`` key appears in the combined allow
+    set (built-in safe types + framework types + caller-specified extras) are
+    permitted.  All other classes raise :class:`pickle.UnpicklingError`.
+    """
+
+    def __init__(self, data: bytes, allowed_types: frozenset[str]) -> None:
+        super().__init__(io.BytesIO(data))
+        self._allowed_types = allowed_types
+
+    def find_class(self, module: str, name: str) -> type:
+        type_key = f"{module}:{name}"
+
+        if (
+            type_key in _BUILTIN_ALLOWED_TYPE_KEYS
+            or type_key in self._allowed_types
+            or module.startswith(_FRAMEWORK_MODULE_PREFIX)
+        ):
+            return super().find_class(module, name)  # type: ignore[no-any-return]  # nosec
+
+        raise pickle.UnpicklingError(
+            f"Checkpoint deserialization blocked for type '{type_key}'. "
+            f"To allow this type, either include its 'module:qualname' key in the "
+            f"'allowed_types' set passed to 'decode_checkpoint_value', or add it to "
+            f"'allowed_checkpoint_types' on your checkpoint storage "
+            f"(for example, 'FileCheckpointStorage.allowed_checkpoint_types')."
+        )
+
 
 def encode_checkpoint_value(value: Any) -> Any:
     """Encode a Python value for checkpoint storage.
@@ -48,29 +128,51 @@ def encode_checkpoint_value(value: Any) -> Any:
     return _encode(value)
 
 
-def decode_checkpoint_value(value: Any) -> Any:
+def decode_checkpoint_value(value: Any, *, allowed_types: frozenset[str] | None = None) -> Any:
     """Decode a value from checkpoint storage.
 
     Reverses the encoding performed by encode_checkpoint_value.
     Pickled values (identified by _PICKLE_MARKER) are decoded and unpickled.
 
-    WARNING: Only call this with trusted data. Pickle can execute
-    arbitrary code during deserialization. The post-unpickle type verification
-    detects accidental corruption or type mismatches, but cannot prevent
-    arbitrary code execution from malicious pickle payloads.
-
     Args:
         value: A JSON-deserialized value from checkpoint storage.
+        allowed_types: If not ``None``, restrict pickle deserialization to the
+            built-in safe set, framework types, and the types listed here.
+            Each entry should use ``"module:qualname"`` format — that is, the
+            dotted module path followed by a colon and the class
+            ``__qualname__``.  For example, given a user-defined class::
+
+                # my_app/models.py
+                class MyState: ...
+
+            the corresponding entry would be ``"my_app.models:MyState"``::
+
+                decode_checkpoint_value(
+                    data,
+                    allowed_types=frozenset({"my_app.models:MyState"}),
+                )
+
+            When using :class:`FileCheckpointStorage`, pass the same strings
+            via ``allowed_checkpoint_types``::
+
+                storage = FileCheckpointStorage(
+                    "/tmp/checkpoints",
+                    allowed_checkpoint_types=["my_app.models:MyState"],
+                )
+
+            If ``None``, no restriction is applied (backward-compatible
+            behavior).
 
     Returns:
         The original Python value.
 
     Raises:
         WorkflowCheckpointException: If the unpickled object's type doesn't match
-            the recorded type, indicating corruption, or if the base64/pickle
-            data is malformed.
+            the recorded type, indicating corruption, if the base64/pickle
+            data is malformed, or if a disallowed type is encountered during
+            restricted deserialization.
     """
-    return _decode(value)
+    return _decode(value, allowed_types=allowed_types)
 
 
 def _encode(value: Any) -> Any:
@@ -94,7 +196,7 @@ def _encode(value: Any) -> Any:
     }
 
 
-def _decode(value: Any) -> Any:
+def _decode(value: Any, *, allowed_types: frozenset[str] | None = None) -> Any:
     """Recursively decode a value from JSON storage."""
     # JSON-native types pass through
     if isinstance(value, _JSON_NATIVE_TYPES):
@@ -104,16 +206,16 @@ def _decode(value: Any) -> Any:
     if isinstance(value, dict):
         # Pickled value: decode, unpickle, and verify type
         if _PICKLE_MARKER in value and _TYPE_MARKER in value:
-            obj = _base64_to_unpickle(value[_PICKLE_MARKER])  # type: ignore
+            obj = _base64_to_unpickle(value[_PICKLE_MARKER], allowed_types=allowed_types)  # type: ignore
             _verify_type(obj, value.get(_TYPE_MARKER))  # type: ignore
             return obj
 
         # Regular dict: decode values recursively
-        return {k: _decode(v) for k, v in value.items()}  # type: ignore
+        return {k: _decode(v, allowed_types=allowed_types) for k, v in value.items()}  # type: ignore
 
     # Handle encoded lists
     if isinstance(value, list):
-        return [_decode(item) for item in value]  # type: ignore
+        return [_decode(item, allowed_types=allowed_types) for item in value]  # type: ignore
 
     return value
 
@@ -148,15 +250,23 @@ def _pickle_to_base64(value: Any) -> str:
     return base64.b64encode(pickled).decode("ascii")
 
 
-def _base64_to_unpickle(encoded: str) -> Any:
+def _base64_to_unpickle(encoded: str, *, allowed_types: frozenset[str] | None = None) -> Any:
     """Decode base64 string and unpickle.
 
+    Args:
+        encoded: Base64-encoded pickle data.
+        allowed_types: If not ``None``, use restricted unpickling that only
+            permits built-in safe types, framework types, and the specified
+            extra types.
+
     Raises:
-        WorkflowCheckpointException: If the base64 data is corrupted or the pickle
-            format is incompatible.
+        WorkflowCheckpointException: If the base64 data is corrupted, the pickle
+            format is incompatible, or a disallowed type is encountered.
     """
     try:
         pickled = base64.b64decode(encoded.encode("ascii"))
+        if allowed_types is not None:
+            return _RestrictedUnpickler(pickled, allowed_types).load()
         return pickle.loads(pickled)  # nosec  # noqa: S301
     except Exception as exc:
         raise WorkflowCheckpointException(f"Failed to decode pickled checkpoint data: {exc}") from exc

python/packages/core/pyproject.toml

@@ -4,7 +4,7 @@ description = "Microsoft Agent Framework for building AI Agents with Python. Thi
 authors = [{ name = "Microsoft", email = "af-support@microsoft.com"}]
 readme = "README.md"
 requires-python = ">=3.10"
-version = "1.0.0"
+version = "1.0.1"
 license-files = ["LICENSE"]
 urls.homepage = "https://aka.ms/agent-framework"
 urls.source = "https://github.com/microsoft/agent-framework/tree/main/python"

python/packages/core/tests/workflow/test_checkpoint.py

@@ -1048,7 +1048,10 @@ async def test_file_checkpoint_storage_roundtrip_datetime():
 async def test_file_checkpoint_storage_roundtrip_dataclass():
     """Test that dataclass objects roundtrip correctly via pickle encoding."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=["tests.workflow.test_checkpoint:_TestCustomData"],
+        )
 
         custom_obj = _TestCustomData(name="test", value=42, tags=["a", "b", "c"])
 
@@ -1238,7 +1241,10 @@ async def test_file_checkpoint_storage_roundtrip_messages_with_complex_data():
 async def test_file_checkpoint_storage_roundtrip_pending_request_info_events():
     """Test that pending_request_info_events with WorkflowEvent objects roundtrip correctly."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=["tests.workflow.test_checkpoint:_TestToolApprovalRequest"],
+        )
 
         # Create request_info events using the proper WorkflowEvent factory
         event1 = WorkflowEvent.request_info(
@@ -1300,7 +1306,13 @@ async def test_file_checkpoint_storage_roundtrip_pending_request_info_events():
 async def test_file_checkpoint_storage_roundtrip_full_checkpoint():
     """Test complete WorkflowCheckpoint roundtrip with all fields populated using proper types."""
     with tempfile.TemporaryDirectory() as temp_dir:
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=[
+                "tests.workflow.test_checkpoint:_TestApprovalRequest",
+                "tests.workflow.test_checkpoint:_TestExecutorState",
+            ],
+        )
 
         # Create proper WorkflowMessage objects
         msg1 = WorkflowMessage(data="msg1", source_id="s", target_id="t")

python/packages/core/tests/workflow/test_checkpoint_unrestricted_pickle.py

@@ -0,0 +1,218 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Tests for restricted checkpoint deserialization.
+
+These tests verify that persisted checkpoint loading uses a restricted
+unpickler by default:
+- Arbitrary callables are blocked during deserialization
+- __reduce__ payloads cannot execute code during deserialization
+- FileCheckpointStorage accepts allowed_checkpoint_types for extension
+- User-defined types are blocked unless explicitly allowed
+- Built-in safe types and framework types are always allowed
+"""
+
+import base64
+import os
+import pickle
+import tempfile
+from dataclasses import dataclass
+from datetime import datetime, timezone
+
+import pytest
+
+from agent_framework import WorkflowCheckpointException
+from agent_framework._workflows._checkpoint import FileCheckpointStorage
+from agent_framework._workflows._checkpoint_encoding import (
+    _PICKLE_MARKER,
+    _TYPE_MARKER,
+    decode_checkpoint_value,
+    encode_checkpoint_value,
+)
+
+
+class MaliciousPayload:
+    """A class whose __reduce__ executes code during unpickling."""
+
+    def __reduce__(self):
+        return (os.getpid, ())
+
+
+def test_restricted_decode_blocks_arbitrary_callable():
+    """Restricted decoding blocks arbitrary module-level callables."""
+    pickled = pickle.dumps(os.getpid, protocol=pickle.HIGHEST_PROTOCOL)
+    encoded_b64 = base64.b64encode(pickled).decode("ascii")
+
+    checkpoint_value = {
+        _PICKLE_MARKER: encoded_b64,
+        _TYPE_MARKER: "builtins:builtin_function_or_method",
+    }
+
+    with pytest.raises(WorkflowCheckpointException, match="deserialization blocked"):
+        decode_checkpoint_value(checkpoint_value, allowed_types=frozenset())
+
+
+def test_restricted_decode_blocks_reduce_payload():
+    """__reduce__-based payloads are blocked before code can execute."""
+    payload = MaliciousPayload()
+    pickled = pickle.dumps(payload, protocol=pickle.HIGHEST_PROTOCOL)
+    encoded_b64 = base64.b64encode(pickled).decode("ascii")
+
+    checkpoint_value = {
+        _PICKLE_MARKER: encoded_b64,
+        _TYPE_MARKER: f"{MaliciousPayload.__module__}:{MaliciousPayload.__qualname__}",
+    }
+
+    with pytest.raises(WorkflowCheckpointException, match="deserialization blocked"):
+        decode_checkpoint_value(checkpoint_value, allowed_types=frozenset())
+
+
+def test_restricted_decode_prevents_code_execution():
+    """Restricted deserialization prevents __reduce__ code from running."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        marker_file = os.path.join(tmpdir, "checkpoint_test_marker")
+
+        payload_bytes = pickle.dumps(
+            type(
+                "Exploit",
+                (),
+                {
+                    "__reduce__": lambda self: (
+                        eval,
+                        (f"open({marker_file!r}, 'w').write('pwned')",),
+                    )
+                },
+            )(),
+            protocol=pickle.HIGHEST_PROTOCOL,
+        )
+        encoded_b64 = base64.b64encode(payload_bytes).decode("ascii")
+
+        checkpoint_value = {
+            _PICKLE_MARKER: encoded_b64,
+            _TYPE_MARKER: "builtins:int",
+        }
+        with pytest.raises(WorkflowCheckpointException, match="deserialization blocked"):
+            decode_checkpoint_value(checkpoint_value, allowed_types=frozenset())
+
+        assert not os.path.exists(marker_file), (
+            "Restricted unpickler should have prevented code execution, but the marker file was created."
+        )
+
+
+def test_file_checkpoint_storage_accepts_allowed_types():
+    """FileCheckpointStorage.__init__ accepts allowed_checkpoint_types."""
+    with tempfile.TemporaryDirectory() as tmpdir:
+        storage = FileCheckpointStorage(
+            tmpdir,
+            allowed_checkpoint_types=["some.module:SomeType"],
+        )
+        assert storage is not None
+
+
+@dataclass
+class _AllowedTestState:
+    """Test dataclass that will be explicitly allowed."""
+
+    name: str
+    value: int
+
+
+def test_restricted_decode_blocks_unlisted_user_type():
+    """User-defined types are blocked when not in allowed_checkpoint_types."""
+    original = _AllowedTestState(name="test", value=42)
+    encoded = encode_checkpoint_value(original)
+
+    with pytest.raises(WorkflowCheckpointException, match="deserialization blocked"):
+        decode_checkpoint_value(encoded, allowed_types=frozenset())
+
+
+def test_restricted_decode_allows_listed_user_type():
+    """User-defined types are allowed when listed in allowed_types."""
+    original = _AllowedTestState(name="test", value=42)
+    encoded = encode_checkpoint_value(original)
+
+    type_key = f"{_AllowedTestState.__module__}:{_AllowedTestState.__qualname__}"
+    decoded = decode_checkpoint_value(encoded, allowed_types=frozenset({type_key}))
+
+    assert isinstance(decoded, _AllowedTestState)
+    assert decoded.name == "test"
+    assert decoded.value == 42
+
+
+def test_restricted_decode_allows_builtin_safe_types():
+    """Built-in safe types (datetime, set, etc.) are always allowed."""
+    test_values = [
+        datetime(2025, 1, 1, tzinfo=timezone.utc),
+        {1, 2, 3},
+        frozenset({4, 5, 6}),
+        (1, "two", 3.0),
+        complex(1, 2),
+    ]
+    for original in test_values:
+        encoded = encode_checkpoint_value(original)
+        decoded = decode_checkpoint_value(encoded, allowed_types=frozenset())
+        assert decoded == original
+
+
+def test_unrestricted_decode_allows_arbitrary_types():
+    """Without allowed_types, decode_checkpoint_value remains unrestricted."""
+    original = _AllowedTestState(name="test", value=42)
+    encoded = encode_checkpoint_value(original)
+
+    decoded = decode_checkpoint_value(encoded)
+
+    assert isinstance(decoded, _AllowedTestState)
+    assert decoded.name == "test"
+
+
+async def test_file_storage_blocks_unlisted_user_type():
+    """FileCheckpointStorage blocks user types not in allowed_checkpoint_types."""
+    from agent_framework import WorkflowCheckpoint
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Save with a storage that allows the type
+        type_key = f"{_AllowedTestState.__module__}:{_AllowedTestState.__qualname__}"
+        save_storage = FileCheckpointStorage(tmpdir, allowed_checkpoint_types=[type_key])
+
+        checkpoint = WorkflowCheckpoint(
+            workflow_name="test",
+            graph_signature_hash="hash",
+            state={"data": _AllowedTestState(name="test", value=1)},
+        )
+        await save_storage.save(checkpoint)
+
+        # Load with a storage that does NOT allow the type
+        load_storage = FileCheckpointStorage(tmpdir)
+        with pytest.raises(WorkflowCheckpointException, match="deserialization blocked"):
+            await load_storage.load(checkpoint.checkpoint_id)
+
+
+async def test_file_storage_allows_listed_user_type():
+    """FileCheckpointStorage allows user types listed in allowed_checkpoint_types."""
+    from agent_framework import WorkflowCheckpoint
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        type_key = f"{_AllowedTestState.__module__}:{_AllowedTestState.__qualname__}"
+        storage = FileCheckpointStorage(tmpdir, allowed_checkpoint_types=[type_key])
+
+        checkpoint = WorkflowCheckpoint(
+            workflow_name="test",
+            graph_signature_hash="hash",
+            state={"data": _AllowedTestState(name="allowed", value=99)},
+        )
+        await storage.save(checkpoint)
+        loaded = await storage.load(checkpoint.checkpoint_id)
+
+        assert isinstance(loaded.state["data"], _AllowedTestState)
+        assert loaded.state["data"].name == "allowed"
+        assert loaded.state["data"].value == 99
+
+
+def test_restricted_unpickler_raises_pickle_error():
+    """_RestrictedUnpickler.find_class raises pickle.UnpicklingError, not a framework exception."""
+    from agent_framework._workflows._checkpoint_encoding import _RestrictedUnpickler
+
+    pickled = pickle.dumps(os.getpid, protocol=pickle.HIGHEST_PROTOCOL)
+
+    unpickler = _RestrictedUnpickler(pickled, frozenset())
+    with pytest.raises(pickle.UnpicklingError, match="deserialization blocked"):
+        unpickler.load()

python/packages/core/tests/workflow/test_request_info_event_rehydrate.py

@@ -130,7 +130,17 @@ async def test_checkpoint_with_pending_request_info_events():
 
     with tempfile.TemporaryDirectory() as temp_dir:
         # Use file-based storage to test full serialization
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=[
+                "tests.workflow.test_request_info_and_response:UserApprovalRequest",
+                "tests.workflow.test_request_info_and_response:CalculationRequest",
+                "tests.workflow.test_request_info_event_rehydrate:MockRequest",
+                "tests.workflow.test_request_info_event_rehydrate:SimpleApproval",
+                "tests.workflow.test_request_info_event_rehydrate:SlottedApproval",
+                "tests.workflow.test_request_info_event_rehydrate:TimedApproval",
+            ],
+        )
 
         # Create workflow with checkpointing enabled
         executor = ApprovalRequiredExecutor(id="approval_executor")
@@ -225,7 +235,17 @@ async def test_checkpoint_restore_with_responses_does_not_reemit_handled_request
 
     with tempfile.TemporaryDirectory() as temp_dir:
         # Use file-based storage to test full serialization
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=[
+                "tests.workflow.test_request_info_and_response:UserApprovalRequest",
+                "tests.workflow.test_request_info_and_response:CalculationRequest",
+                "tests.workflow.test_request_info_event_rehydrate:MockRequest",
+                "tests.workflow.test_request_info_event_rehydrate:SimpleApproval",
+                "tests.workflow.test_request_info_event_rehydrate:SlottedApproval",
+                "tests.workflow.test_request_info_event_rehydrate:TimedApproval",
+            ],
+        )
 
         # Create workflow with checkpointing enabled
         executor = ApprovalRequiredExecutor(id="approval_executor")
@@ -288,7 +308,17 @@ async def test_checkpoint_restore_with_partial_responses_reemits_unhandled_reque
     import tempfile
 
     with tempfile.TemporaryDirectory() as temp_dir:
-        storage = FileCheckpointStorage(temp_dir)
+        storage = FileCheckpointStorage(
+            temp_dir,
+            allowed_checkpoint_types=[
+                "tests.workflow.test_request_info_and_response:UserApprovalRequest",
+                "tests.workflow.test_request_info_and_response:CalculationRequest",
+                "tests.workflow.test_request_info_event_rehydrate:MockRequest",
+                "tests.workflow.test_request_info_event_rehydrate:SimpleApproval",
+                "tests.workflow.test_request_info_event_rehydrate:SlottedApproval",
+                "tests.workflow.test_request_info_event_rehydrate:TimedApproval",
+            ],
+        )
 
         # Create workflow with multiple requests
         executor = MultiRequestExecutor(id="multi_executor")