FIX: Preserve DatasetConfiguration subclass when backend overrides dataset_names

pyrit/backend/services/scenario_run_service.py

@@ -267,6 +267,16 @@ def _build_init_kwargs(
 
         Resolves strategies and dataset configuration from the request.
 
+        Dataset configuration is built so that the scenario's default
+        ``DatasetConfiguration`` *subclass* (e.g. ``EncodingDatasetConfiguration``)
+        is preserved when the caller overrides ``dataset_names`` or
+        ``max_dataset_size``. Subclasses commonly override
+        ``get_all_seed_attack_groups()`` or ``_load_seed_groups_for_dataset()``
+        to shape seeds into scenario-appropriate ``SeedAttackGroup`` objects;
+        substituting a plain ``DatasetConfiguration`` silently loses that
+        behavior and surfaces as obscure downstream errors at attack-construction
+        time (e.g. ``SeedAttackGroup must have exactly one objective``).
+
         Args:
             request: The run request.
             scenario_class: The resolved scenario class.
@@ -276,7 +286,10 @@ def _build_init_kwargs(
             Dict of kwargs to pass to scenario.initialize_async.
 
         Raises:
-            ValueError: If a strategy name is invalid for the scenario.
+            ValueError: If a strategy name is invalid for the scenario, or the
+                scenario class cannot be instantiated with no arguments when
+                introspection is required to resolve strategies or dataset
+                configuration.
         """
         init_kwargs: dict[str, Any] = {
             "objective_target": objective_target,
@@ -287,46 +300,77 @@ def _build_init_kwargs(
         if request.labels:
             init_kwargs["memory_labels"] = request.labels
 
-        # Resolve strategies and default dataset config from a temporary instance
-        # of the scenario. The downstream _initialize_scenario_async builds its
-        # own instance (so that scenario_result_id can be passed), so this is a
-        # cheap throwaway used only for introspection.
-        needs_introspection = bool(request.strategies) or (
-            request.max_dataset_size is not None and not request.dataset_names
+        # Resolve strategies and dataset config from a temporary instance of the
+        # scenario. The downstream _initialize_scenario_async builds its own
+        # instance (so scenario_result_id can be passed), so this is a cheap
+        # throwaway used only for introspection. Introspection is required
+        # whenever the caller wants to override strategies, dataset names, or
+        # the sample cap, because each of those needs the scenario's own
+        # strategy enum or dataset-config subclass to be resolved correctly.
+        needs_introspection = (
+            bool(request.strategies) or bool(request.dataset_names) or request.max_dataset_size is not None
         )
-        if needs_introspection:
-            try:
-                introspection_instance = scenario_class()  # type: ignore[ty:missing-argument]
-            except Exception as exc:
-                raise ValueError(
-                    f"Cannot resolve runtime configuration for scenario '{request.scenario_name}': "
-                    f"scenario class is not instantiable without arguments ({exc})."
-                ) from exc
-
-            if request.strategies:
-                strategy_class = introspection_instance._strategy_class
-                strategy_enums = []
-                for name in request.strategies:
-                    try:
-                        strategy_enums.append(strategy_class(name))
-                    except ValueError:
-                        available_strategies = [s.value for s in strategy_class]
-                        raise ValueError(
-                            f"Strategy '{name}' not found for scenario '{request.scenario_name}'. "
-                            f"Available: {', '.join(available_strategies)}"
-                        ) from None
-                init_kwargs["scenario_strategies"] = strategy_enums
-
-            if request.max_dataset_size is not None and not request.dataset_names:
-                default_config = introspection_instance._default_dataset_config
-                default_config.max_dataset_size = request.max_dataset_size
-                init_kwargs["dataset_config"] = default_config
+        if not needs_introspection:
+            return init_kwargs
+
+        try:
+            introspection_instance = scenario_class()  # type: ignore[ty:missing-argument]
+        except Exception as exc:
+            raise ValueError(
+                f"Cannot resolve runtime configuration for scenario '{request.scenario_name}': "
+                f"scenario class is not instantiable without arguments ({exc})."
+            ) from exc
+
+        if request.strategies:
+            strategy_class = introspection_instance._strategy_class
+            strategy_enums = []
+            for name in request.strategies:
+                try:
+                    strategy_enums.append(strategy_class(name))
+                except ValueError:
+                    available_strategies = [s.value for s in strategy_class]
+                    raise ValueError(
+                        f"Strategy '{name}' not found for scenario '{request.scenario_name}'. "
+                        f"Available: {', '.join(available_strategies)}"
+                    ) from None
+            init_kwargs["scenario_strategies"] = strategy_enums
+
+        default_config = introspection_instance._default_dataset_config
 
         if request.dataset_names:
-            init_kwargs["dataset_config"] = DatasetConfiguration(
-                dataset_names=request.dataset_names,
-                max_dataset_size=request.max_dataset_size,
-            )
+            # Construct a fresh instance of the scenario's own dataset-config
+            # class so subclass-specific behavior (e.g. EncodingDatasetConfiguration's
+            # objective-shaping in get_all_seed_attack_groups) is preserved.
+            default_config_class = type(default_config)
+            try:
+                init_kwargs["dataset_config"] = default_config_class(
+                    dataset_names=request.dataset_names,
+                    max_dataset_size=request.max_dataset_size,
+                )
+            except TypeError as exc:
+                # The subclass __init__ takes extra required kwargs we cannot
+                # supply from a backend request. Fall back to the base
+                # DatasetConfiguration so the run can still proceed; downstream
+                # scenarios that strictly require the subclass should either
+                # define a no-extra-required-args constructor or surface the
+                # incompatibility through their own initialize_async validation.
+                logger.warning(
+                    "Cannot construct %s(dataset_names=..., max_dataset_size=...) (%s). "
+                    "Falling back to a generic DatasetConfiguration; scenario-specific "
+                    "dataset-config behavior may be lost.",
+                    default_config_class.__name__,
+                    exc,
+                )
+                init_kwargs["dataset_config"] = DatasetConfiguration(
+                    dataset_names=request.dataset_names,
+                    max_dataset_size=request.max_dataset_size,
+                )
+        elif request.max_dataset_size is not None:
+            # Reuse the scenario's default dataset config (preserves subtype +
+            # the scenario's own default dataset names) and override only the
+            # sample cap. Safe because the introspection instance is throwaway.
+            default_config.max_dataset_size = request.max_dataset_size
+            init_kwargs["dataset_config"] = default_config
 
         return init_kwargs
 

tests/unit/backend/test_scenario_run_service.py

@@ -6,6 +6,7 @@
 """
 
 from datetime import datetime, timezone
+from typing import Any
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -20,6 +21,7 @@
     ScenarioRunService,
 )
 from pyrit.models import AttackOutcome
+from pyrit.scenario.core import DatasetConfiguration
 
 _REGISTRY_PATCH_BASE = "pyrit.registry"
 _MEMORY_PATCH = "pyrit.memory.CentralMemory.get_memory_instance"
@@ -279,6 +281,139 @@ async def test_start_run_max_dataset_size_uses_default_config(self, mock_all_reg
         init_call = scenario_instance.initialize_async.await_args
         assert init_call.kwargs["dataset_config"] is default_config
 
+    async def test_start_run_dataset_names_preserves_subclass_config_type(self, mock_all_registries) -> None:
+        """``dataset_names`` rebuilds the config using the scenario's own DatasetConfiguration subclass.
+
+        Regression: passing ``dataset_names`` via the backend used to construct
+        a plain ``DatasetConfiguration``, silently losing subclass behavior
+        (e.g. ``EncodingDatasetConfiguration``'s objective shaping).
+        """
+
+        # Create a marker subclass so we can verify type preservation without
+        # depending on any concrete scenario implementation.
+        class _MarkerDatasetConfiguration(DatasetConfiguration):
+            pass
+
+        default_config = _MarkerDatasetConfiguration(dataset_names=["original"], max_dataset_size=100)
+        scenario_instance = mock_all_registries["scenario_instance"]
+        scenario_instance._default_dataset_config = default_config
+
+        service = ScenarioRunService()
+        await service.start_run_async(request=_make_request(dataset_names=["custom_a", "custom_b"], max_dataset_size=3))
+
+        init_call = scenario_instance.initialize_async.await_args
+        built_config = init_call.kwargs["dataset_config"]
+
+        # Type is preserved (this is the regression assertion)
+        assert type(built_config) is _MarkerDatasetConfiguration
+        # And carries the caller-supplied values, not the scenario defaults
+        assert built_config.get_default_dataset_names() == ["custom_a", "custom_b"]
+        assert built_config.max_dataset_size == 3
+        # The original default config is not mutated when a fresh dataset_names is supplied
+        assert default_config.get_default_dataset_names() == ["original"]
+        assert default_config.max_dataset_size == 100
+
+    async def test_start_run_dataset_names_without_max_dataset_size_preserves_subclass(
+        self, mock_all_registries
+    ) -> None:
+        """``dataset_names`` alone (no ``max_dataset_size``) still preserves the subclass type."""
+
+        class _MarkerDatasetConfiguration(DatasetConfiguration):
+            pass
+
+        scenario_instance = mock_all_registries["scenario_instance"]
+        scenario_instance._default_dataset_config = _MarkerDatasetConfiguration(dataset_names=["original"])
+
+        service = ScenarioRunService()
+        await service.start_run_async(request=_make_request(dataset_names=["only_this"]))
+
+        init_call = scenario_instance.initialize_async.await_args
+        built_config = init_call.kwargs["dataset_config"]
+        assert type(built_config) is _MarkerDatasetConfiguration
+        assert built_config.get_default_dataset_names() == ["only_this"]
+        assert built_config.max_dataset_size is None
+
+    async def test_start_run_dataset_names_falls_back_when_subclass_constructor_incompatible(
+        self, mock_all_registries, caplog
+    ) -> None:
+        """If the subclass __init__ rejects standard kwargs, fall back to plain ``DatasetConfiguration``."""
+
+        class _RequiresExtraArgConfiguration(DatasetConfiguration):
+            def __init__(self, *, required_extra: str, **kwargs: Any) -> None:
+                super().__init__(**kwargs)
+                self._required_extra = required_extra
+
+        scenario_instance = mock_all_registries["scenario_instance"]
+        # Build the default with the required kwarg so introspection succeeds.
+        scenario_instance._default_dataset_config = _RequiresExtraArgConfiguration(
+            required_extra="seeded", dataset_names=["original"]
+        )
+
+        service = ScenarioRunService()
+        with caplog.at_level("WARNING", logger=_svc_mod.logger.name):
+            await service.start_run_async(request=_make_request(dataset_names=["custom"]))
+
+        init_call = scenario_instance.initialize_async.await_args
+        built_config = init_call.kwargs["dataset_config"]
+
+        # Fallback is the generic base class, not the subclass
+        assert type(built_config) is DatasetConfiguration
+        assert built_config.get_default_dataset_names() == ["custom"]
+        # Warning was logged so the operator can see the silent degradation
+        assert any(
+            "_RequiresExtraArgConfiguration" in record.message
+            and "Falling back to a generic DatasetConfiguration" in record.message
+            for record in caplog.records
+        )
+
+    async def test_start_run_dataset_names_introspection_failure_raises(self, mock_memory) -> None:
+        """Passing ``dataset_names`` against a non-no-arg-instantiable scenario fails fast."""
+        # Mirrors test_start_run_scenario_not_no_arg_instantiable_raises but for the dataset_names path.
+        mock_scenario_class = MagicMock(
+            side_effect=[
+                TypeError("missing 1 required positional argument: 'objective_target'"),
+            ]
+        )
+        mock_sr = MagicMock()
+        mock_sr.get_class.return_value = mock_scenario_class
+
+        mock_tr = MagicMock()
+        mock_tr.get_instance_by_name.return_value = MagicMock()
+        mock_tr.get_names.return_value = ["my_target"]
+
+        mock_ir = MagicMock()
+
+        service = ScenarioRunService()
+
+        with (
+            patch(f"{_REGISTRY_PATCH_BASE}.ScenarioRegistry.get_registry_singleton", return_value=mock_sr),
+            patch(f"{_REGISTRY_PATCH_BASE}.TargetRegistry.get_registry_singleton", return_value=mock_tr),
+            patch(f"{_REGISTRY_PATCH_BASE}.InitializerRegistry.get_registry_singleton", return_value=mock_ir),
+        ):
+            with pytest.raises(ValueError, match="not instantiable without arguments"):
+                await service.start_run_async(request=_make_request(dataset_names=["custom"]))
+
+    async def test_start_run_max_dataset_size_with_dataset_names_uses_subclass_with_both(
+        self, mock_all_registries
+    ) -> None:
+        """When both ``dataset_names`` and ``max_dataset_size`` are supplied, both flow into the subclass instance."""
+
+        class _MarkerDatasetConfiguration(DatasetConfiguration):
+            pass
+
+        scenario_instance = mock_all_registries["scenario_instance"]
+        scenario_instance._default_dataset_config = _MarkerDatasetConfiguration(
+            dataset_names=["original"], max_dataset_size=99
+        )
+
+        service = ScenarioRunService()
+        await service.start_run_async(request=_make_request(dataset_names=["a", "b"], max_dataset_size=7))
+
+        built_config = scenario_instance.initialize_async.await_args.kwargs["dataset_config"]
+        assert type(built_config) is _MarkerDatasetConfiguration
+        assert built_config.get_default_dataset_names() == ["a", "b"]
+        assert built_config.max_dataset_size == 7
+
     async def test_start_run_exceeds_concurrent_limit(self, mock_all_registries) -> None:
         """Test that exceeding concurrent run limit raises ValueError."""
         service = ScenarioRunService()