litellm/tests/test_litellm/integrations/test_custom_guardrail.py

import asyncio
from unittest.mock import AsyncMock

import pytest

from litellm.integrations.custom_guardrail import (
    DEFAULT_ADVISORY_MESSAGE,
    CustomGuardrail,
    log_guardrail_information,
)
from litellm.proxy._types import CallTypes, UserAPIKeyAuth
from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailTracingDetail


class TestCustomGuardrailDeploymentHook:

    @pytest.mark.asyncio
    async def test_async_pre_call_deployment_hook_no_guardrails(self):
        """Test that method returns kwargs unchanged when no guardrails are present"""
        custom_guardrail = CustomGuardrail()

        # Test with guardrails as None
        kwargs = {
            "messages": [{"role": "user", "content": "test message"}],
            "model": "gpt-3.5-turbo",
            "guardrails": None,
        }

        result = await custom_guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        assert result == kwargs

        # Test with guardrails as non-list
        kwargs["guardrails"] = "not_a_list"

        result = await custom_guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        assert result == kwargs

    @pytest.mark.asyncio
    async def test_async_pre_call_deployment_hook_with_guardrails_and_message_update(
        self,
    ):
        """Test that method processes guardrails and updates messages when result contains messages"""
        custom_guardrail = CustomGuardrail()

        # Mock the async_pre_call_hook method
        mock_result = {"messages": [{"role": "user", "content": "filtered message"}]}
        custom_guardrail.async_pre_call_hook = AsyncMock(return_value=mock_result)

        original_messages = [{"role": "user", "content": "original message"}]
        kwargs = {
            "messages": original_messages,
            "model": "gpt-3.5-turbo",
            "guardrails": ["some_guardrail"],
            "user_api_key_user_id": "test_user",
            "user_api_key_team_id": "test_team",
            "user_api_key_end_user_id": "test_end_user",
            "user_api_key_hash": "test_hash",
            "user_api_key_request_route": "test_route",
        }

        result = await custom_guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        # Verify async_pre_call_hook was called with correct parameters
        custom_guardrail.async_pre_call_hook.assert_called_once()
        call_args = custom_guardrail.async_pre_call_hook.call_args

        # Check that UserAPIKeyAuth was created properly
        user_api_key_dict = call_args[1]["user_api_key_dict"]
        assert isinstance(user_api_key_dict, UserAPIKeyAuth)
        assert user_api_key_dict.user_id == "test_user"
        assert user_api_key_dict.team_id == "test_team"
        assert user_api_key_dict.end_user_id == "test_end_user"
        assert user_api_key_dict.api_key == "test_hash"
        assert user_api_key_dict.request_route == "test_route"

        # Check other parameters
        assert call_args[1]["data"] == kwargs
        assert call_args[1]["call_type"] == "completion"

        # Verify messages were updated in result
        assert result["messages"] == mock_result["messages"]
        assert result["messages"] != original_messages

    @pytest.mark.asyncio
    async def test_deployment_hook_skips_when_pre_call_already_ran(self):
        """The deployment hook must not re-run async_pre_call_hook once the proxy
        pre-call loop has already run it for this request."""

        class CountingGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(guardrail_name="g1", default_on=True)
                self.pre_call_count = 0

            async def async_pre_call_hook(
                self, user_api_key_dict, cache, data, call_type
            ):
                self.pre_call_count += 1
                return data

        guardrail = CountingGuardrail()
        kwargs = {
            "messages": [{"role": "user", "content": "hi"}],
            "model": "gpt-3.5-turbo",
            "guardrails": ["g1"],
            "metadata": {},
        }

        guardrail.mark_pre_call_hook_ran(kwargs)
        await guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        assert guardrail.pre_call_count == 0

    @pytest.mark.asyncio
    async def test_deployment_hook_runs_when_not_marked(self):
        """Without the proxy marker (direct-SDK usage) the deployment hook is the
        only execution path and must still run the guardrail exactly once."""

        class CountingGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(guardrail_name="g1", default_on=True)
                self.pre_call_count = 0

            async def async_pre_call_hook(
                self, user_api_key_dict, cache, data, call_type
            ):
                self.pre_call_count += 1
                return data

        guardrail = CountingGuardrail()
        kwargs = {
            "messages": [{"role": "user", "content": "hi"}],
            "model": "gpt-3.5-turbo",
            "guardrails": ["g1"],
            "metadata": {},
        }

        await guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        assert guardrail.pre_call_count == 1

    def test_mark_pre_call_hook_ran_uses_litellm_metadata(self):
        """The marker is recorded in litellm_metadata when that is the metadata
        bucket in use, and is then visible to the skip check."""
        from litellm.constants import PRE_CALL_EXECUTED_GUARDRAILS_KEY

        guardrail = CustomGuardrail(guardrail_name="g1")
        kwargs = {"litellm_metadata": {}}

        guardrail.mark_pre_call_hook_ran(kwargs)

        assert kwargs["litellm_metadata"][PRE_CALL_EXECUTED_GUARDRAILS_KEY]
        assert guardrail._pre_call_hook_already_ran(kwargs) is True

    @pytest.mark.asyncio
    async def test_deployment_hook_ignores_forged_caller_marker(self):
        """A direct-SDK caller controls request metadata but cannot know the
        per-process token, so a hand-crafted marker must not suppress a
        requested guardrail in async_pre_call_deployment_hook."""
        from litellm.constants import PRE_CALL_EXECUTED_GUARDRAILS_KEY

        class CountingGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(guardrail_name="g1", default_on=True)
                self.pre_call_count = 0

            async def async_pre_call_hook(
                self, user_api_key_dict, cache, data, call_type
            ):
                self.pre_call_count += 1
                return data

        guardrail = CountingGuardrail()
        kwargs = {
            "messages": [{"role": "user", "content": "hi"}],
            "model": "gpt-3.5-turbo",
            "guardrails": ["g1"],
            "metadata": {PRE_CALL_EXECUTED_GUARDRAILS_KEY: ["g1"]},
        }

        await guardrail.async_pre_call_deployment_hook(
            kwargs=kwargs, call_type=CallTypes.completion
        )

        assert guardrail.pre_call_count == 1


class TestCustomGuardrailShouldRunGuardrail:

    def test_should_run_guardrail_with_litellm_metadata(self):
        """Test that should_run_guardrail works with litellm_metadata pattern"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            default_on=False,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test with guardrails in litellm_metadata
        data = {
            "model": "gpt-3.5-turbo",
            "litellm_metadata": {"guardrails": ["test_guardrail"]},
        }

        result = custom_guardrail.should_run_guardrail(
            data=data, event_type=GuardrailEventHooks.pre_call
        )

        assert result is True

    def test_should_run_guardrail_with_metadata(self):
        """Test that should_run_guardrail works with metadata pattern"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            default_on=False,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test with guardrails in metadata
        data = {
            "model": "gpt-3.5-turbo",
            "metadata": {"guardrails": ["test_guardrail"]},
        }

        result = custom_guardrail.should_run_guardrail(
            data=data, event_type=GuardrailEventHooks.pre_call
        )

        assert result is True

    def test_should_run_guardrail_with_root_level_guardrails(self):
        """Test that should_run_guardrail works with root level guardrails"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            default_on=False,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test with guardrails at root level
        data = {"model": "gpt-3.5-turbo", "guardrails": ["test_guardrail"]}

        result = custom_guardrail.should_run_guardrail(
            data=data, event_type=GuardrailEventHooks.pre_call
        )

        assert result is True

    def test_should_run_guardrail_no_matching_guardrail(self):
        """Test that should_run_guardrail returns False when guardrail name doesn't match"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            default_on=False,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test with different guardrail name
        data = {
            "model": "gpt-3.5-turbo",
            "litellm_metadata": {"guardrails": ["different_guardrail"]},
        }

        result = custom_guardrail.should_run_guardrail(
            data=data, event_type=GuardrailEventHooks.pre_call
        )

        assert result is False

    def test_should_run_guardrail_with_disable_global_guardrail(self):
        """Test that disable_global_guardrails only works from admin metadata"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="global_guardrail",
            default_on=True,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test 1: Global guardrail runs by default
        data = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
        }
        result = custom_guardrail.should_run_guardrail(
            data=data, event_type=GuardrailEventHooks.pre_call
        )
        assert result is True, "Global guardrail should run when default_on=True"

        # Test 2: User-injected disable at root level is IGNORED
        data_with_disable_root = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "disable_global_guardrails": True,
        }
        result = custom_guardrail.should_run_guardrail(
            data=data_with_disable_root, event_type=GuardrailEventHooks.pre_call
        )
        assert (
            result is True
        ), "User-injected disable_global_guardrails should be ignored"

        # Test 3: User-injected disable in metadata is IGNORED
        data_with_disable_metadata = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"disable_global_guardrails": True},
        }
        result = custom_guardrail.should_run_guardrail(
            data=data_with_disable_metadata, event_type=GuardrailEventHooks.pre_call
        )
        assert result is True, "User-injected metadata disable should be ignored"

        # Test 4: Admin-configured disable via user_api_key_metadata IS respected
        data_with_admin_disable = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"user_api_key_metadata": {"disable_global_guardrails": True}},
        }
        result = custom_guardrail.should_run_guardrail(
            data=data_with_admin_disable, event_type=GuardrailEventHooks.pre_call
        )
        assert result is False, "Admin-configured disable should be respected"

        # Test 5: Admin config in metadata isn't shadowed by user-supplied litellm_metadata
        data_cross_key = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"user_api_key_metadata": {"disable_global_guardrails": True}},
            "litellm_metadata": {"request_tags": ["user-supplied"]},
        }
        result = custom_guardrail.should_run_guardrail(
            data=data_cross_key, event_type=GuardrailEventHooks.pre_call
        )
        assert (
            result is False
        ), "Admin config in metadata must not be shadowed by user-supplied litellm_metadata"

        # Test 6: After the pre-call strip runs, user-injected
        # user_api_key_metadata in the non-authoritative metadata key is gone.
        # _get_admin_metadata must then surface admin config unchanged.
        data_post_strip = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"user_api_key_metadata": {"disable_global_guardrails": True}},
            "litellm_metadata": {},  # post-strip: attacker payload removed
        }
        result = custom_guardrail.should_run_guardrail(
            data=data_post_strip, event_type=GuardrailEventHooks.pre_call
        )
        assert (
            result is False
        ), "Admin config in metadata must be respected when other metadata key is empty"

    def test_should_run_guardrail_key_disable_global_not_overruled_by_team_guardrail_list(
        self,
    ):
        """Key disable_global_guardrails must take precedence over the guardrail
        appearing in the team's explicit guardrails list."""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="global_guardrail",
            default_on=True,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Key disabled globals; team added the same guardrail to its explicit list
        # (simulates what _add_guardrails_from_key_or_team_metadata produces).
        data_key_disabled_team_listed = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {
                "user_api_key_metadata": {"disable_global_guardrails": True},
                "guardrails": ["global_guardrail"],
            },
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_key_disabled_team_listed,
                event_type=GuardrailEventHooks.pre_call,
            )
            is False
        ), "Key disable_global_guardrails must win over team's explicit guardrail list"

        # Complementary: key NOT disabled, team added guardrail → should run
        data_key_enabled_team_listed = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {
                "user_api_key_metadata": {},
                "guardrails": ["global_guardrail"],
            },
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_key_enabled_team_listed,
                event_type=GuardrailEventHooks.pre_call,
            )
            is True
        ), "Guardrail in team's explicit list should run when key has not disabled globals"

    def test_should_run_guardrail_with_opted_out_global_guardrails(self):
        """Test that per-guardrail opt-out only works from admin metadata"""
        from litellm.types.guardrails import GuardrailEventHooks

        custom_guardrail = CustomGuardrail(
            guardrail_name="global_guardrail",
            default_on=True,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # Test 1: User-injected opt-out at root level is IGNORED
        data_root = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "opted_out_global_guardrails": ["global_guardrail"],
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_root, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )

        # Test 2: User-injected opt-out in metadata is IGNORED
        data_metadata = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"opted_out_global_guardrails": ["global_guardrail"]},
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_metadata, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )

        # Test 4: a different guardrail in the opt-out list → still runs
        data_other = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"opted_out_global_guardrails": ["some_other_guardrail"]},
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_other, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )

        # Test 5: empty opt-out list → still runs
        data_empty = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"opted_out_global_guardrails": []},
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_empty, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )

        # Test 6: malformed value (bool instead of list) → safely ignored, guardrail runs
        data_malformed = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {"opted_out_global_guardrails": True},
        }
        assert (
            custom_guardrail.should_run_guardrail(
                data=data_malformed, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )

    def test_should_run_guardrail_opt_out_does_not_affect_non_global(self):
        """Opt-out list only matters for default_on=True guardrails"""
        from litellm.types.guardrails import GuardrailEventHooks

        non_global = CustomGuardrail(
            guardrail_name="opt_in_guardrail",
            default_on=False,
            event_hook=GuardrailEventHooks.pre_call,
        )

        # An opt-in guardrail named in opted_out_global_guardrails is still controlled
        # by the explicit `guardrails` request list, not by the global opt-out list.
        data = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "user", "content": "test"}],
            "metadata": {
                "opted_out_global_guardrails": ["opt_in_guardrail"],
                "guardrails": ["opt_in_guardrail"],
            },
        }
        assert (
            non_global.should_run_guardrail(
                data=data, event_type=GuardrailEventHooks.pre_call
            )
            is True
        )


class TestApplyGuardrailCheck:
    def test_apply_guardrail_check_only_on_direct_implementation(self):
        """
        Test that "apply_guardrail" in type(callback).__dict__ only returns True
        when the object's own class implements the method, not when it's inherited
        from a parent class.

        This is critical for properly routing guardrail handling to the unified
        guardrail handler vs the guardrail's own implementation.
        """

        # Parent class with apply_guardrail (CustomGuardrail already has it)
        class ParentGuardrail(CustomGuardrail):
            """Parent that inherits apply_guardrail from CustomGuardrail"""

            pass

        # Child class that only inherits apply_guardrail (doesn't override)
        class ChildGuardrailWithoutOverride(ParentGuardrail):
            """Child that only inherits apply_guardrail"""

            pass

        # Child class that overrides apply_guardrail
        class ChildGuardrailWithOverride(ParentGuardrail):
            """Child that overrides apply_guardrail"""

            async def apply_guardrail(self, text, language=None, entities=None):
                return f"modified: {text}"

        # Instantiate the classes
        parent_instance = ParentGuardrail()
        child_without_override = ChildGuardrailWithoutOverride()
        child_with_override = ChildGuardrailWithOverride()

        # Test: CustomGuardrail itself has apply_guardrail in its __dict__
        assert (
            "apply_guardrail" in type(CustomGuardrail()).__dict__
        ), "CustomGuardrail should have apply_guardrail in its own __dict__"

        # Test: ParentGuardrail inherits but doesn't override, so it should NOT be in __dict__
        assert (
            "apply_guardrail" not in type(parent_instance).__dict__
        ), "ParentGuardrail should NOT have apply_guardrail in its own __dict__ (only inherited)"

        # Test: ChildGuardrailWithoutOverride only inherits, should NOT be in __dict__
        assert (
            "apply_guardrail" not in type(child_without_override).__dict__
        ), "ChildGuardrailWithoutOverride should NOT have apply_guardrail in its own __dict__ (only inherited)"

        # Test: ChildGuardrailWithOverride overrides the method, SHOULD be in __dict__
        assert (
            "apply_guardrail" in type(child_with_override).__dict__
        ), "ChildGuardrailWithOverride SHOULD have apply_guardrail in its own __dict__ (overridden)"

        # Verify that all instances still have the method via inheritance (hasattr)
        assert hasattr(
            parent_instance, "apply_guardrail"
        ), "All instances should have apply_guardrail via inheritance"
        assert hasattr(
            child_without_override, "apply_guardrail"
        ), "All instances should have apply_guardrail via inheritance"
        assert hasattr(
            child_with_override, "apply_guardrail"
        ), "All instances should have apply_guardrail via inheritance"


class TestGuardrailLoggingAggregation:
    def _make_guardrail(self):
        from litellm.types.guardrails import GuardrailEventHooks

        return CustomGuardrail(
            guardrail_name="test_guardrail",
            event_hook=GuardrailEventHooks.pre_call,
        )

    def _invoke_add_log(self, request_data: dict) -> None:
        guardrail = self._make_guardrail()
        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={"result": "ok"},
            request_data=request_data,
            guardrail_status="success",
            start_time=1.0,
            end_time=2.0,
            duration=1.0,
            masked_entity_count={"EMAIL": 1},
            guardrail_provider="presidio",
        )

    def test_appends_to_existing_metadata_list(self):
        request_data = {
            "metadata": {
                "standard_logging_guardrail_information": [
                    {"guardrail_name": "existing_guardrail"}
                ]
            }
        }

        self._invoke_add_log(request_data)

        info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert isinstance(info, list)
        assert len(info) == 2
        assert info[0]["guardrail_name"] == "existing_guardrail"
        assert info[1]["guardrail_name"] == "test_guardrail"

    def test_converts_existing_metadata_dict_to_list(self):
        request_data = {
            "metadata": {
                "standard_logging_guardrail_information": {"guardrail_name": "legacy"}
            }
        }

        self._invoke_add_log(request_data)

        info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert isinstance(info, list)
        assert len(info) == 2
        assert info[0]["guardrail_name"] == "legacy"
        assert info[1]["guardrail_name"] == "test_guardrail"

    def test_appends_to_litellm_metadata(self):
        request_data = {
            "litellm_metadata": {
                "standard_logging_guardrail_information": [
                    {"guardrail_name": "litellm_existing"}
                ]
            }
        }

        self._invoke_add_log(request_data)

        info = request_data["litellm_metadata"][
            "standard_logging_guardrail_information"
        ]
        assert isinstance(info, list)
        assert len(info) == 2
        assert info[1]["guardrail_name"] == "test_guardrail"

    def test_caller_metadata_does_not_divert_the_entry_from_the_reader(self):
        """A caller-supplied `metadata` field must not send the entry to a bucket the
        spend log never reads. Routes in LITELLM_METADATA_ROUTES (/v1/messages,
        /v1/responses, batches, files) seed `litellm_metadata`, and Claude Code sends
        `metadata.user_id`, so both keys are present on the same request."""
        request_data = {
            "metadata": {"user_id": "device-account-session"},
            "litellm_metadata": {"user_api_key_hash": "abc"},
        }

        self._invoke_add_log(request_data)

        assert (
            "standard_logging_guardrail_information" not in request_data["metadata"]
        ), "entry landed in the caller's metadata, where the spend log does not read it"
        info = request_data["litellm_metadata"][
            "standard_logging_guardrail_information"
        ]
        assert len(info) == 1
        assert info[0]["guardrail_name"] == "test_guardrail"

    def test_entry_and_applied_guardrails_header_share_one_bucket(self):
        """The x-litellm-applied-guardrails writer and the guardrail-info writer must
        resolve the same bucket, otherwise the response header and the spend log
        disagree about whether the guardrail ran."""
        from litellm.proxy.common_utils.callback_utils import (
            add_guardrail_to_applied_guardrails_header,
        )

        request_data = {
            "metadata": {"user_id": "device-account-session"},
            "litellm_metadata": {},
        }

        self._invoke_add_log(request_data)
        add_guardrail_to_applied_guardrails_header(
            request_data=request_data, guardrail_name="test_guardrail"
        )

        buckets = {
            key
            for key in ("metadata", "litellm_metadata")
            for field in ("standard_logging_guardrail_information", "applied_guardrails")
            if field in request_data[key]
        }
        assert buckets == {"litellm_metadata"}


class TestGuardrailOtelSpanEmission:
    """Recording a guardrail emits its otel span inline, so every guardrail
    execution produces a span — including the pass-through allow path that never
    reaches a post-call hook."""

    def _make_guardrail(self):
        from litellm.types.guardrails import GuardrailEventHooks

        return CustomGuardrail(
            guardrail_name="emit_guard",
            event_hook=GuardrailEventHooks.pre_call,
        )

    def _record(self, guardrail, request_data):
        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={"result": "ok"},
            request_data=request_data,
            guardrail_status="success",
            start_time=1.0,
            end_time=2.0,
            duration=1.0,
        )

    def test_emits_span_for_recorded_entry(self, monkeypatch):
        captured = []
        monkeypatch.setattr(
            "litellm.integrations.otel.logger.emit_guardrail_span",
            captured.append,
        )

        request_data = {"metadata": {}}
        self._record(self._make_guardrail(), request_data)

        assert len(captured) == 1
        emitted = captured[0]
        recorded = request_data["metadata"]["standard_logging_guardrail_information"][
            -1
        ]
        assert emitted is recorded
        assert emitted["guardrail_name"] == "emit_guard"
        assert emitted["start_time"] == 1.0
        assert emitted["end_time"] == 2.0

    def test_span_emission_failure_does_not_break_recording(self, monkeypatch):
        def _boom(_entry):
            raise RuntimeError("otel exporter down")

        monkeypatch.setattr(
            "litellm.integrations.otel.logger.emit_guardrail_span", _boom
        )

        request_data = {"metadata": {}}
        self._record(self._make_guardrail(), request_data)

        info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(info) == 1
        assert info[0]["guardrail_name"] == "emit_guard"


class TestGuardrailSensitiveFieldStripping:
    """Tests that secret_fields is stripped from guardrail responses before logging.

    Matches the pattern used by Langfuse and Arize integrations which also
    pop("secret_fields") to prevent raw Authorization headers from being persisted.
    """

    def _make_guardrail(self):
        from litellm.types.guardrails import GuardrailEventHooks

        return CustomGuardrail(
            guardrail_name="test_guardrail",
            event_hook=GuardrailEventHooks.pre_call,
        )

    def test_secret_fields_stripped_from_guardrail_response(self):
        """Ensure secret_fields (containing raw Authorization headers) is not persisted."""
        guardrail = self._make_guardrail()
        request_data = {"metadata": {}}

        guardrail_response_with_secrets = {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "hello"}],
            "secret_fields": {
                "raw_headers": {
                    "authorization": "Bearer sk-live-secret-key-12345",
                    "content-type": "application/json",
                }
            },
            "proxy_server_request": {"url": "http://localhost:4000/chat/completions"},
        }

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response=guardrail_response_with_secrets,
            request_data=request_data,
            guardrail_status="success",
            duration=1.0,
        )

        info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(info) == 1
        logged_response = info[0]["guardrail_response"]

        # secret_fields must be stripped
        assert "secret_fields" not in logged_response

        # Other fields should be preserved
        assert "model" in logged_response
        assert "messages" in logged_response
        assert "proxy_server_request" in logged_response

    def test_string_guardrail_response_not_affected(self):
        """String responses (e.g. 'allow', 'deny') should pass through unchanged."""
        guardrail = self._make_guardrail()
        request_data = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response="allow",
            request_data=request_data,
            guardrail_status="success",
            duration=0.5,
        )

        info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert info[0]["guardrail_response"] == "allow"

    def test_no_authorization_header_in_logged_response(self):
        """Verify no plaintext Authorization header ends up in the logged guardrail response."""
        import json

        guardrail = self._make_guardrail()
        request_data = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "model": "gpt-4",
                "secret_fields": {
                    "raw_headers": {
                        "authorization": "Bearer sk-live-SHOULD-NOT-APPEAR",
                    }
                },
            },
            request_data=request_data,
            guardrail_status="success",
            duration=1.0,
        )

        logged_response = request_data["metadata"][
            "standard_logging_guardrail_information"
        ][0]["guardrail_response"]
        assert "secret_fields" not in logged_response
        assert "sk-live-SHOULD-NOT-APPEAR" not in json.dumps(logged_response)

    def test_secret_fields_stripped_from_list_dict_response(self):
        """Ensure secret_fields is stripped from List[dict] guardrail responses too."""
        guardrail = self._make_guardrail()
        request_data = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response=[
                {
                    "result": "ok",
                    "secret_fields": {
                        "raw_headers": {"authorization": "Bearer sk-secret"}
                    },
                },
                {"result": "also_ok"},
            ],
            request_data=request_data,
            guardrail_status="success",
            duration=1.0,
        )

        import json

        serialized = json.dumps(request_data)
        assert "secret_fields" not in serialized
        assert "sk-secret" not in serialized


class TestGuardrailResponseCredentialMasking:
    """LIT-4314 issue B regression: credentials embedded in guardrail_response
    (via team callback_vars flowing through data["metadata"]) must be masked at
    the construction seam so every downstream sink (SpendLogs, OTel, Langfuse,
    custom loggers) sees masked values rather than plaintext.
    """

    def _make_guardrail(self):
        from litellm.types.guardrails import GuardrailEventHooks

        return CustomGuardrail(
            guardrail_name="test_guardrail",
            event_hook=GuardrailEventHooks.pre_call,
        )

    def test_callback_vars_api_key_is_masked(self):
        import json

        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}
        plaintext_key = "lsv2_pt_abcdef1234567890"

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "model": "gpt-4o-mini",
                "messages": [{"role": "user", "content": "hi"}],
                "metadata_snapshot": {
                    "callback_vars": {
                        "langsmith_api_key": plaintext_key,
                        "langsmith_project": "proj-name",
                    }
                },
            },
            request_data=request_data,
            guardrail_status="success",
            duration=1.0,
        )

        logged = request_data["metadata"]["standard_logging_guardrail_information"][0][
            "guardrail_response"
        ]

        masked_key = logged["metadata_snapshot"]["callback_vars"]["langsmith_api_key"]
        assert masked_key != plaintext_key
        assert "*" in masked_key
        assert plaintext_key not in json.dumps(request_data)

        assert logged["model"] == "gpt-4o-mini"
        assert logged["messages"] == [{"role": "user", "content": "hi"}]
        assert (
            logged["metadata_snapshot"]["callback_vars"]["langsmith_project"]
            == "proj-name"
        )

    def test_nested_user_api_key_auth_metadata_is_masked(self):
        import json

        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}
        token_value = "1b01552f6e52e0d41963dd6a185bd6b074624e330999534ca7ff5adfdf622dfc"

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "evaluated_metadata": {
                    "user_api_key_auth": {
                        "token": token_value,
                        "api_key": token_value,
                        "metadata": {
                            "callback_vars": {
                                "langsmith_api_key": "lsv2_pt_super_secret_value_1234",
                            }
                        },
                    }
                }
            },
            request_data=request_data,
            guardrail_status="success",
        )

        serialized = json.dumps(request_data)
        assert token_value not in serialized
        assert "lsv2_pt_super_secret_value_1234" not in serialized

    def test_secret_fields_pop_still_runs(self):
        import json

        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "model": "gpt-4",
                "secret_fields": {
                    "raw_headers": {
                        "authorization": "Bearer sk-live-should-not-appear",
                    }
                },
            },
            request_data=request_data,
            guardrail_status="success",
        )

        serialized = json.dumps(request_data)
        assert "secret_fields" not in serialized
        assert "sk-live-should-not-appear" not in serialized

    def test_match_and_regex_redaction_still_runs(self):
        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "filters": [{"regex": r"\d{3}-\d{2}-\d{4}", "action": "BLOCKED"}]
            },
            request_data=request_data,
            guardrail_status="success",
        )

        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg["guardrail_response"]["filters"][0]["regex"] == "[REDACTED]"

    def test_scalar_types_pass_through_unchanged(self):
        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "flagged": True,
                "score": 0.94,
                "tokens_used": 42,
                "categories": ["pii", "toxicity"],
            },
            request_data=request_data,
            guardrail_status="success",
        )

        logged = request_data["metadata"]["standard_logging_guardrail_information"][0][
            "guardrail_response"
        ]
        assert logged["flagged"] is True
        assert logged["score"] == 0.94
        assert logged["tokens_used"] == 42
        assert logged["categories"] == ["pii", "toxicity"]

    def test_masking_reveals_prefix_and_suffix(self):
        guardrail = self._make_guardrail()
        request_data: dict = {"metadata": {}}
        plaintext = "lsv2_pt_abcdef1234567890"

        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={
                "metadata_snapshot": {
                    "callback_vars": {"langsmith_api_key": plaintext}
                }
            },
            request_data=request_data,
            guardrail_status="success",
        )

        masked = request_data["metadata"]["standard_logging_guardrail_information"][0][
            "guardrail_response"
        ]["metadata_snapshot"]["callback_vars"]["langsmith_api_key"]
        assert masked != plaintext
        assert masked.startswith(plaintext[:4])
        assert masked.endswith(plaintext[-4:])


class TestCustomGuardrailPassthroughSupport:
    """Tests for passthrough endpoint guardrail support - Issue fixes."""

    @pytest.mark.asyncio
    async def test_async_post_call_success_deployment_hook_with_httpx_response(self):
        """
        Test that async_post_call_success_deployment_hook handles raw httpx.Response objects
        from passthrough endpoints without crashing with TypeError.

        This tests Fix #3: TypeError: TypedDict does not support instance and class checks
        """
        import httpx

        custom_guardrail = CustomGuardrail()

        # Mock the async_post_call_success_hook to return None (guardrail didn't modify response)
        custom_guardrail.async_post_call_success_hook = AsyncMock(return_value=None)

        # Create a mock httpx.Response object (typical passthrough response)
        mock_response = AsyncMock(spec=httpx.Response)
        mock_response.status_code = 200
        mock_response.text = "Mock response"

        request_data = {
            "guardrails": ["test_guardrail"],
            "user_api_key_user_id": "test_user",
            "user_api_key_team_id": "test_team",
            "user_api_key_end_user_id": "test_end_user",
            "user_api_key_hash": "test_hash",
            "user_api_key_request_route": "passthrough_route",
        }

        # This should not raise TypeError: TypedDict does not support instance and class checks
        result = await custom_guardrail.async_post_call_success_deployment_hook(
            request_data=request_data,
            response=mock_response,
            call_type=CallTypes.allm_passthrough_route,
        )

        # When result is None, should return the original response
        assert result == mock_response

    @pytest.mark.asyncio
    async def test_async_post_call_success_deployment_hook_with_none_call_type(self):
        """
        Test that async_post_call_success_deployment_hook handles None call_type gracefully.

        This ensures that even if call_type is None (before fix #1), the guardrail doesn't crash.
        """
        custom_guardrail = CustomGuardrail()

        # Mock the async_post_call_success_hook to return None
        custom_guardrail.async_post_call_success_hook = AsyncMock(return_value=None)

        mock_response = AsyncMock()

        request_data = {
            "guardrails": ["test_guardrail"],
            "user_api_key_user_id": "test_user",
        }

        # Call with None call_type - should not crash
        result = await custom_guardrail.async_post_call_success_deployment_hook(
            request_data=request_data,
            response=mock_response,
            call_type=None,
        )

        # Should return the original response when result is None
        assert result == mock_response

    def test_is_valid_response_type_with_none(self):
        """
        Test _is_valid_response_type helper method correctly identifies None as invalid.

        This is part of Fix #3: Safely handling TypedDict types that don't support isinstance checks.
        """
        custom_guardrail = CustomGuardrail()

        # None should be invalid
        assert custom_guardrail._is_valid_response_type(None) is False

    def test_is_valid_response_type_with_typeddict_error(self):
        """
        Test _is_valid_response_type gracefully handles TypeError from TypedDict.

        This tests Fix #3: When isinstance() is called with TypedDict types, it raises TypeError.
        The method should catch this and allow the response through.
        """
        from litellm.types.utils import ModelResponse

        custom_guardrail = CustomGuardrail()

        # Create a valid LiteLLM response object
        response = ModelResponse(
            id="test-id",
            choices=[],
            created=0,
            model="test-model",
            object="chat.completion",
        )

        # This should return True (it's a valid response type or TypeError is caught)
        result = custom_guardrail._is_valid_response_type(response)
        assert result is True


class TestInjectAdvisoryMessage:
    """
    Tests for CustomGuardrail.inject_advisory_message: the shared, guardrail-agnostic
    "advisory" flagged-content strategy (append a note, let the LLM decide) that sits
    alongside raise_passthrough_exception (short-circuit with a canned message).
    """

    def test_appends_to_empty_messages_list(self):
        guardrail = CustomGuardrail()
        data = {"model": "gpt-5-mini"}

        guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert data["messages"] == [{"role": "system", "content": "This looks suspicious."}]

    def test_appends_to_existing_messages_list(self):
        guardrail = CustomGuardrail()
        original_messages = [{"role": "user", "content": "Hello"}]
        data = {"model": "gpt-5-mini", "messages": list(original_messages)}

        guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert data["messages"] == original_messages + [{"role": "system", "content": "This looks suspicious."}]

    def test_does_not_mutate_other_data_keys(self):
        guardrail = CustomGuardrail()
        data = {"model": "gpt-5-mini", "metadata": {"user_id": "abc"}, "temperature": 0.5}

        guardrail.inject_advisory_message(data, "Advisory note.")

        assert data["model"] == "gpt-5-mini"
        assert data["metadata"] == {"user_id": "abc"}
        assert data["temperature"] == 0.5

    def test_works_on_bare_customguardrail_not_just_lakera(self):
        """Proves genericity: this is a CustomGuardrail method, not Lakera-specific."""

        class SomeOtherGuardrail(CustomGuardrail):
            pass

        guardrail = SomeOtherGuardrail(guardrail_name="some_other_guardrail")
        data = {"messages": [{"role": "user", "content": "hi"}]}

        guardrail.inject_advisory_message(data, DEFAULT_ADVISORY_MESSAGE.format(reason="a content safety concern"))

        assert len(data["messages"]) == 2

    def test_appends_to_responses_api_input_string(self):
        """
        The Responses API stores its content in "input", not "messages". Appending
        only to "messages" would leave the advisory unreachable for that endpoint,
        since the Responses backend never reads a "messages" key.
        """
        guardrail = CustomGuardrail()
        data = {"model": "gpt-5-mini", "input": "What's the weather today?"}

        guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert data["input"] == "What's the weather today?\n\nThis looks suspicious."
        assert "messages" not in data

    def test_appends_to_both_messages_and_input_when_both_present(self):
        guardrail = CustomGuardrail()
        data = {"messages": [{"role": "user", "content": "hi"}], "input": "hi"}

        guardrail.inject_advisory_message(data, "Advisory note.")

        assert data["messages"][-1] == {"role": "system", "content": "Advisory note."}
        assert data["input"] == "hi\n\nAdvisory note."

    def test_prefers_instructions_over_input_for_responses_api(self):
        """
        Veria-ai finding on BerriAI/litellm#34940: "instructions" is the
        privileged, developer-set Responses-API field; "input" is caller-
        controlled and a caller could include text telling the model to
        disregard a trailing warning appended there instead. The advisory
        must land in "instructions" whenever it's present, not "input".
        """
        guardrail = CustomGuardrail()
        data = {"instructions": "You are a helpful assistant.", "input": "hi"}

        guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert data["instructions"] == "You are a helpful assistant.\n\nThis looks suspicious."
        assert data["input"] == "hi"

    def test_prefers_instructions_over_structured_input_for_responses_api(self):
        guardrail = CustomGuardrail()
        structured_input = [{"role": "user", "content": [{"type": "input_text", "text": "hi"}]}]
        data = {"instructions": "You are a helpful assistant.", "input": list(structured_input)}

        delivered = guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert delivered is True
        assert data["instructions"] == "You are a helpful assistant.\n\nThis looks suspicious."
        assert data["input"] == structured_input

    def test_returns_true_when_delivered_to_messages_or_input(self):
        guardrail = CustomGuardrail()
        assert guardrail.inject_advisory_message({"messages": []}, "note") is True
        assert guardrail.inject_advisory_message({"input": "hi"}, "note") is True
        assert guardrail.inject_advisory_message({"model": "gpt-5-mini"}, "note") is True

    def test_returns_false_and_does_not_mutate_structured_responses_api_input(self):
        """
        A structured Responses-API input (a list of input items, not a plain
        string) with no "messages" key has no field this helper can safely
        append into -- adding a "messages" key would be inert, since the
        Responses backend reads only "input". The caller must be able to tell
        this happened so it can degrade to blocking instead of silently
        letting the flagged request through with no advisory delivered.
        """
        guardrail = CustomGuardrail()
        structured_input = [{"role": "user", "content": [{"type": "input_text", "text": "hi"}]}]
        data = {"model": "gpt-5-mini", "input": list(structured_input)}

        delivered = guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert delivered is False
        assert data["input"] == structured_input
        assert "messages" not in data

    def test_returns_false_and_does_not_mutate_when_messages_also_present_alongside_structured_input(self):
        """
        Bugbot finding on BerriAI/litellm#34940: a request can carry both a
        "messages" list and a structured Responses-API "input" list at the
        same time (the raw request body is passed through largely unvalidated).
        The Responses backend reads only "input" in that shape, so a "messages"
        list being present too must not make this return True -- appending
        there is exactly as inert as when "messages" is absent, and previously
        this returned True (and mutated "messages") purely because a
        "messages" list happened to exist, silently letting a flagged request
        through advisory mode believed it had delivered a note the model never saw.
        """
        guardrail = CustomGuardrail()
        structured_input = [{"role": "user", "content": [{"type": "input_text", "text": "hi"}]}]
        original_messages = [{"role": "user", "content": "hi"}]
        data = {"model": "gpt-5-mini", "messages": list(original_messages), "input": list(structured_input)}

        delivered = guardrail.inject_advisory_message(data, "This looks suspicious.")

        assert delivered is False
        assert data["input"] == structured_input
        assert data["messages"] == original_messages


class TestEventTypeLogging:
    """Tests for event_type logging in guardrail information."""

    @pytest.mark.asyncio
    async def test_log_guardrail_information_infers_event_type_from_async_pre_call_hook(
        self,
    ):
        """
        Test that log_guardrail_information decorator correctly infers GuardrailEventHooks.pre_call
        from async_pre_call_hook function name.
        """
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="test_event_type_guardrail",
                    event_hook=[
                        GuardrailEventHooks.pre_call,
                        GuardrailEventHooks.post_call,
                    ],
                )

            @log_guardrail_information
            async def async_pre_call_hook(self, data: dict, **kwargs):
                return {"result": "pre_call_executed"}

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.async_pre_call_hook(data=request_data)

        # Check that the guardrail_mode was set to pre_call (not the full list)
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.pre_call

    @pytest.mark.asyncio
    async def test_log_guardrail_information_infers_event_type_from_async_post_call_success_hook(
        self,
    ):
        """
        Test that log_guardrail_information decorator correctly infers GuardrailEventHooks.post_call
        from async_post_call_success_hook function name.
        """
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="test_event_type_guardrail",
                    event_hook=[
                        GuardrailEventHooks.pre_call,
                        GuardrailEventHooks.post_call,
                    ],
                )

            @log_guardrail_information
            async def async_post_call_success_hook(self, data: dict, **kwargs):
                return {"result": "post_call_executed"}

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.async_post_call_success_hook(data=request_data)

        # Check that the guardrail_mode was set to post_call (not the full list)
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.post_call

    @pytest.mark.asyncio
    async def test_log_guardrail_information_infers_event_type_from_async_moderation_hook(
        self,
    ):
        """
        Test that log_guardrail_information decorator correctly infers GuardrailEventHooks.during_call
        from async_moderation_hook function name.
        """
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="test_event_type_guardrail",
                    event_hook=[
                        GuardrailEventHooks.during_call,
                        GuardrailEventHooks.post_call,
                    ],
                )

            @log_guardrail_information
            async def async_moderation_hook(self, data: dict, **kwargs):
                return {"result": "moderation_executed"}

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.async_moderation_hook(data=request_data)

        # Check that the guardrail_mode was set to during_call (not the full list)
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.during_call

    @pytest.mark.asyncio
    async def test_log_guardrail_information_infers_event_type_from_async_post_call_streaming_hook(
        self,
    ):
        """
        Test that log_guardrail_information decorator correctly infers GuardrailEventHooks.post_call
        from async_post_call_streaming_hook function name.
        """
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="test_event_type_guardrail",
                    event_hook=[
                        GuardrailEventHooks.pre_call,
                        GuardrailEventHooks.post_call,
                    ],
                )

            @log_guardrail_information
            async def async_post_call_streaming_hook(self, data: dict, **kwargs):
                return {"result": "streaming_executed"}

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.async_post_call_streaming_hook(data=request_data)

        # Check that the guardrail_mode was set to post_call (not the full list)
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.post_call

    @pytest.mark.asyncio
    async def test_log_guardrail_information_returns_none_for_unknown_function_name(
        self,
    ):
        """
        Test that log_guardrail_information decorator returns None for event_type
        when function name doesn't match known patterns, and falls back to self.event_hook.
        """
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="test_event_type_guardrail",
                    event_hook=GuardrailEventHooks.pre_call,
                )

            @log_guardrail_information
            async def some_other_hook(self, data: dict, **kwargs):
                return {"result": "other_hook_executed"}

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.some_other_hook(data=request_data)

        # Check that the guardrail_mode falls back to self.event_hook
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.pre_call

    def test_add_standard_logging_uses_event_type_over_event_hook(self):
        """
        Test that add_standard_logging_guardrail_information_to_request_data
        prioritizes event_type parameter over self.event_hook.
        """
        from litellm.types.guardrails import GuardrailEventHooks

        guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            event_hook=[GuardrailEventHooks.pre_call, GuardrailEventHooks.post_call],
        )

        request_data = {"metadata": {}}

        # Call with explicit event_type
        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={"result": "ok"},
            request_data=request_data,
            guardrail_status="success",
            event_type=GuardrailEventHooks.post_call,
        )

        # Should use the provided event_type (post_call), not the full event_hook list
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.post_call

    @pytest.mark.asyncio
    async def test_log_guardrail_information_skips_auto_record_if_function_already_recorded(
        self,
    ):
        """When a wrapped guardrail function records its own entry directly
        (e.g. block_code_execution.apply_guardrail records a rich
        ``[detections...]`` payload), the decorator must NOT also append its
        own ``"allow"``/raw-response entry — otherwise every backend
        (OTEL spans, Datadog, Langfuse, spend logs) double-records one
        logical guardrail invocation."""
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="block-code",
                    event_hook=GuardrailEventHooks.pre_call,
                )

            @log_guardrail_information
            async def apply_guardrail(self, inputs, request_data, **kwargs):
                self.add_standard_logging_guardrail_information_to_request_data(
                    guardrail_json_response=[{"action_taken": "block"}],
                    request_data=request_data,
                    guardrail_status="success",
                    event_type=GuardrailEventHooks.pre_call,
                )
                return inputs

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        await guardrail.apply_guardrail(
            inputs={"texts": ["x"]}, request_data=request_data
        )

        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1, (
            f"Decorator must not double-record when the wrapped function "
            f"already appended its own entry; got {len(logged_info)} entries"
        )
        assert logged_info[0]["guardrail_response"] == [{"action_taken": "block"}]

    @pytest.mark.asyncio
    async def test_log_guardrail_information_skips_auto_record_on_exception_if_function_already_recorded(
        self,
    ):
        """Same as above on the failure path: if the wrapped function
        appended an entry in its ``finally`` block before re-raising, the
        decorator must just re-raise without auto-recording on top."""
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class TestGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="block-code",
                    event_hook=GuardrailEventHooks.pre_call,
                )

            @log_guardrail_information
            async def apply_guardrail(self, inputs, request_data, **kwargs):
                try:
                    raise ValueError("blocked")
                finally:
                    self.add_standard_logging_guardrail_information_to_request_data(
                        guardrail_json_response=[{"action_taken": "block"}],
                        request_data=request_data,
                        guardrail_status="guardrail_intervened",
                        event_type=GuardrailEventHooks.pre_call,
                    )

        guardrail = TestGuardrail()
        request_data = {"metadata": {}}

        with pytest.raises(ValueError, match="blocked"):
            await guardrail.apply_guardrail(
                inputs={"texts": ["x"]}, request_data=request_data
            )

        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_status"] == "guardrail_intervened"

    @pytest.mark.asyncio
    async def test_log_guardrail_information_records_every_concurrent_guardrail(self):
        """Guardrails run concurrently (parallel pre_call/post_call, during_call) share one
        request_data dict. Each must still record its own entry. The previous guard counted
        entries in that shared dict, so a sibling's append made a guardrail think it had already
        recorded and skip its own auto-record — silently dropping lifecycle logs the UI shows."""
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class SleeperGuardrail(CustomGuardrail):
            def __init__(self, name, sleep):
                super().__init__(guardrail_name=name, event_hook=GuardrailEventHooks.pre_call)
                self._sleep = sleep

            @log_guardrail_information
            async def async_pre_call_hook(self, data: dict, **kwargs):
                await asyncio.sleep(self._sleep)
                return data

        request_data = {"metadata": {}}
        # Different sleeps guarantee overlapping execution windows: the faster guardrail
        # records while the slower one is still awaiting, which is exactly what tripped the
        # old shared-count guard.
        await asyncio.gather(
            SleeperGuardrail("guardrail-a", 0.05).async_pre_call_hook(data=request_data),
            SleeperGuardrail("guardrail-b", 0.15).async_pre_call_hook(data=request_data),
        )

        logged = request_data["metadata"]["standard_logging_guardrail_information"]
        assert {entry["guardrail_name"] for entry in logged} == {"guardrail-a", "guardrail-b"}
        assert len(logged) == 2

    def test_add_standard_logging_falls_back_to_event_hook_when_event_type_is_none(
        self,
    ):
        """
        Test that add_standard_logging_guardrail_information_to_request_data
        falls back to self.event_hook when event_type is None.
        """
        from litellm.types.guardrails import GuardrailEventHooks

        guardrail = CustomGuardrail(
            guardrail_name="test_guardrail",
            event_hook=GuardrailEventHooks.pre_call,
        )

        request_data = {"metadata": {}}

        # Call with event_type=None
        guardrail.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={"result": "ok"},
            request_data=request_data,
            guardrail_status="success",
            event_type=None,
        )

        # Should fall back to self.event_hook
        logged_info = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(logged_info) == 1
        assert logged_info[0]["guardrail_mode"] == GuardrailEventHooks.pre_call


class TestTracingFieldsPopulation:
    """Verify add_standard_logging_guardrail_information_to_request_data passes tracing_detail fields."""

    def test_new_fields_set_on_slg(self):
        cg = CustomGuardrail(guardrail_name="test-rail")
        request_data = {"metadata": {}}
        cg.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response={"result": "ok"},
            request_data=request_data,
            guardrail_status="success",
            tracing_detail=GuardrailTracingDetail(
                guardrail_id="rail-123",
                policy_template="EU AI Act Article 5",
                detection_method="regex",
                confidence_score=0.95,
                match_details=[{"type": "pattern", "action_taken": "BLOCK"}],
                patterns_checked=12,
                alert_recipients=["admin@example.com"],
            ),
        )
        slg_list = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(slg_list) == 1
        slg = slg_list[0]
        assert slg["guardrail_id"] == "rail-123"
        assert slg["policy_template"] == "EU AI Act Article 5"
        assert slg["detection_method"] == "regex"
        assert slg["confidence_score"] == 0.95
        assert slg["patterns_checked"] == 12
        assert slg["alert_recipients"] == ["admin@example.com"]
        assert len(slg["match_details"]) == 1

    def test_new_fields_default_to_absent(self):
        """When tracing_detail is not passed, new fields are absent from the SLG dict."""
        cg = CustomGuardrail(guardrail_name="test-rail")
        request_data = {"metadata": {}}
        cg.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response="ok",
            request_data=request_data,
            guardrail_status="success",
        )
        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg.get("guardrail_id") is None
        assert slg.get("policy_template") is None
        assert slg.get("confidence_score") is None

    def test_multiple_guardrails_with_different_policies(self):
        """One request, multiple guardrails each with own policy_template."""
        cg1 = CustomGuardrail(guardrail_name="rail-1")
        cg2 = CustomGuardrail(guardrail_name="rail-2")
        request_data = {"metadata": {}}

        cg1.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response="ok",
            request_data=request_data,
            guardrail_status="success",
            tracing_detail=GuardrailTracingDetail(policy_template="GDPR"),
        )
        cg2.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response="blocked",
            request_data=request_data,
            guardrail_status="guardrail_intervened",
            tracing_detail=GuardrailTracingDetail(
                policy_template="EU AI Act Article 5"
            ),
        )

        slg_list = request_data["metadata"]["standard_logging_guardrail_information"]
        assert len(slg_list) == 2
        assert slg_list[0]["policy_template"] == "GDPR"
        assert slg_list[1]["policy_template"] == "EU AI Act Article 5"

    def test_classification_field_passed_through(self):
        """Classification dict for LLM-judge guardrails is passed through."""
        cg = CustomGuardrail(guardrail_name="judge-rail")
        request_data = {"metadata": {}}
        classification = {
            "flagged": True,
            "category": "workplace_emotion_recognition",
            "article_reference": "Article 5(1)(f)",
            "confidence": 0.94,
            "reason": "Request asks to analyze employee sentiment",
        }
        cg.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response="blocked",
            request_data=request_data,
            guardrail_status="guardrail_intervened",
            tracing_detail=GuardrailTracingDetail(
                classification=classification,
                detection_method="llm-judge",
                confidence_score=0.94,
            ),
        )
        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg["classification"] == classification
        assert slg["detection_method"] == "llm-judge"
        assert slg["confidence_score"] == 0.94


class TestCustomGuardrailSpendLogMatchRedaction:
    """Guardrail JSON persisted via standard_logging must not contain raw match spans."""

    def test_add_standard_logging_redacts_nested_match(self):
        cg = CustomGuardrail(guardrail_name="test-rail")
        raw = {
            "assessments": [
                {
                    "sensitiveInformationPolicy": {
                        "piiEntities": [
                            {"type": "NAME", "match": "GG", "action": "BLOCKED"}
                        ]
                    }
                }
            ]
        }
        request_data: dict = {"metadata": {}}
        cg.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response=raw,
            request_data=request_data,
            guardrail_status="guardrail_intervened",
        )
        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert (
            slg["guardrail_response"]["assessments"][0]["sensitiveInformationPolicy"][
                "piiEntities"
            ][0]["match"]
            == "[REDACTED]"
        )
        assert (
            raw["assessments"][0]["sensitiveInformationPolicy"]["piiEntities"][0][
                "match"
            ]
            == "GG"
        )

    def test_add_standard_logging_redacts_regex_field(self):
        cg = CustomGuardrail(guardrail_name="test-rail")
        raw = {"filters": [{"regex": r"\d{3}-\d{2}-\d{4}", "action": "BLOCKED"}]}
        request_data: dict = {"metadata": {}}
        cg.add_standard_logging_guardrail_information_to_request_data(
            guardrail_json_response=raw,
            request_data=request_data,
            guardrail_status="success",
        )
        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg["guardrail_response"]["filters"][0]["regex"] == "[REDACTED]"
        assert raw["filters"][0]["regex"] == r"\d{3}-\d{2}-\d{4}"


class TestGuardrailInterventionClassification:
    """A routing decision is a deliberate guardrail intervention, not a failure."""

    def test_sensitive_data_route_exception_is_intervention(self):
        from litellm.exceptions import SensitiveDataRouteException

        exc = SensitiveDataRouteException(
            route_to_model="on-prem-model",
            session_id="sess-1",
            guardrail_name="pii-rail",
        )
        assert CustomGuardrail._is_guardrail_intervention(exc) is True

    @pytest.mark.parametrize("status_code", [400, 403, 422])
    def test_block_signalling_http_exception_is_intervention(self, status_code):
        from fastapi.exceptions import HTTPException

        exc = HTTPException(status_code=status_code, detail="blocked by guardrail")
        assert CustomGuardrail._is_guardrail_intervention(exc) is True

    @pytest.mark.parametrize("status_code", [300, 401, 408, 429, 451, 499, 500, 502, 503])
    def test_non_block_http_exception_is_not_intervention(self, status_code):
        from fastapi.exceptions import HTTPException

        exc = HTTPException(status_code=status_code, detail="guardrail api error")
        assert CustomGuardrail._is_guardrail_intervention(exc) is False

    @pytest.mark.asyncio
    async def test_non_400_4xx_logged_as_intervened_not_failed(self):
        from fastapi.exceptions import HTTPException

        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class BlockingGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="block-rail",
                    event_hook=GuardrailEventHooks.pre_call,
                )

            @log_guardrail_information
            async def async_pre_call_hook(self, data, **kwargs):
                raise HTTPException(status_code=403, detail="blocked by guardrail")

        guardrail = BlockingGuardrail()
        request_data: dict = {"metadata": {}}

        with pytest.raises(HTTPException):
            await guardrail.async_pre_call_hook(data=request_data)

        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg["guardrail_status"] == "guardrail_intervened"

    @pytest.mark.asyncio
    async def test_routing_logged_as_intervened_not_failed(self):
        from litellm.exceptions import SensitiveDataRouteException
        from litellm.integrations.custom_guardrail import log_guardrail_information
        from litellm.types.guardrails import GuardrailEventHooks

        class RoutingGuardrail(CustomGuardrail):
            def __init__(self):
                super().__init__(
                    guardrail_name="pii-rail",
                    event_hook=GuardrailEventHooks.pre_call,
                )

            @log_guardrail_information
            async def async_pre_call_hook(self, data, **kwargs):
                raise SensitiveDataRouteException(
                    route_to_model="on-prem-model",
                    session_id="sess-1",
                    guardrail_name=self.guardrail_name,
                )

        guardrail = RoutingGuardrail()
        request_data: dict = {"metadata": {}}

        with pytest.raises(SensitiveDataRouteException):
            await guardrail.async_pre_call_hook(data=request_data)

        slg = request_data["metadata"]["standard_logging_guardrail_information"][0]
        assert slg["guardrail_status"] == "guardrail_intervened"


class _ApplyStyleGuardrail(CustomGuardrail):
    """Overrides only apply_guardrail, like openai_moderation; async_pre_call_hook stays the CustomLogger no-op."""

    def __init__(self, block: bool):
        from litellm.types.guardrails import GuardrailEventHooks

        super().__init__(
            guardrail_name="apply-style-guardrail",
            event_hook=GuardrailEventHooks.pre_call,
            default_on=False,
        )
        self.block = block
        self.apply_called = False
        self.seen_texts = None

    async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None):
        from fastapi import HTTPException

        self.apply_called = True
        self.seen_texts = inputs.get("texts")
        if self.block:
            raise HTTPException(status_code=400, detail={"error": "Violated moderation policy"})
        return inputs


class TestApplyGuardrailStyleDeploymentDispatch:
    """LIT-4217 regression: model-level guardrails that implement only the
    unified apply_guardrail interface must execute in
    async_pre_call_deployment_hook instead of silently hitting the
    async_pre_call_hook no-op."""

    @pytest.mark.asyncio
    @pytest.mark.parametrize("call_type", [CallTypes.completion, CallTypes.acompletion])
    async def test_blocks_when_requested_via_model_level_guardrails(self, call_type):
        from fastapi import HTTPException

        guardrail = _ApplyStyleGuardrail(block=True)
        kwargs = {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "flagged content"}],
            "guardrails": ["apply-style-guardrail"],
            "metadata": {},
        }

        with pytest.raises(HTTPException):
            await guardrail.async_pre_call_deployment_hook(kwargs, call_type)

        assert guardrail.apply_called is True
        assert guardrail.seen_texts == ["flagged content"]

    @pytest.mark.asyncio
    async def test_pass_path_runs_guardrail_and_strips_dispatch_key(self):
        guardrail = _ApplyStyleGuardrail(block=False)
        kwargs = {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "hello"}],
            "guardrails": ["apply-style-guardrail"],
            "metadata": {},
        }

        result = await guardrail.async_pre_call_deployment_hook(kwargs, CallTypes.acompletion)

        assert guardrail.apply_called is True
        assert result is not None
        assert "guardrail_to_apply" not in result
        assert result["messages"] == [{"role": "user", "content": "hello"}]

    @pytest.mark.asyncio
    async def test_skips_when_not_requested(self):
        guardrail = _ApplyStyleGuardrail(block=True)
        kwargs = {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "hello"}],
            "guardrails": ["some-other-guardrail"],
            "metadata": {},
        }

        result = await guardrail.async_pre_call_deployment_hook(kwargs, CallTypes.acompletion)

        assert guardrail.apply_called is False
        assert result is not None

    @pytest.mark.asyncio
    async def test_fails_closed_when_proxy_extras_missing(self):
        import sys
        from unittest.mock import patch

        guardrail = _ApplyStyleGuardrail(block=True)
        kwargs = {
            "model": "gpt-4",
            "messages": [{"role": "user", "content": "flagged content"}],
            "guardrails": ["apply-style-guardrail"],
            "metadata": {},
        }

        with patch.dict(sys.modules, {"litellm.proxy.utils": None}):
            with pytest.raises(ImportError, match="litellm\\[proxy\\]"):
                await guardrail.async_pre_call_deployment_hook(kwargs, CallTypes.acompletion)

        assert guardrail.apply_called is False


class TestOnlyScanNewMessages:
    """Incremental guardrail scanning: only send text segments not already scanned this session."""

    def _guardrail(self, **overrides):
        params = dict(guardrail_name="test-guard", only_scan_new_messages=True)
        params.update(overrides)
        return CustomGuardrail(**params)

    def _cache(self):
        from litellm.caching import DualCache

        return DualCache()

    @pytest.mark.asyncio
    async def test_disabled_returns_none(self):
        guardrail = self._guardrail(only_scan_new_messages=False)
        result = await guardrail.filter_new_texts_for_session(
            texts=["hi"],
            request_data={"litellm_session_id": "s1"},
            cache=self._cache(),
        )
        assert result is None

    @pytest.mark.asyncio
    async def test_no_session_id_fails_safe_to_full_scan(self):
        guardrail = self._guardrail()
        result = await guardrail.filter_new_texts_for_session(
            texts=["hi"],
            request_data={"metadata": {}},
            cache=self._cache(),
        )
        assert result is None

    @pytest.mark.asyncio
    async def test_masking_guardrail_not_supported(self):
        guardrail = self._guardrail(mask_request_content=True)
        result = await guardrail.filter_new_texts_for_session(
            texts=["hi"],
            request_data={"litellm_session_id": "s1"},
            cache=self._cache(),
        )
        assert result is None

    @pytest.mark.asyncio
    async def test_cache_read_failure_fails_safe_to_full_scan(self):
        from unittest.mock import AsyncMock

        guardrail = self._guardrail()
        cache = self._cache()
        cache.async_get_cache = AsyncMock(side_effect=RuntimeError("redis down"))
        result = await guardrail.filter_new_texts_for_session(
            texts=["hi"],
            request_data={"litellm_session_id": "s1"},
            cache=cache,
        )
        assert result is None

    @pytest.mark.asyncio
    async def test_dedupes_previously_scanned_texts(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"litellm_session_id": "sess-dedupe"}
        turn1 = ["you are helpful", "first question"]

        first = await guardrail.filter_new_texts_for_session(texts=turn1, request_data=request, cache=cache)
        assert first == turn1
        await guardrail.mark_texts_scanned(texts=turn1, request_data=request, cache=cache)

        turn2 = turn1 + ["an answer", "second question"]
        second = await guardrail.filter_new_texts_for_session(texts=turn2, request_data=request, cache=cache)
        assert second == ["an answer", "second question"]

    @pytest.mark.asyncio
    async def test_no_new_texts_returns_empty(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"litellm_session_id": "sess-empty"}
        texts = ["only message"]

        await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        await guardrail.mark_texts_scanned(texts=texts, request_data=request, cache=cache)

        again = await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        assert again == []

    @pytest.mark.asyncio
    async def test_modified_earlier_text_is_rescanned(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"litellm_session_id": "sess-edit"}
        original = ["original"]

        await guardrail.filter_new_texts_for_session(texts=original, request_data=request, cache=cache)
        await guardrail.mark_texts_scanned(texts=original, request_data=request, cache=cache)

        edited = ["original EDITED"]
        result = await guardrail.filter_new_texts_for_session(texts=edited, request_data=request, cache=cache)
        assert result == edited

    @pytest.mark.asyncio
    async def test_blocked_scan_does_not_persist_hashes(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"litellm_session_id": "sess-blocked"}
        texts = ["please block me"]

        filtered = await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        assert filtered == texts

        again = await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        assert again == texts

    @pytest.mark.asyncio
    async def test_scanned_hashes_written_with_fixed_ttl(self):
        from unittest.mock import AsyncMock

        from litellm.constants import GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS

        guardrail = self._guardrail()
        cache = self._cache()
        cache.async_set_cache = AsyncMock()
        request = {"litellm_session_id": "sess-ttl"}

        await guardrail.mark_texts_scanned(texts=["a", "b"], request_data=request, cache=cache)

        cache.async_set_cache.assert_awaited_once()
        assert cache.async_set_cache.await_args.kwargs["ttl"] == GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS

    @pytest.mark.asyncio
    async def test_session_id_from_metadata_is_used_for_dedupe(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"metadata": {"session_id": "sess-meta"}}
        texts = ["shared message"]

        await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        await guardrail.mark_texts_scanned(texts=texts, request_data=request, cache=cache)

        again = await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        assert again == []

    @pytest.mark.asyncio
    async def test_session_id_from_litellm_metadata_is_used_for_dedupe(self):
        guardrail = self._guardrail()
        cache = self._cache()
        request = {"litellm_metadata": {"session_id": "sess-lmeta"}}
        texts = ["shared message"]

        await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        await guardrail.mark_texts_scanned(texts=texts, request_data=request, cache=cache)

        again = await guardrail.filter_new_texts_for_session(texts=texts, request_data=request, cache=cache)
        assert again == []

    @pytest.mark.asyncio
    async def test_mark_texts_scanned_disabled_does_not_persist(self):
        from unittest.mock import AsyncMock

        guardrail = self._guardrail(only_scan_new_messages=False)
        cache = self._cache()
        cache.async_set_cache = AsyncMock()

        await guardrail.mark_texts_scanned(texts=["a"], request_data={"litellm_session_id": "s1"}, cache=cache)
        cache.async_set_cache.assert_not_awaited()

    @pytest.mark.asyncio
    async def test_mark_texts_scanned_masking_does_not_persist(self):
        from unittest.mock import AsyncMock

        guardrail = self._guardrail(mask_request_content=True)
        cache = self._cache()
        cache.async_set_cache = AsyncMock()

        await guardrail.mark_texts_scanned(texts=["a"], request_data={"litellm_session_id": "s1"}, cache=cache)
        cache.async_set_cache.assert_not_awaited()

    @pytest.mark.asyncio
    async def test_mark_texts_scanned_without_session_does_not_persist(self):
        from unittest.mock import AsyncMock

        guardrail = self._guardrail()
        cache = self._cache()
        cache.async_set_cache = AsyncMock()

        await guardrail.mark_texts_scanned(texts=["a"], request_data={"metadata": {}}, cache=cache)
        cache.async_set_cache.assert_not_awaited()

    @pytest.mark.asyncio
    async def test_mark_texts_scanned_survives_cache_write_failure(self):
        from unittest.mock import AsyncMock

        guardrail = self._guardrail()
        cache = self._cache()
        cache.async_set_cache = AsyncMock(side_effect=RuntimeError("redis down"))

        await guardrail.mark_texts_scanned(texts=["a"], request_data={"litellm_session_id": "s1"}, cache=cache)


def _guardrail_entries(request_data: dict) -> list:
    container = request_data.get("metadata") or request_data.get("litellm_metadata") or {}
    entries = container.get("standard_logging_guardrail_information")
    return entries if isinstance(entries, list) else []


class _NoopGuardrail(CustomGuardrail):
    """apply_guardrail that returns the inputs untouched and records nothing."""

    @log_guardrail_information
    async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None):
        return inputs


class _NoopSelfLoggingGuardrail(_NoopGuardrail):
    records_own_guardrail_information = True


class TestRecordsOwnGuardrailInformation:
    """The @log_guardrail_information decorator must not synthesize an "allow"/"success"
    entry for a no-op apply_guardrail when the guardrail sets
    records_own_guardrail_information (LIT-4650)."""

    @pytest.mark.asyncio
    async def test_default_noop_apply_guardrail_is_auto_logged(self):
        guardrail = _NoopGuardrail(guardrail_name="g1")
        request_data: dict = {"model": "gpt-4o"}

        await guardrail.apply_guardrail(
            inputs=GenericGuardrailAPIInputs(texts=["x"]),
            request_data=request_data,
            input_type="request",
        )

        entries = _guardrail_entries(request_data)
        assert len(entries) == 1
        assert entries[0]["guardrail_status"] == "success"

    @pytest.mark.asyncio
    async def test_self_logging_noop_apply_guardrail_is_not_logged(self):
        guardrail = _NoopSelfLoggingGuardrail(guardrail_name="g2")
        request_data: dict = {"model": "gpt-4o"}

        await guardrail.apply_guardrail(
            inputs=GenericGuardrailAPIInputs(texts=["x"]),
            request_data=request_data,
            input_type="request",
        )

        assert _guardrail_entries(request_data) == []