fix(vertex): propagate Vertex AI metadata in streaming success callbacks (#29899)

* fix(vertex): propagate Vertex AI metadata in streaming success callbacks

Streaming calls assembled via stream_chunk_builder were missing
vertex_ai_grounding_metadata and vertex_ai_url_context_metadata in
standard_logging_object.response. Merge metadata from chunks into the
assembled response and mirror non-streaming hidden_params on Gemini chunks.

Co-authored-by: Cursor <cursoragent@cursor.com>

* refactor(vertex): move streaming metadata merge into provider config hook

Address review feedback by delegating assembled-stream metadata propagation
to VertexGeminiConfig via BaseConfig.apply_assembled_streaming_response_metadata,
and only write chunk hidden_params when metadata is non-empty.

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(redaction): scrub Vertex provider metadata when message logging is off

Clear vertex_ai_grounding_metadata and related fields from standard
logging responses and assembled streaming ModelResponse objects so
turn_off_message_logging cannot leak prompt-derived web search queries.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Use assembled model for streaming metadata hook

* Fix Vertex metadata redaction bypass in logging callbacks.

Scrub Vertex provider fields from litellm_params.metadata.hidden_params during perform_redaction so streaming success_handler merges do not leak prompt-derived metadata when message logging is disabled.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Fix Vertex streaming metadata from hidden params

* fix(vertex): mirror vertex_ai_safety_results on assembled streaming responses

The non-streaming transform_response stores safety data under
vertex_ai_safety_results, but the streaming path only wrote
vertex_ai_safety_ratings. Assembled streaming responses therefore never
carried vertex_ai_safety_results, so any consumer reading that field saw
a silent difference between streaming and non-streaming calls.

Set vertex_ai_safety_results alongside vertex_ai_safety_ratings in the
shared stream metadata setter and add it to the assembled metadata field
list so it propagates through stream_chunk_builder.

* fix(streaming): log provider streaming metadata hook failures instead of swallowing them

* refactor(vertex): share single Vertex metadata field tuple across redaction and streaming

* refactor(vertex): move Vertex metadata redaction helpers into llms/vertex_ai

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>

litellm/litellm_core_utils/redact_messages.py

@@ -17,6 +17,10 @@ from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.core_helpers import (
     get_metadata_variable_name_from_kwargs,
 )
+from litellm.llms.vertex_ai.common_utils import (
+    redact_vertex_ai_metadata_from_litellm_params,
+    redact_vertex_ai_metadata_from_logged_object,
+)
 from litellm.secret_managers.main import str_to_bool
 from litellm.types.utils import StandardCallbackDynamicParams
 
@@ -119,10 +123,12 @@ def _redact_standard_logging_object(model_call_details: dict):
             # ResponsesAPIResponse format - redact content in output items
             if isinstance(response.get("output"), list):
                 _redact_responses_api_output_dict(response["output"], redacted_str)
+            redact_vertex_ai_metadata_from_logged_object(response)
         elif isinstance(response, dict) and "choices" in response:
             # ModelResponse dict format - redact content in choices
             if isinstance(response.get("choices"), list):
                 _redact_model_response_dict_choices(response["choices"], redacted_str)
+            redact_vertex_ai_metadata_from_logged_object(response)
         elif isinstance(response, str):
             standard_logging_object["response"] = redacted_str
         else:
@@ -164,6 +170,7 @@ def perform_redaction(model_call_details: dict, result):
     model_call_details["prompt"] = ""
     model_call_details["input"] = ""
     _redact_standard_logging_object(model_call_details)
+    redact_vertex_ai_metadata_from_litellm_params(model_call_details)
 
     # Redact streaming response
     if (
@@ -174,6 +181,7 @@ def perform_redaction(model_call_details: dict, result):
         if hasattr(_streaming_response, "choices"):
             for choice in _streaming_response.choices:
                 _redact_choice_content(choice)
+            redact_vertex_ai_metadata_from_logged_object(_streaming_response)
         elif hasattr(_streaming_response, "output"):
             _redact_responses_api_output(_streaming_response.output)
             # Redact reasoning field in ResponsesAPIResponse
@@ -200,12 +208,14 @@ def perform_redaction(model_call_details: dict, result):
             if hasattr(_result, "choices") and _result.choices is not None:
                 for choice in _result.choices:
                     _redact_choice_content(choice)
+            redact_vertex_ai_metadata_from_logged_object(_result)
         elif isinstance(_result, dict) and "choices" in _result:
             # Handle dict representation of ModelResponse (e.g., from model_dump())
             if _result.get("choices") is not None:
                 _redact_model_response_dict_choices(
                     _result["choices"], "redacted-by-litellm"
                 )
+            redact_vertex_ai_metadata_from_logged_object(_result)
         elif isinstance(_result, dict) and "output" in _result:
             if isinstance(_result.get("output"), list):
                 _redact_responses_api_output_dict(

litellm/litellm_core_utils/streaming_chunk_builder_utils.py

@@ -20,6 +20,7 @@ from litellm.types.utils import (
     ServerToolUse,
     Usage,
 )
+from litellm._logging import verbose_logger
 from litellm.utils import print_verbose, token_counter
 
 if TYPE_CHECKING:
@@ -79,6 +80,54 @@ class ChunkProcessor:
             model_response._hidden_params = chunk.get("_hidden_params", {})
         return model_response
 
+    @staticmethod
+    def apply_provider_assembled_streaming_metadata(
+        response: ModelResponse,
+        chunks: List[Any],
+        logging_obj: Optional[Any] = None,
+    ) -> None:
+        if not chunks:
+            return
+
+        model = getattr(response, "model", None)
+        if not model:
+            return
+
+        custom_llm_provider = None
+        if logging_obj is not None:
+            custom_llm_provider = logging_obj.model_call_details.get(
+                "custom_llm_provider"
+            )
+
+        try:
+            from litellm.litellm_core_utils.get_llm_provider_logic import (
+                get_llm_provider,
+            )
+            from litellm.types.utils import LlmProviders
+            from litellm.utils import ProviderConfigManager
+
+            if custom_llm_provider:
+                provider = LlmProviders(custom_llm_provider)
+            else:
+                _, provider_str, _, _ = get_llm_provider(model)
+                provider = LlmProviders(provider_str)
+
+            provider_config = ProviderConfigManager.get_provider_chat_config(
+                model=model,
+                provider=provider,
+            )
+            if provider_config is not None:
+                provider_config.apply_assembled_streaming_response_metadata(
+                    response=response,
+                    chunks=chunks,
+                )
+        except Exception as e:
+            verbose_logger.debug(
+                "apply_provider_assembled_streaming_metadata failed for model=%s: %s",
+                model,
+                e,
+            )
+
     @staticmethod
     def _get_chunk_id(chunks: List[Dict[str, Any]]) -> str:
         """

litellm/llms/base_llm/chat/transformation.py

@@ -442,6 +442,14 @@ class BaseConfig(ABC):
         """Hook for providers to post-process streaming responses. Default: pass-through."""
         return stream
 
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: "ModelResponse",
+        chunks: List[Any],
+    ) -> None:
+        """Hook for providers to merge chunk metadata into assembled streaming responses."""
+        return None
+
     def calculate_additional_costs(
         self, model: str, prompt_tokens: int, completion_tokens: int
     ) -> Optional[dict]:

litellm/llms/vertex_ai/common_utils.py

@@ -12,7 +12,11 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import unpack_defs
 from litellm.llms.base_llm.base_utils import BaseLLMModelInfo, BaseTokenCounter
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.types.llms.openai import AllMessageValues
-from litellm.types.llms.vertex_ai import PartType, Schema
+from litellm.types.llms.vertex_ai import (
+    VERTEX_AI_PROVIDER_METADATA_FIELDS,
+    PartType,
+    Schema,
+)
 from litellm.types.utils import TokenCountResponse
 from litellm.utils import supports_response_schema, supports_system_messages
 
@@ -27,6 +31,47 @@ class VertexAIError(BaseLLMException):
         super().__init__(message=message, status_code=status_code, headers=headers)
 
 
+def redact_vertex_ai_metadata_from_logged_object(obj: Any) -> None:
+    if isinstance(obj, dict):
+        for field in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+            if field in obj:
+                obj[field] = []
+        hidden_params = obj.get("_hidden_params")
+        if isinstance(hidden_params, dict):
+            for field in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+                hidden_params.pop(field, None)
+        return
+
+    for field in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+        if hasattr(obj, field):
+            setattr(obj, field, [])
+    hidden_params = getattr(obj, "_hidden_params", None)
+    if isinstance(hidden_params, dict):
+        for field in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+            hidden_params.pop(field, None)
+
+
+def redact_vertex_ai_metadata_from_litellm_params(model_call_details: dict) -> None:
+    """
+    success_handler() merges response._hidden_params into
+    litellm_params.metadata['hidden_params'] before redaction runs, so the Vertex
+    metadata must be scrubbed from that copy too.
+    """
+    litellm_params = model_call_details.get("litellm_params")
+    if not isinstance(litellm_params, dict):
+        return
+
+    for metadata_key in ("metadata", "litellm_metadata"):
+        metadata = litellm_params.get(metadata_key)
+        if not isinstance(metadata, dict):
+            continue
+        hidden_params = metadata.get("hidden_params")
+        if not isinstance(hidden_params, dict):
+            continue
+        for field in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+            hidden_params.pop(field, None)
+
+
 def vertex_request_labels_from_litellm_params(
     litellm_params: Optional[dict],
 ) -> Optional[Dict[str, str]]:

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -63,6 +63,7 @@ from litellm.types.llms.openai import (
     OpenAIChatCompletionFinishReason,
 )
 from litellm.types.llms.vertex_ai import (
+    VERTEX_AI_PROVIDER_METADATA_FIELDS,
     VERTEX_CREDENTIALS_TYPES,
     Candidates,
     ContentType,
@@ -2258,6 +2259,71 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             citation_metadata,
         )
 
+    @staticmethod
+    def _get_stream_chunk_attr(chunk: Any, field_name: str) -> Any:
+        if isinstance(chunk, dict):
+            value = chunk.get(field_name)
+            if value is not None:
+                return value
+            model_extra = chunk.get("model_extra")
+            if isinstance(model_extra, dict):
+                value = model_extra.get(field_name)
+                if value is not None:
+                    return value
+            hidden_params = chunk.get("_hidden_params")
+            if isinstance(hidden_params, dict):
+                return hidden_params.get(field_name)
+            return None
+        return getattr(chunk, field_name, None)
+
+    @staticmethod
+    def _set_stream_metadata_on_response(
+        model_response: Any,
+        grounding_metadata: List[dict],
+        url_context_metadata: List[dict],
+        safety_ratings: List[dict],
+        citation_metadata: List[dict],
+    ) -> None:
+        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
+        if grounding_metadata:
+            model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+                grounding_metadata
+            )
+        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
+        if url_context_metadata:
+            model_response._hidden_params["vertex_ai_url_context_metadata"] = (
+                url_context_metadata
+            )
+        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
+        setattr(model_response, "vertex_ai_safety_results", safety_ratings)  # type: ignore
+        if safety_ratings:
+            model_response._hidden_params["vertex_ai_safety_ratings"] = safety_ratings
+            model_response._hidden_params["vertex_ai_safety_results"] = safety_ratings
+        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        if citation_metadata:
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata
+            )
+
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: ModelResponse,
+        chunks: List[Any],
+    ) -> None:
+        for field_name in VERTEX_AI_PROVIDER_METADATA_FIELDS:
+            merged: List[Any] = []
+            for chunk in chunks:
+                value = VertexGeminiConfig._get_stream_chunk_attr(chunk, field_name)
+                if not value:
+                    continue
+                if isinstance(value, list):
+                    merged.extend(value)
+                else:
+                    merged.append(value)
+            if merged:
+                setattr(response, field_name, merged)
+                response._hidden_params[field_name] = merged
+
     @staticmethod
     def _convert_grounding_metadata_to_annotations(
         grounding_metadata: List[dict],
@@ -3390,10 +3456,13 @@ class ModelResponseIterator:
                 if choice.finish_reason == "stop":
                     choice.finish_reason = "tool_calls"
 
-        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
-        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        VertexGeminiConfig._set_stream_metadata_on_response(
+            model_response,
+            grounding_metadata,
+            url_context_metadata,
+            safety_ratings,
+            citation_metadata,
+        )
 
         return (
             grounding_metadata,

litellm/main.py

@@ -7761,6 +7761,9 @@ def stream_chunk_builder(  # noqa: PLR0915
                     "cost",
                     logging_obj._response_cost_calculator(result=response),
                 )
+            processor.apply_provider_assembled_streaming_metadata(
+                response, chunks, logging_obj
+            )
             return response
 
         tool_call_chunks = [
@@ -7940,6 +7943,9 @@ def stream_chunk_builder(  # noqa: PLR0915
                 usage, "cost", logging_obj._response_cost_calculator(result=response)
             )
 
+        processor.apply_provider_assembled_streaming_metadata(
+            response, chunks, logging_obj
+        )
         return response
     except Exception as e:
         verbose_logger.exception(

litellm/types/llms/vertex_ai.py

@@ -758,3 +758,12 @@ class VertexPartnerProvider(str, Enum):
     llama = "llama"
     ai21 = "ai21"
     claude = "claude"
+
+
+VERTEX_AI_PROVIDER_METADATA_FIELDS = (
+    "vertex_ai_grounding_metadata",
+    "vertex_ai_url_context_metadata",
+    "vertex_ai_safety_ratings",
+    "vertex_ai_safety_results",
+    "vertex_ai_citation_metadata",
+)

tests/test_litellm/litellm_core_utils/test_litellm_logging.py

@@ -2165,6 +2165,41 @@ def test_get_assembled_streaming_response_returns_result_for_streaming():
     assert assembled is result
 
 
+def test_streaming_success_handler_includes_vertex_ai_metadata_in_standard_logging():
+    """Assembled streaming responses should include Vertex AI metadata in logging payload."""
+    import datetime
+
+    from litellm.types.utils import Choices, Message
+
+    logging_obj = _make_logging_obj(stream=True)
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+    result = ModelResponse(
+        id="resp-1",
+        choices=[
+            Choices(
+                index=0,
+                message=Message(role="assistant", content="hello"),
+                finish_reason="stop",
+            )
+        ],
+        model="gemini-2.5-flash",
+    )
+    setattr(result, "vertex_ai_grounding_metadata", grounding_metadata)
+    setattr(result, "vertex_ai_url_context_metadata", url_context_metadata)
+    result._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+    result._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    start = datetime.datetime.now()
+    end = datetime.datetime.now()
+    logging_obj.success_handler(result=result, start_time=start, end_time=end)
+
+    payload = logging_obj.model_call_details.get("standard_logging_object")
+    assert payload is not None
+    assert payload["response"]["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert payload["response"]["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
 def test_get_assembled_streaming_response_returns_none_for_non_streaming_text_completion():
     """Non-streaming TextCompletionResponse should also return None."""
     import datetime

tests/test_litellm/litellm_core_utils/test_redact_messages.py

@@ -349,3 +349,96 @@ class TestPerformRedaction:
 
         assert redacted.output[0].content[0].text == "redacted-by-litellm"
         assert response.output[0].content[0].text == "sensitive output"
+
+    def test_redacts_vertex_provider_metadata_in_standard_logging_response(self):
+        details = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "sensitive prompt"}],
+                "response": {
+                    "choices": [
+                        {
+                            "message": {
+                                "content": "sensitive answer",
+                                "role": "assistant",
+                            }
+                        }
+                    ],
+                    "vertex_ai_grounding_metadata": [
+                        {"webSearchQueries": ["sensitive search term"]}
+                    ],
+                    "vertex_ai_url_context_metadata": [
+                        {"urlMetadata": [{"retrievedUrl": "https://example.com"}]}
+                    ],
+                },
+            }
+        }
+
+        perform_redaction(details, None)
+
+        response = details["standard_logging_object"]["response"]
+        assert response["choices"][0]["message"]["content"] == "redacted-by-litellm"
+        assert response["vertex_ai_grounding_metadata"] == []
+        assert response["vertex_ai_url_context_metadata"] == []
+
+    def test_redacts_vertex_provider_metadata_on_streaming_model_response(self):
+        response = litellm.ModelResponse(
+            id="resp-1",
+            choices=[
+                litellm.Choices(
+                    message=litellm.Message(
+                        content="sensitive answer",
+                        role="assistant",
+                    )
+                )
+            ],
+            model="gemini-2.5-flash",
+        )
+        setattr(
+            response,
+            "vertex_ai_grounding_metadata",
+            [{"webSearchQueries": ["sensitive search term"]}],
+        )
+        response._hidden_params["vertex_ai_grounding_metadata"] = [
+            {"webSearchQueries": ["sensitive search term"]}
+        ]
+
+        details = {
+            "stream": True,
+            "complete_streaming_response": response,
+        }
+
+        perform_redaction(details, response)
+
+        assert response.choices[0].message.content == "redacted-by-litellm"
+        assert getattr(response, "vertex_ai_grounding_metadata") == []
+        assert "vertex_ai_grounding_metadata" not in response._hidden_params
+
+    def test_redacts_vertex_provider_metadata_from_metadata_hidden_params(self):
+        """Streaming success_handler copies _hidden_params into metadata before redaction."""
+        details = {
+            "stream": True,
+            "litellm_params": {
+                "metadata": {
+                    "hidden_params": {
+                        "response_cost": 0.01,
+                        "vertex_ai_grounding_metadata": [
+                            {"webSearchQueries": ["sensitive search term"]}
+                        ],
+                        "vertex_ai_url_context_metadata": [
+                            {"urlMetadata": [{"retrievedUrl": "https://example.com"}]}
+                        ],
+                        "vertex_ai_safety_ratings": [{"category": "HARM"}],
+                        "vertex_ai_citation_metadata": [{"citations": ["source"]}],
+                    }
+                }
+            },
+        }
+
+        perform_redaction(details, None)
+
+        hidden_params = details["litellm_params"]["metadata"]["hidden_params"]
+        assert hidden_params["response_cost"] == 0.01
+        assert "vertex_ai_grounding_metadata" not in hidden_params
+        assert "vertex_ai_url_context_metadata" not in hidden_params
+        assert "vertex_ai_safety_ratings" not in hidden_params
+        assert "vertex_ai_citation_metadata" not in hidden_params

tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py

@@ -613,3 +613,153 @@ def test_stream_chunk_builder_dict_snapshot_preserves_hidden_provider_fields():
     assert (
         response._hidden_params["provider_specific_fields"]["traffic_type"] == "default"
     )
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks():
+    """Vertex AI metadata on streaming chunks must appear on assembled response."""
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk1, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk1._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_url_context_metadata", url_context_metadata)
+    chunk2._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+    assert getattr(response, "vertex_ai_url_context_metadata") == url_context_metadata
+    assert response._hidden_params["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert (
+        response._hidden_params["vertex_ai_url_context_metadata"]
+        == url_context_metadata
+    )
+
+    dumped = response.model_dump()
+    assert dumped["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert dumped["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
+def test_stream_chunk_builder_uses_assembled_model_for_provider_metadata():
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gpt-4o",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role=None),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk2._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert response.model == "gemini-2.5-flash"
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_safety_results():
+    """Assembled response must expose safety data under the non-streaming field name."""
+    safety_ratings = [
+        [{"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"}]
+    ]
+
+    chunk = ModelResponseStream(
+        id="chatcmpl-vertex-safety",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content="hello", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk, "vertex_ai_safety_ratings", safety_ratings)
+    setattr(chunk, "vertex_ai_safety_results", safety_ratings)
+    chunk._hidden_params["vertex_ai_safety_ratings"] = safety_ratings
+    chunk._hidden_params["vertex_ai_safety_results"] = safety_ratings
+
+    response = stream_chunk_builder(chunks=[chunk])
+    assert response is not None
+    assert getattr(response, "vertex_ai_safety_results") == safety_ratings
+    assert response._hidden_params["vertex_ai_safety_results"] == safety_ratings
+    assert response.model_dump()["vertex_ai_safety_results"] == safety_ratings
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_dict_chunks():
+    """Dict snapshot chunks (model_dump) should also propagate Vertex AI metadata."""
+    chunk_dict = ModelResponseStream(
+        id="chatcmpl-vertex-2",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content="hello", role="assistant"),
+            )
+        ],
+    ).model_dump()
+    chunk_dict["_hidden_params"] = {
+        "vertex_ai_grounding_metadata": [{"webSearchQueries": ["test query"]}]
+    }
+
+    response = stream_chunk_builder(chunks=[chunk_dict])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == [
+        {"webSearchQueries": ["test query"]}
+    ]
+    assert response.model_dump()["vertex_ai_grounding_metadata"] == [
+        {"webSearchQueries": ["test query"]}
+    ]

tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py

@@ -1459,6 +1459,26 @@ def test_vertex_ai_process_candidates_with_grounding_metadata():
     assert len(result[0]) == 1
 
 
+def test_set_stream_metadata_mirrors_non_streaming_safety_field_names():
+    safety_ratings = [
+        [{"category": "HARM_CATEGORY_HATE_SPEECH", "probability": "NEGLIGIBLE"}]
+    ]
+
+    model_response = ModelResponse()
+    VertexGeminiConfig._set_stream_metadata_on_response(
+        model_response=model_response,
+        grounding_metadata=[],
+        url_context_metadata=[],
+        safety_ratings=safety_ratings,
+        citation_metadata=[],
+    )
+
+    assert getattr(model_response, "vertex_ai_safety_ratings") == safety_ratings
+    assert getattr(model_response, "vertex_ai_safety_results") == safety_ratings
+    assert model_response._hidden_params["vertex_ai_safety_ratings"] == safety_ratings
+    assert model_response._hidden_params["vertex_ai_safety_results"] == safety_ratings
+
+
 def test_vertex_ai_tool_call_id_format():
     """
     Test that tool call IDs have the correct format and length.