diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 2df470edc59..505f1a94700 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -1557,6 +1557,12 @@ class CustomStreamWrapper:
         else:  # openai / azure chat model
             if isinstance(chunk, ChatCompletionChunk):
                 self._openai_handler_receives_chat_chunks = True
+                if any(str(choice.finish_reason) == "abort" for choice in chunk.choices):
+                    raise litellm.InternalServerError(
+                        message="Upstream generation aborted (finish_reason=abort)",
+                        model=chunk.model,
+                        llm_provider=self.custom_llm_provider,
+                    )
             if self.custom_llm_provider in [
                 LlmProviders.AZURE.value,
                 LlmProviders.AZURE_AI.value,
diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py
index b4c92848752..2c457009b24 100644
--- a/litellm/llms/openai/chat/gpt_transformation.py
+++ b/litellm/llms/openai/chat/gpt_transformation.py
@@ -76,6 +76,7 @@ else:
 
 _NO_TOOLS_UPDATE: Final[Mapping[str, object]] = MappingProxyType({})
 _OBJECT_MAPPING: Final[TypeAdapter[Mapping[str, object]]] = TypeAdapter(Mapping[str, object])
+_CHOICE_MAPPINGS: Final = TypeAdapter(tuple[Mapping[str, object], ...])
 _DECODED_JSON: Final = TypeAdapter(object)
 
 
@@ -958,6 +959,11 @@ class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator):
                     message=error_message,
                 )
             choices = chunk.get("choices", [])
+            if any(choice.get("finish_reason") == "abort" for choice in _CHOICE_MAPPINGS.validate_python(choices)):
+                raise OpenAIError(
+                    status_code=500,
+                    message="Upstream generation aborted (finish_reason=abort)",
+                )
             choices = self._map_reasoning_to_reasoning_content(choices)
             fingerprint: Final = _OBJECT_MAPPING.validate_python(chunk).get("system_fingerprint")
 
diff --git a/tests/unit/litellm_core_utils/test_streaming_handler.py b/tests/unit/litellm_core_utils/test_streaming_handler.py
index cc1fa1b8803..a56e042e76c 100644
--- a/tests/unit/litellm_core_utils/test_streaming_handler.py
+++ b/tests/unit/litellm_core_utils/test_streaming_handler.py
@@ -13,6 +13,7 @@ from unittest.mock import AsyncMock, MagicMock, Mock, patch
 
 import httpx
 import pytest
+from openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
 from openai.types.chat import ChatCompletionChunk
 from pydantic import BaseModel, TypeAdapter
 import litellm
@@ -28,6 +29,7 @@ from litellm.litellm_core_utils.streaming_handler import (
     _ProviderChunkParsed,
 )
 from litellm.llms.custom_httpx.http_handler import HTTPHandler
+from litellm.llms.openai.chat.gpt_transformation import OpenAIChatCompletionStreamingHandler
 from litellm.responses.mcp.chat_completions_handler import _add_mcp_metadata_to_response
 from litellm.types.utils import (
     CompletionTokensDetailsWrapper,
@@ -1367,6 +1369,82 @@ async def test_bedrock_validation_error_raises_directly(logging_obj: Logging):
     assert getattr(excinfo.value, "status_code", None) == 400
 
 
+@pytest.mark.parametrize("async_stream", [False, True])
+@pytest.mark.parametrize("transport", ["sdk", "http_handler"])
+@pytest.mark.parametrize("prefix", ["", "Partial answer"])
+@pytest.mark.parametrize("abort_index", [0, 1])
+@pytest.mark.parametrize("abort_content", ["", "unfinished"])
+async def test_aborted_stream_raises_instead_of_finishing_successfully(
+    async_stream: bool, transport: str, prefix: str, abort_index: int, abort_content: str, logging_obj: Logging
+) -> None:
+    initial: Final = (
+        (({"index": 0, "delta": {"role": "assistant", "content": prefix}, "finish_reason": None},),) if prefix else ()
+    )
+    final: Final = tuple(
+        {
+            "index": index,
+            "delta": {"content": abort_content},
+            "finish_reason": "abort" if index == abort_index else "stop",
+        }
+        for index in range(abort_index + 1)
+    )
+    body: Final = (
+        "".join(
+            "data: "
+            + json.dumps(
+                {
+                    "id": "chatcmpl-aborted",
+                    "object": "chat.completion.chunk",
+                    "created": 1,
+                    "model": "test-model",
+                    "choices": choices,
+                }
+            )
+            + "\n\n"
+            for choices in (*initial, final)
+        )
+        + "data: [DONE]\n\n"
+    )
+    raw_response: Final = httpx.Response(
+        200,
+        content=body,
+        headers={"content-type": "text/event-stream"},
+        request=httpx.Request("POST", "https://provider.example/v1/chat/completions"),
+    )
+    client: Final = AsyncOpenAI(api_key="unused") if async_stream else OpenAI(api_key="unused")
+    stream: Final = (
+        (
+            AsyncStream(cast_to=ChatCompletionChunk, response=raw_response, client=client)
+            if isinstance(client, AsyncOpenAI)
+            else Stream(cast_to=ChatCompletionChunk, response=raw_response, client=client)
+        )
+        if transport == "sdk"
+        else OpenAIChatCompletionStreamingHandler(
+            streaming_response=raw_response.aiter_lines() if async_stream else raw_response.iter_lines(),
+            sync_stream=not async_stream,
+        )
+    )
+    response: Final = CustomStreamWrapper(
+        completion_stream=stream,
+        model="test-model",
+        logging_obj=logging_obj,
+        custom_llm_provider="openai" if transport == "sdk" else "hosted_vllm",
+    )
+
+    try:
+        with pytest.raises(MidStreamFallbackError, match="abort") as excinfo:
+            _chunks: Final = [chunk async for chunk in response] if async_stream else list(response)
+
+        assert excinfo.value.is_pre_first_chunk is (not prefix)
+        assert excinfo.value.generated_content == prefix
+        assert getattr(excinfo.value.original_exception, "status_code", None) == 500
+    finally:
+        if isinstance(client, AsyncOpenAI):
+            await client.close()
+        else:
+            client.close()
+
+
 def _hosted_vllm_stream_wrapper(logging_obj: Logging, error_payload: dict) -> CustomStreamWrapper:
     """A CustomStreamWrapper over the real OpenAI-compatible line iterator,
     fed an HTTP 200 SSE body that carries an in-body error payload the way