Merge pull request #32587 from BerriAI/litellm_fix_batch_model_access_hash_32580

fix(auth): resolve managed batch/file deployment model_id to model name for team access checks

litellm/proxy/auth/auth_utils.py

@@ -1432,7 +1432,7 @@ def _extract_models_from_managed_resource_id(
             )
             _append_model_candidates(
                 candidates=candidates,
-                value=get_model_id_from_unified_batch_id(unified_file_id),
+                value=_resolve_model_id_with_router(get_model_id_from_unified_batch_id(unified_file_id), llm_router),
             )
     except Exception as e:
         verbose_proxy_logger.debug("Unable to extract model from managed file/batch ID: %s", str(e))
@@ -1442,7 +1442,10 @@ def _extract_models_from_managed_resource_id(
 
         parsed_id = parse_unified_id(resource_id)
         if parsed_id:
-            _append_model_candidates(candidates=candidates, value=parsed_id.get("model_id"))
+            _append_model_candidates(
+                candidates=candidates,
+                value=_resolve_model_id_with_router(parsed_id.get("model_id"), llm_router),
+            )
             _append_model_candidates(candidates=candidates, value=parsed_id.get("target_model_names"))
     except Exception as e:
         verbose_proxy_logger.debug("Unable to extract model from unified managed resource ID: %s", str(e))

litellm/router.py

@@ -9560,7 +9560,12 @@ class Router:
             return None
 
         # Strategy 1: Check if model_id directly matches a model_name or deployment ID
-        if model_id in self.model_names or self.has_model_id(model_id):
+        if model_id in self.model_names:
+            return model_id
+        if self.has_model_id(model_id):
+            deployment = self.get_deployment(model_id=model_id)
+            if deployment is not None and deployment.model_name:
+                return deployment.model_name
             return model_id
 
         # Strategy 2: Search through router's model_list to find by litellm_params.model

tests/router_unit_tests/test_router_helper_utils.py

@@ -2659,6 +2659,23 @@ def test_resolve_model_name_from_model_id():
     result = router.resolve_model_name_from_model_id("gpt-5-mini")
     assert result == "gpt-5-mini"
 
+    # Test case 10: model_id is a deployment ID (hash) that differs from the
+    # public model_name. Regression for #32580: managed batch/file IDs embed the
+    # deployment model_id, and it must resolve back to the public model_name so
+    # team model-access checks compare against the model group, not the hash.
+    model_list = [
+        {
+            "model_name": "bedrock-batch-model",
+            "litellm_params": {
+                "model": "bedrock/global.anthropic.claude-haiku-4-5-20251001-v1:0",
+            },
+            "model_info": {"id": "8d0eaa7e6c6f54a425dfd0062cb6b0dc"},
+        },
+    ]
+    router = Router(model_list=model_list)
+    result = router.resolve_model_name_from_model_id("8d0eaa7e6c6f54a425dfd0062cb6b0dc")
+    assert result == "bedrock-batch-model"
+
 
 def test_get_valid_args():
     """Test get_valid_args static method returns valid Router.__init__ arguments"""

tests/test_litellm/proxy/auth/test_auth_utils.py

@@ -569,6 +569,103 @@ def test_get_model_from_request_resolves_video_id_model_with_router():
     )
 
 
+_BATCH_DEPLOYMENT_ID = "8d0eaa7e6c6f54a425dfd0062cb6b0dc"
+
+
+def _managed_batch_router():
+    from litellm.router import Router
+
+    return Router(
+        model_list=[
+            {
+                "model_name": "bedrock-batch-model",
+                "litellm_params": {
+                    "model": "bedrock/global.anthropic.claude-haiku-4-5-20251001-v1:0",
+                },
+                "model_info": {"id": _BATCH_DEPLOYMENT_ID},
+            },
+            {
+                "model_name": "some-other-model",
+                "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "test-key"},
+                "model_info": {"id": "a-different-deployment-id"},
+            },
+        ]
+    )
+
+
+def _encode_managed_id(decoded: str) -> str:
+    return base64.urlsafe_b64encode(decoded.encode()).decode().rstrip("=")
+
+
+_MANAGED_BATCH_ID = _encode_managed_id(
+    f"litellm_proxy;model_id:{_BATCH_DEPLOYMENT_ID};llm_batch_id:provider-batch-123"
+)
+_MANAGED_BATCH_OUTPUT_FILE_ID = _encode_managed_id(
+    f"litellm_proxy;model_id:{_BATCH_DEPLOYMENT_ID};llm_batch_id:provider-batch-123;"
+    "llm_output_file_id:provider-file-456"
+)
+
+
+@pytest.mark.parametrize(
+    "route, request_data",
+    [
+        ("/v1/batches/{batch_id}", {"batch_id": _MANAGED_BATCH_ID}),
+        ("/v1/batches/{batch_id}/cancel", {"batch_id": _MANAGED_BATCH_ID}),
+        ("/v1/files/{file_id}", {"file_id": _MANAGED_BATCH_OUTPUT_FILE_ID}),
+        ("/v1/files/{file_id}/content", {"file_id": _MANAGED_BATCH_OUTPUT_FILE_ID}),
+    ],
+)
+def test_get_model_from_request_resolves_batch_id_deployment_to_model_name(route, request_data):
+    """Regression for #32580: managed batch retrieve/cancel and managed batch output
+    file reads encode the deployment model_id into the resource id. The auth layer must
+    resolve that id back to the public model group name so model-access checks compare
+    against the model group, not the raw deployment id."""
+    assert (
+        get_model_from_request(
+            request_data=request_data,
+            route=route,
+            llm_router=_managed_batch_router(),
+        )
+        == "bedrock-batch-model"
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "route, request_data",
+    [
+        ("/v1/batches/{batch_id}", {"batch_id": _MANAGED_BATCH_ID}),
+        ("/v1/batches/{batch_id}/cancel", {"batch_id": _MANAGED_BATCH_ID}),
+        ("/v1/files/{file_id}/content", {"file_id": _MANAGED_BATCH_OUTPUT_FILE_ID}),
+    ],
+)
+async def test_managed_batch_routes_pass_team_model_access_check(route, request_data):
+    """End-to-end regression for #32580: a team scoped to the batch model group got
+    ``team_model_access_denied`` on retrieve/cancel because the deployment id, not the
+    model group, was authorized. Fails pre-fix with the deployment id in the message."""
+    from litellm.proxy._types import LiteLLM_TeamTable
+    from litellm.proxy.auth.auth_checks import can_team_access_model
+
+    llm_router = _managed_batch_router()
+    model = get_model_from_request(request_data=request_data, route=route, llm_router=llm_router)
+
+    assert (
+        await can_team_access_model(
+            model=model,
+            team_object=LiteLLM_TeamTable(team_id="team-batch", models=["bedrock-batch-model"]),
+            llm_router=llm_router,
+        )
+        is True
+    )
+
+    with pytest.raises(Exception, match="team not allowed to access model"):
+        await can_team_access_model(
+            model=model,
+            team_object=LiteLLM_TeamTable(team_id="team-other", models=["some-other-model"]),
+            llm_router=llm_router,
+        )
+
+
 def test_get_model_from_request_resolves_character_id_model_with_router():
     from litellm.types.videos.utils import encode_character_id_with_provider