lint url encoding

litellm/llms/anthropic/batches/transformation.py

@@ -5,6 +5,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cas
 import httpx
 from httpx import Headers, Response
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.batches.transformation import BaseBatchesConfig
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.types.llms.openai import AllMessageValues, CreateBatchRequest
@@ -122,7 +123,7 @@ class AnthropicBatchesConfig(BaseBatchesConfig):
             Complete URL for Anthropic batch retrieval: {api_base}/v1/messages/batches/{batch_id}
         """
         api_base = api_base or self.anthropic_model_info.get_api_base(api_base)
-        return f"{api_base.rstrip('/')}/v1/messages/batches/{batch_id}"
+        return f"{api_base.rstrip('/')}/v1/messages/batches/{encode_path_segment(batch_id)}"
 
     def transform_retrieve_batch_request(
         self,

litellm/llms/anthropic/files/handler.py

@@ -9,6 +9,7 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
 from litellm.types.llms.openai import (
     FileContentRequest,
@@ -89,7 +90,7 @@ class AnthropicFilesHandler:
             raise ValueError("Missing Anthropic API Key")
 
         # Construct the Anthropic batch results URL
-        results_url = f"{api_base.rstrip('/')}/v1/messages/batches/{batch_id}/results"
+        results_url = f"{api_base.rstrip('/')}/v1/messages/batches/{encode_path_segment(batch_id)}/results"
 
         # Prepare headers
         headers = {

litellm/llms/anthropic/files/transformation.py

@@ -20,6 +20,7 @@ import httpx
 from openai.types.file_deleted import FileDeleted
 
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.files.transformation import (
     BaseFilesConfig,
@@ -185,7 +186,7 @@ class AnthropicFilesConfig(BaseFilesConfig):
             AnthropicModelInfo.get_api_base(litellm_params.get("api_base"))
             or ANTHROPIC_FILES_API_BASE
         )
-        return f"{api_base.rstrip('/')}/v1/files/{file_id}", {}
+        return f"{api_base.rstrip('/')}/v1/files/{encode_path_segment(file_id)}", {}
 
     def transform_retrieve_file_response(
         self,
@@ -206,7 +207,7 @@ class AnthropicFilesConfig(BaseFilesConfig):
             AnthropicModelInfo.get_api_base(litellm_params.get("api_base"))
             or ANTHROPIC_FILES_API_BASE
         )
-        return f"{api_base.rstrip('/')}/v1/files/{file_id}", {}
+        return f"{api_base.rstrip('/')}/v1/files/{encode_path_segment(file_id)}", {}
 
     def transform_delete_file_response(
         self,
@@ -268,7 +269,10 @@ class AnthropicFilesConfig(BaseFilesConfig):
             AnthropicModelInfo.get_api_base(litellm_params.get("api_base"))
             or ANTHROPIC_FILES_API_BASE
         )
-        return f"{api_base.rstrip('/')}/v1/files/{file_id}/content", {}
+        return (
+            f"{api_base.rstrip('/')}/v1/files/{encode_path_segment(file_id)}/content",
+            {},
+        )
 
     def transform_file_content_response(
         self,

litellm/llms/anthropic/skills/transformation.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, Optional, Tuple
 import httpx
 
 from litellm._logging import verbose_logger
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.skills.transformation import (
     BaseSkillsAPIConfig,
     LiteLLMLoggingObj,
@@ -81,7 +82,7 @@ class AnthropicSkillsConfig(BaseSkillsAPIConfig):
             api_base = AnthropicModelInfo.get_api_base()
 
         if skill_id:
-            return f"{api_base}/v1/skills/{skill_id}"
+            return f"{api_base}/v1/skills/{encode_path_segment(skill_id)}"
         return f"{api_base}/v1/{endpoint}"
 
     def transform_create_skill_request(

litellm/llms/azure/common_utils.py

@@ -8,6 +8,7 @@ from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
 import litellm
 from litellm._logging import verbose_logger
 from litellm.caching.caching import DualCache
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.openai.common_utils import BaseOpenAILLM
 from litellm.secret_managers.get_azure_ad_token_provider import (
@@ -211,7 +212,7 @@ def get_azure_ad_token_from_oidc(
     client = litellm.module_level_client
 
     req_token = client.post(
-        f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
+        f"{azure_authority_host}/{encode_path_segment(azure_tenant_id)}/oauth2/v2.0/token",
         data={
             "client_id": azure_client_id,
             "grant_type": "client_credentials",

litellm/llms/azure/responses/transformation.py

@@ -6,6 +6,7 @@ from openai.types.responses import ResponseReasoningItem
 
 from litellm._logging import verbose_logger
 from litellm.llms.azure.common_utils import BaseAzureLLM
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
 from litellm.types.llms.openai import *
 from litellm.types.responses.main import *
@@ -201,7 +202,7 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
         # Insert the response_id at the end of the path component
         # Remove trailing slash if present to avoid double slashes
         path = parsed_url.path.rstrip("/")
-        new_path = f"{path}/{response_id}"
+        new_path = f"{path}/{encode_path_segment(response_id)}"
 
         # Reconstruct the URL with all original components but with the modified path
         constructed_url = urlunparse(
@@ -322,7 +323,7 @@ class AzureOpenAIResponsesAPIConfig(OpenAIResponsesAPIConfig):
         # Insert the response_id and /cancel at the end of the path component
         # Remove trailing slash if present to avoid double slashes
         path = parsed_url.path.rstrip("/")
-        new_path = f"{path}/{response_id}/cancel"
+        new_path = f"{path}/{encode_path_segment(response_id)}/cancel"
 
         # Reconstruct the URL with all original components but with the modified path
         cancel_url = urlunparse(

litellm/llms/azure_ai/agents/handler.py

@@ -40,6 +40,7 @@ from litellm.llms.azure_ai.agents.transformation import (
     AzureAIAgentsConfig,
     AzureAIAgentsError,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.types.utils import ModelResponse
 
 if TYPE_CHECKING:
@@ -75,20 +76,20 @@ class AzureAIAgentsHandler:
     def _build_messages_url(
         self, api_base: str, thread_id: str, api_version: str
     ) -> str:
-        return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}"
+        return f"{api_base}/threads/{encode_path_segment(thread_id)}/messages?api-version={api_version}"
 
     def _build_runs_url(self, api_base: str, thread_id: str, api_version: str) -> str:
-        return f"{api_base}/threads/{thread_id}/runs?api-version={api_version}"
+        return f"{api_base}/threads/{encode_path_segment(thread_id)}/runs?api-version={api_version}"
 
     def _build_run_status_url(
         self, api_base: str, thread_id: str, run_id: str, api_version: str
     ) -> str:
-        return f"{api_base}/threads/{thread_id}/runs/{run_id}?api-version={api_version}"
+        return f"{api_base}/threads/{encode_path_segment(thread_id)}/runs/{encode_path_segment(run_id)}?api-version={api_version}"
 
     def _build_list_messages_url(
         self, api_base: str, thread_id: str, api_version: str
     ) -> str:
-        return f"{api_base}/threads/{thread_id}/messages?api-version={api_version}"
+        return f"{api_base}/threads/{encode_path_segment(thread_id)}/messages?api-version={api_version}"
 
     def _build_create_thread_and_run_url(self, api_base: str, api_version: str) -> str:
         """URL for the create-thread-and-run endpoint (supports streaming)."""

litellm/llms/azure_ai/ocr/document_intelligence/transformation.py

@@ -22,6 +22,7 @@ from litellm.constants import (
     AZURE_DOCUMENT_INTELLIGENCE_DEFAULT_DPI,
     AZURE_OPERATION_POLLING_TIMEOUT,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.ocr.transformation import (
     BaseOCRConfig,
     DocumentType,
@@ -220,10 +221,7 @@ class AzureDocumentIntelligenceOCRConfig(BaseOCRConfig):
 
         # Azure Document Intelligence analyze endpoint
         # Note: API version 2024-11-30+ uses /documentintelligence/ (not /formrecognizer/)
-        url = (
-            f"{api_base}/documentintelligence/documentModels/{model_id}:analyze"
-            f"?api-version={AZURE_DOCUMENT_INTELLIGENCE_API_VERSION}"
-        )
+        url = f"{api_base}/documentintelligence/documentModels/{encode_path_segment(model_id)}:analyze?api-version={AZURE_DOCUMENT_INTELLIGENCE_API_VERSION}"
 
         # Azure DI accepts `pages` as a query param (1-based, e.g. "1-3,5").
         # `optional_params` has already been normalized in `map_ocr_params`.

litellm/llms/azure_ai/vector_stores/transformation.py

@@ -4,6 +4,7 @@ import httpx
 
 import litellm
 from litellm.llms.azure.common_utils import BaseAzureLLM
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
@@ -139,7 +140,7 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
 
         # Azure AI Search endpoint for search
         index_name = vector_store_id  # vector_store_id is the index name
-        url = f"{api_base}/indexes/{index_name}/docs/search?api-version=2024-07-01"
+        url = f"{api_base}/indexes/{encode_path_segment(index_name)}/docs/search?api-version=2024-07-01"
 
         # Build the request body for Azure AI Search with vector search
         request_body = {

litellm/llms/base_llm/_url_utils.py

@@ -0,0 +1,37 @@
+"""URL-encoding helpers for provider transformations."""
+
+from typing import Optional
+from urllib.parse import quote
+
+
+def _reject_dot_segment(segment: str, full: str) -> None:
+    if segment in ("..", "."):
+        raise ValueError(f"Illegal path segment in identifier: {full!r}")
+
+
+def encode_path_segment(segment: Optional[str], safe: str = "") -> str:
+    """Percent-encode a single path segment. Raises on empty / ``None`` / ``.`` / ``..``.
+
+    ``safe`` is forwarded to ``urllib.parse.quote`` for callers that need to
+    preserve specific characters (e.g. ``:`` in Bedrock model IDs).
+    """
+    if segment is None or segment == "":
+        raise ValueError("identifier is required, got empty or None")
+    str_segment = str(segment)
+    _reject_dot_segment(str_segment, str_segment)
+    return quote(str_segment, safe=safe)
+
+
+def encode_url_path(path: Optional[str]) -> str:
+    """Percent-encode a multi-segment path; preserves ``/`` and ``@``.
+
+    ``None`` / ``""`` → ``""``. Rejects ``.``, ``..``, or empty segments.
+    """
+    if path is None or path == "":
+        return ""
+    str_path = str(path)
+    for segment in str_path.split("/"):
+        if segment == "":
+            raise ValueError(f"Empty path segment in identifier: {str_path!r}")
+        _reject_dot_segment(segment, str_path)
+    return quote(str_path, safe="/@")

litellm/llms/bedrock/chat/invoke_agent/transformation.py

@@ -15,6 +15,7 @@ from litellm._uuid import uuid
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     convert_content_list_to_str,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.bedrock.common_utils import BedrockError
@@ -98,7 +99,7 @@ class AmazonInvokeAgentConfig(BaseConfig, BaseAWSLLM):
         agent_id, agent_alias_id = self._get_agent_id_and_alias_id(model)
         session_id = self._get_session_id(optional_params)
 
-        endpoint_url = f"{endpoint_url}/agents/{agent_id}/agentAliases/{agent_alias_id}/sessions/{session_id}/text"
+        endpoint_url = f"{endpoint_url}/agents/{encode_path_segment(agent_id)}/agentAliases/{encode_path_segment(agent_alias_id)}/sessions/{encode_path_segment(session_id)}/text"
 
         return endpoint_url
 

litellm/llms/bedrock/chat/invoke_transformations/amazon_openai_transformation.py

@@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
 
 import httpx
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.bedrock.common_utils import BedrockError
 from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
@@ -98,6 +99,9 @@ class AmazonBedrockOpenAIConfig(OpenAIGPTConfig, BaseAWSLLM):
 
         # Encode model ID for ARNs (e.g., :imported-model/ -> :imported-model%2F)
         model_id = CommonUtils.encode_bedrock_runtime_modelid_arn(model_id)
+        if "arn:aws:" not in model_id:
+            # Bedrock model IDs use ':' for version (e.g. amazon.nova-pro-v1:0)
+            model_id = encode_path_segment(model_id, safe=":")
 
         # Build the invoke URL
         if stream:

litellm/llms/bedrock/count_tokens/transformation.py

@@ -8,6 +8,7 @@ to AWS Bedrock's CountTokens API format and vice versa.
 import re
 from typing import Any, Dict, List, Optional
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.bedrock.common_utils import get_bedrock_base_model
 
@@ -207,7 +208,10 @@ class BedrockCountTokensConfig(BaseAWSLLM):
             aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint,
             aws_region_name=aws_region_name,
         )
-        endpoint = f"{base_url}/model/{model_id}/count-tokens"
+        # Bedrock model IDs use ':' for version (e.g. amazon.nova-pro-v1:0)
+        endpoint = (
+            f"{base_url}/model/{encode_path_segment(model_id, safe=':')}/count-tokens"
+        )
 
         return endpoint
 

litellm/llms/bedrock/files/transformation.py

@@ -11,6 +11,7 @@ from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.files.utils import FilesAPIUtils
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.files.transformation import (
     BaseFilesConfig,
@@ -195,7 +196,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
             or f"https://s3.{aws_region_name}.amazonaws.com"
         )
 
-        return f"{s3_endpoint_url}/{bucket_name}/{object_name}"
+        return f"{s3_endpoint_url}/{encode_path_segment(bucket_name)}/{encode_url_path(object_name)}"
 
     def get_supported_openai_params(
         self, model: str

litellm/llms/bedrock/vector_stores/transformation.py

@@ -3,6 +3,7 @@ from urllib.parse import urlparse
 
 import httpx
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.types.integrations.rag.bedrock_knowledgebase import (
@@ -206,7 +207,7 @@ class BedrockVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
         if isinstance(query, list):
             query = " ".join(query)
 
-        url = f"{api_base}/{vector_store_id}/retrieve"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}/retrieve"
 
         request_body: Dict[str, Any] = {
             "retrievalQuery": BedrockKBRetrievalQuery(text=query),

litellm/llms/bytez/chat/transformation.py

@@ -7,6 +7,7 @@ import httpx
 
 from litellm.litellm_core_utils.exception_mapping_utils import exception_type
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
@@ -149,7 +150,7 @@ class BytezChatConfig(BaseConfig):
         litellm_params: dict,
         stream: Optional[bool] = None,
     ) -> str:
-        return f"{API_BASE}/{model}"
+        return f"{API_BASE}/{encode_url_path(model)}"
 
     def transform_request(
         self,

litellm/llms/cloudflare/chat/transformation.py

@@ -5,6 +5,7 @@ from typing import AsyncIterator, Iterator, List, Optional, Union
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
 from litellm.llms.base_llm.chat.transformation import (
     BaseConfig,
@@ -89,7 +90,7 @@ class CloudflareChatConfig(BaseConfig):
             api_base = (
                 f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/"
             )
-        return api_base + model
+        return api_base + encode_url_path(model)
 
     def get_supported_openai_params(self, model: str) -> List[str]:
         return [

litellm/llms/custom_httpx/container_handler.py

@@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Type, Union
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     HTTPHandler,
@@ -72,7 +73,9 @@ def _build_url(
 
     # Substitute path parameters
     for param, value in path_params.items():
-        path_template = path_template.replace(f"{{{param}}}", value)
+        path_template = path_template.replace(
+            f"{{{param}}}", encode_path_segment(value)
+        )
 
     # Parse the api_base to extract existing query params
     parsed_base = httpx.URL(api_base)

litellm/llms/custom_httpx/llm_http_handler.py

@@ -26,6 +26,7 @@ from litellm._logging import _redact_string, verbose_logger
 from litellm.anthropic_beta_headers_manager import update_headers_with_filtered_beta
 from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES
 from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.anthropic_messages.transformation import (
     BaseAnthropicMessagesConfig,
 )
@@ -8907,7 +8908,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         logging_obj.pre_call(
             input="",
@@ -8974,7 +8975,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         logging_obj.pre_call(
             input="",
@@ -9173,7 +9174,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         request_body: Dict[str, Any] = dict(vector_store_update_optional_params)
 
@@ -9256,7 +9257,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         request_body: Dict[str, Any] = dict(vector_store_update_optional_params)
 
@@ -9322,7 +9323,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         logging_obj.pre_call(
             input="",
@@ -9387,7 +9388,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url = f"{api_base}/{vector_store_id}"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}"
 
         logging_obj.pre_call(
             input="",

litellm/llms/elevenlabs/text_to_speech/transformation.py

@@ -12,6 +12,7 @@ from httpx import Headers
 
 import litellm
 from litellm.types.utils import all_litellm_params
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.text_to_speech.transformation import (
     BaseTextToSpeechConfig,
@@ -321,7 +322,7 @@ class ElevenLabsTextToSpeechConfig(BaseTextToSpeechConfig):
                 "ElevenLabs voice_id is required. Pass `voice` when calling `litellm.speech()`."
             )
 
-        url = f"{base_url}{self.TTS_ENDPOINT_PATH}/{voice_id}"
+        url = f"{base_url}{self.TTS_ENDPOINT_PATH}/{encode_path_segment(voice_id)}"
 
         query_params = litellm_params.get(self.ELEVENLABS_QUERY_PARAMS_KEY, {})
         if query_params:

litellm/llms/fireworks_ai/chat/transformation.py

@@ -10,6 +10,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
 from litellm.litellm_core_utils.llm_response_utils.get_headers import (
     get_response_headers,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import (
     AllMessageValues,
@@ -438,7 +439,7 @@ class FireworksAIConfig(OpenAIGPTConfig):
         if base.endswith("/v1"):
             base = base[: -len("/v1")]
         response = litellm.module_level_client.get(
-            url=f"{base}/v1/accounts/{account_id}/models",
+            url=f"{base}/v1/accounts/{encode_path_segment(account_id)}/models",
             headers={"Authorization": f"Bearer {api_key}"},
         )
 

litellm/llms/gemini/count_tokens/handler.py

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
 from litellm.types.utils import LlmProviders
 
@@ -54,7 +55,7 @@ class GoogleAIStudioTokenCounter:
         Construct the URL for the Google Gen AI Studio countTokens endpoint.
         """
         base_url = api_base or "https://generativelanguage.googleapis.com"
-        return f"{base_url}/v1beta/models/{model}:countTokens"
+        return f"{base_url}/v1beta/models/{encode_url_path(model)}:countTokens"
 
     async def validate_environment(
         self,

litellm/llms/gemini/files/transformation.py

@@ -13,6 +13,7 @@ from openai.types.file_deleted import FileDeleted
 
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.files.transformation import (
     BaseFilesConfig,
     LiteLLMLoggingObj,
@@ -232,7 +233,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
         )
         api_base = api_base.rstrip("/")
 
-        url = f"{api_base}/v1beta/{file_part}"
+        url = f"{api_base}/v1beta/{encode_url_path(file_part)}"
 
         # API key is passed via x-goog-api-key header (set in validate_environment)
         return url, {}
@@ -346,7 +347,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig):
             file_name = file_id if file_id.startswith("files/") else f"files/{file_id}"
 
         # Construct the delete URL
-        url = f"{api_base}/v1beta/{file_name}"
+        url = f"{api_base}/v1beta/{encode_url_path(file_name)}"
 
         # Add API key as header (Google AI Studio uses x-goog-api-key header)
         params: dict = {}

litellm/llms/gemini/image_edit/transformation.py

@@ -6,6 +6,7 @@ import httpx
 from httpx._types import RequestFiles
 
 from litellm.images.utils import ImageEditRequestUtils
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.images.main import ImageEditOptionalRequestParams
@@ -79,7 +80,7 @@ class GeminiImageEditConfig(BaseImageEditConfig):
             api_base or get_secret_str("GEMINI_API_BASE") or self.DEFAULT_BASE_URL
         )
         base_url = base_url.rstrip("/")
-        return f"{base_url}/models/{model}:generateContent"
+        return f"{base_url}/models/{encode_url_path(model)}:generateContent"
 
     def transform_image_edit_request(  # type: ignore[override]
         self,

litellm/llms/gemini/image_generation/transformation.py

@@ -2,6 +2,7 @@ from typing import TYPE_CHECKING, Any, List, Optional
 
 import httpx
 
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
@@ -127,11 +128,12 @@ class GoogleImageGenConfig(BaseImageGenerationConfig):
         complete_url = complete_url.rstrip("/")
 
         # Gemini Flash Image Preview models use generateContent endpoint
+        encoded_model = encode_url_path(model)
         if "gemini" in model:
-            complete_url = f"{complete_url}/models/{model}:generateContent"
+            complete_url = f"{complete_url}/models/{encoded_model}:generateContent"
         else:
             # All other Imagen models use predict endpoint
-            complete_url = f"{complete_url}/models/{model}:predict"
+            complete_url = f"{complete_url}/models/{encoded_model}:predict"
 
         return complete_url
 

litellm/llms/gemini/interactions/transformation.py

@@ -15,6 +15,7 @@ import httpx
 
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.core_helpers import process_response_headers
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.interactions.transformation import BaseInteractionsAPIConfig
 from litellm.llms.gemini.common_utils import GeminiError, GeminiModelInfo
 from litellm.types.interactions import (
@@ -206,7 +207,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
         if not GeminiModelInfo.get_api_key(litellm_params.api_key):
             raise ValueError("Google API key is required")
         return (
-            f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}",
+            f"{resolved_api_base}/{self.api_version}/interactions/{encode_path_segment(interaction_id)}",
             {},
         )
 
@@ -239,7 +240,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
         if not GeminiModelInfo.get_api_key(litellm_params.api_key):
             raise ValueError("Google API key is required")
         return (
-            f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}",
+            f"{resolved_api_base}/{self.api_version}/interactions/{encode_path_segment(interaction_id)}",
             {},
         )
 
@@ -269,7 +270,7 @@ class GoogleAIStudioInteractionsConfig(BaseInteractionsAPIConfig):
         if not GeminiModelInfo.get_api_key(litellm_params.api_key):
             raise ValueError("Google API key is required")
         return (
-            f"{resolved_api_base}/{self.api_version}/interactions/{interaction_id}:cancel",
+            f"{resolved_api_base}/{self.api_version}/interactions/{encode_path_segment(interaction_id)}:cancel",
             {},
         )
 

litellm/llms/gemini/vector_stores/transformation.py

@@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
 import httpx
 
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
 from litellm.llms.gemini.common_utils import (
     GeminiError,
@@ -137,7 +138,7 @@ class GeminiVectorStoreConfig(BaseVectorStoreConfig):
         api_key = litellm_params.get("api_key") or GeminiModelInfo.get_api_key()
         if not api_key:
             raise ValueError("GEMINI_API_KEY or GOOGLE_API_KEY is required")
-        url = f"{api_base}/models/{model}:generateContent"
+        url = f"{api_base}/models/{encode_url_path(model)}:generateContent"
 
         # Build file_search tool configuration (using snake_case as per Gemini docs)
         file_search_config: Dict[str, Any] = {

litellm/llms/gemini/videos/transformation.py

@@ -7,6 +7,7 @@ from httpx._types import RequestFiles
 import litellm
 from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
 from litellm.images.utils import ImageEditRequestUtils
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.gemini import (
@@ -245,7 +246,7 @@ class GeminiVideoConfig(BaseVideoConfig):
             return api_base.rstrip("/")
 
         model_name = model.replace("gemini/", "")
-        url = f"{api_base.rstrip('/')}/v1beta/models/{model_name}:predictLongRunning"
+        url = f"{api_base.rstrip('/')}/v1beta/models/{encode_url_path(model_name)}:predictLongRunning"
 
         return url
 
@@ -376,7 +377,7 @@ class GeminiVideoConfig(BaseVideoConfig):
         GET https://generativelanguage.googleapis.com/v1beta/{operation_name}
         """
         operation_name = extract_original_video_id(video_id)
-        url = f"{api_base.rstrip('/')}/v1beta/{operation_name}"
+        url = f"{api_base.rstrip('/')}/v1beta/{encode_url_path(operation_name)}"
         params: Dict[str, Any] = {}
 
         return url, params
@@ -451,7 +452,7 @@ class GeminiVideoConfig(BaseVideoConfig):
         """
         operation_name = extract_original_video_id(video_id)
 
-        status_url = f"{api_base.rstrip('/')}/v1beta/{operation_name}"
+        status_url = f"{api_base.rstrip('/')}/v1beta/{encode_url_path(operation_name)}"
         client = litellm.module_level_client
         status_response = client.get(url=status_url, headers=headers)
         status_response.raise_for_status()

litellm/llms/huggingface/chat/transformation.py

@@ -13,6 +13,7 @@ if TYPE_CHECKING:
 else:
     LoggingClass = Any
 
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
@@ -114,7 +115,7 @@ class HuggingFaceChatConfig(OpenAIGPTConfig):
             if "/" in remaining:
                 provider = first_part
                 if provider == "hf-inference":
-                    route = f"{provider}/models/{model}/v1/chat/completions"
+                    route = f"{provider}/models/{encode_url_path(model)}/v1/chat/completions"
                 elif provider == "novita":
                     route = f"{provider}/v3/openai/chat/completions"
                 elif provider == "fireworks-ai":

litellm/llms/huggingface/common_utils.py

@@ -4,6 +4,7 @@ from typing import Literal, Optional, Union
 
 import httpx
 
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 
 HF_HUB_URL = "https://huggingface.co"
@@ -76,7 +77,7 @@ def _fetch_inference_provider_mapping(model: str) -> dict:
     if os.getenv("HUGGINGFACE_API_KEY"):
         headers["Authorization"] = f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"
 
-    path = f"{HF_HUB_URL}/api/models/{model}"
+    path = f"{HF_HUB_URL}/api/models/{encode_url_path(model)}"
     params = {"expand": ["inferenceProviderMapping"]}
 
     try:

litellm/llms/huggingface/embedding/handler.py

@@ -6,6 +6,7 @@ import httpx
 
 import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     HTTPHandler,
@@ -40,7 +41,7 @@ def get_hf_task_embedding_for_model(
             )
     http_client = HTTPHandler(concurrent_limit=1)
 
-    model_info = http_client.get(url=f"{api_base}/api/models/{model}")
+    model_info = http_client.get(url=f"{api_base}/api/models/{encode_url_path(model)}")
 
     model_info_dict = model_info.json()
 
@@ -65,7 +66,9 @@ async def async_get_hf_task_embedding_for_model(
         llm_provider=litellm.LlmProviders.HUGGINGFACE,
     )
 
-    model_info = await http_client.get(url=f"{api_base}/api/models/{model}")
+    model_info = await http_client.get(
+        url=f"{api_base}/api/models/{encode_url_path(model)}"
+    )
 
     model_info_dict = model_info.json()
 

litellm/llms/huggingface/embedding/transformation.py

@@ -15,6 +15,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     prompt_factory,
 )
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
@@ -345,7 +346,9 @@ class HuggingFaceEmbeddingConfig(BaseConfig):
         elif "HUGGINGFACE_API_BASE" in os.environ:
             completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
         else:
-            completion_url = f"https://api-inference.huggingface.co/models/{model}"
+            completion_url = (
+                f"https://api-inference.huggingface.co/models/{encode_url_path(model)}"
+            )
 
         return completion_url
 

litellm/llms/manus/files/transformation.py

@@ -19,6 +19,7 @@ from openai.types.file_deleted import FileDeleted
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.files.transformation import (
     BaseFilesConfig,
@@ -306,7 +307,7 @@ class ManusFilesConfig(BaseFilesConfig):
             optional_params=optional_params,
             litellm_params=litellm_params,
         )
-        return f"{api_base}/{file_id}", {}
+        return f"{api_base}/{encode_path_segment(file_id)}", {}
 
     def transform_retrieve_file_response(
         self,
@@ -336,7 +337,7 @@ class ManusFilesConfig(BaseFilesConfig):
             optional_params=optional_params,
             litellm_params=litellm_params,
         )
-        return f"{api_base}/{file_id}", {}
+        return f"{api_base}/{encode_path_segment(file_id)}", {}
 
     def transform_delete_file_response(
         self,
@@ -422,7 +423,7 @@ class ManusFilesConfig(BaseFilesConfig):
             optional_params=optional_params,
             litellm_params=litellm_params,
         )
-        return f"{api_base}/{file_id}/content", {}
+        return f"{api_base}/{encode_path_segment(file_id)}/content", {}
 
     def transform_file_content_response(
         self,

litellm/llms/manus/responses/transformation.py

@@ -9,6 +9,7 @@ from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
     _safe_convert_created_field,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.openai.common_utils import OpenAIError
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
@@ -270,7 +271,7 @@ class ManusResponsesAPIConfig(OpenAIResponsesAPIConfig):
 
         Reference: https://open.manus.im/docs/openai-compatibility
         """
-        url = f"{api_base}/{response_id}"
+        url = f"{api_base}/{encode_path_segment(response_id)}"
         data: Dict = {}
         return url, data
 

litellm/llms/nvidia_nim/rerank/transformation.py

@@ -6,6 +6,7 @@ from typing_extensions import Required, TypedDict
 import litellm
 from litellm._uuid import uuid
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm._url_utils import encode_url_path
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
 from litellm.secret_managers.main import get_secret_str
@@ -92,7 +93,7 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
         # Strip nvidia_nim/ prefix from model name if present
         clean_model = self._get_clean_model_name(model)
 
-        return f"{api_base}/v1/retrieval/{clean_model}/reranking"
+        return f"{api_base}/v1/retrieval/{encode_url_path(clean_model)}/reranking"
 
     def get_supported_cohere_rerank_params(self, model: str) -> list:
         """

litellm/llms/openai/containers/transformation.py

@@ -16,6 +16,7 @@ from litellm.types.containers.main import (
 )
 from litellm.types.router import GenericLiteLLMParams
 
+from ...base_llm._url_utils import encode_path_segment
 from ...base_llm.containers.transformation import BaseContainerConfig
 from .utils import join_container_api_base_path
 
@@ -198,7 +199,9 @@ class OpenAIContainerConfig(BaseContainerConfig):
     ) -> Tuple[str, Dict]:
         """Transform the OpenAI container retrieve request."""
         # For container retrieve, we just need to construct the URL
-        url = join_container_api_base_path(api_base, f"/{container_id}")
+        url = join_container_api_base_path(
+            api_base, f"/{encode_path_segment(container_id)}"
+        )
 
         # No additional data needed for GET request
         data: Dict[str, Any] = {}
@@ -230,7 +233,9 @@ class OpenAIContainerConfig(BaseContainerConfig):
         - DELETE /v1/containers/{container_id}
         """
         # Construct the URL for container delete
-        url = join_container_api_base_path(api_base, f"/{container_id}")
+        url = join_container_api_base_path(
+            api_base, f"/{encode_path_segment(container_id)}"
+        )
 
         # No data needed for DELETE request
         data: Dict[str, Any] = {}
@@ -267,7 +272,9 @@ class OpenAIContainerConfig(BaseContainerConfig):
         - GET /v1/containers/{container_id}/files
         """
         # Construct the URL for container files
-        url = join_container_api_base_path(api_base, f"/{container_id}/files")
+        url = join_container_api_base_path(
+            api_base, f"/{encode_path_segment(container_id)}/files"
+        )
 
         # Prepare query parameters
         params: Dict[str, Any] = {}
@@ -312,7 +319,8 @@ class OpenAIContainerConfig(BaseContainerConfig):
         """
         # Construct the URL for container file content
         url = join_container_api_base_path(
-            api_base, f"/{container_id}/files/{file_id}/content"
+            api_base,
+            f"/{encode_path_segment(container_id)}/files/{encode_path_segment(file_id)}/content",
         )
 
         # No query parameters needed

litellm/llms/openai/evals/transformation.py

@@ -7,6 +7,7 @@ from typing import Any, Dict, Optional, Tuple
 import httpx
 
 from litellm._logging import verbose_logger
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.evals.transformation import (
     BaseEvalsAPIConfig,
     LiteLLMLoggingObj,
@@ -76,7 +77,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
             api_base = "https://api.openai.com"
 
         if eval_id:
-            return f"{api_base}/v1/evals/{eval_id}"
+            return f"{api_base}/v1/evals/{encode_path_segment(eval_id)}"
         return f"{api_base}/v1/{endpoint}"
 
     def transform_create_eval_request(
@@ -276,7 +277,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
         if litellm_params and litellm_params.api_base:
             api_base = litellm_params.api_base
 
-        url = f"{api_base}/v1/evals/{eval_id}/runs"
+        url = f"{api_base}/v1/evals/{encode_path_segment(eval_id)}/runs"
 
         # Build request body
         request_body = {k: v for k, v in create_request.items() if v is not None}
@@ -310,7 +311,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
         if litellm_params and litellm_params.api_base:
             api_base = litellm_params.api_base
 
-        url = f"{api_base}/v1/evals/{eval_id}/runs"
+        url = f"{api_base}/v1/evals/{encode_path_segment(eval_id)}/runs"
 
         # Build query parameters
         query_params: Dict[str, Any] = {}
@@ -350,7 +351,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
         headers: dict,
     ) -> Tuple[str, Dict]:
         """Transform get run request for OpenAI"""
-        url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}"
+        url = f"{api_base}/v1/evals/{encode_path_segment(eval_id)}/runs/{encode_path_segment(run_id)}"
 
         verbose_logger.debug("Get run request - URL: %s", url)
 
@@ -376,7 +377,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
         headers: dict,
     ) -> Tuple[str, Dict, Dict]:
         """Transform cancel run request for OpenAI"""
-        url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}/cancel"
+        url = f"{api_base}/v1/evals/{encode_path_segment(eval_id)}/runs/{encode_path_segment(run_id)}/cancel"
 
         # Empty body for cancel request
         request_body: Dict[str, Any] = {}
@@ -405,7 +406,7 @@ class OpenAIEvalsConfig(BaseEvalsAPIConfig):
         headers: dict,
     ) -> Tuple[str, Dict, Dict]:
         """Transform delete run request for OpenAI"""
-        url = f"{api_base}/v1/evals/{eval_id}/runs/{run_id}"
+        url = f"{api_base}/v1/evals/{encode_path_segment(eval_id)}/runs/{encode_path_segment(run_id)}"
 
         # Empty body for delete request
         request_body: Dict[str, Any] = {}

litellm/llms/openai/responses/transformation.py

@@ -10,6 +10,7 @@ from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
     _safe_convert_created_field,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import *
@@ -421,7 +422,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         OpenAI API expects the following request
         - DELETE /v1/responses/{response_id}
         """
-        url = f"{api_base}/{response_id}"
+        url = f"{api_base}/{encode_path_segment(response_id)}"
         data: Dict = {}
         return url, data
 
@@ -457,7 +458,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         OpenAI API expects the following request
         - GET /v1/responses/{response_id}
         """
-        url = f"{api_base}/{response_id}"
+        url = f"{api_base}/{encode_path_segment(response_id)}"
         data: Dict = {}
         return url, data
 
@@ -498,7 +499,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         limit: int = 20,
         order: Literal["asc", "desc"] = "desc",
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{response_id}/input_items"
+        url = f"{api_base}/{encode_path_segment(response_id)}/input_items"
         params: Dict[str, Any] = {}
         if after is not None:
             params["after"] = after
@@ -540,7 +541,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
         OpenAI API expects the following request
         - POST /v1/responses/{response_id}/cancel
         """
-        url = f"{api_base}/{response_id}/cancel"
+        url = f"{api_base}/{encode_path_segment(response_id)}/cancel"
         data: Dict = {}
         return url, data
 

litellm/llms/openai/vector_store_files/transformation.py

@@ -3,6 +3,7 @@ from typing import Any, Dict, Optional, Tuple, cast
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.vector_store_files.transformation import (
     BaseVectorStoreFilesConfig,
 )
@@ -98,7 +99,7 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig):
             or "https://api.openai.com/v1"
         )
         base_url = base_url.rstrip("/")
-        return f"{base_url}/vector_stores/{vector_store_id}/files"
+        return f"{base_url}/vector_stores/{encode_path_segment(vector_store_id)}/files"
 
     def transform_create_vector_store_file_request(
         self,
@@ -163,7 +164,7 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig):
         file_id: str,
         api_base: str,
     ) -> Tuple[str, Dict[str, Any]]:
-        return f"{api_base}/{file_id}", {}
+        return f"{api_base}/{encode_path_segment(file_id)}", {}
 
     def transform_retrieve_vector_store_file_response(
         self,
@@ -186,7 +187,7 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig):
         file_id: str,
         api_base: str,
     ) -> Tuple[str, Dict[str, Any]]:
-        return f"{api_base}/{file_id}/content", {}
+        return f"{api_base}/{encode_path_segment(file_id)}/content", {}
 
     def transform_retrieve_vector_store_file_content_response(
         self,
@@ -218,7 +219,7 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig):
                 payload["attributes"] = filtered_attributes
             else:
                 payload.pop("attributes", None)
-        return f"{api_base}/{file_id}", payload
+        return f"{api_base}/{encode_path_segment(file_id)}", payload
 
     def transform_update_vector_store_file_response(
         self,
@@ -241,7 +242,7 @@ class OpenAIVectorStoreFilesConfig(BaseVectorStoreFilesConfig):
         file_id: str,
         api_base: str,
     ) -> Tuple[str, Dict[str, Any]]:
-        return f"{api_base}/{file_id}", {}
+        return f"{api_base}/{encode_path_segment(file_id)}", {}
 
     def transform_delete_vector_store_file_response(
         self,

litellm/llms/openai/vector_stores/transformation.py

@@ -3,6 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.router import GenericLiteLLMParams
@@ -107,7 +108,7 @@ class OpenAIVectorStoreConfig(BaseVectorStoreConfig):
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{vector_store_id}/search"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}/search"
         typed_request_body = VectorStoreSearchRequest(
             query=query,
             filters=vector_store_search_optional_params.get("filters", None),

litellm/llms/openai/videos/transformation.py

@@ -1,11 +1,13 @@
 import mimetypes
 from io import BufferedReader, BytesIO
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
+from urllib.parse import quote
 
 import httpx
 from httpx._types import RequestFiles
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.llms.openai.image_edit.transformation import ImageEditRequestUtils
 from litellm.secret_managers.main import get_secret_str
@@ -222,9 +224,9 @@ class OpenAIVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Construct the URL for video content download
-        url = f"{api_base.rstrip('/')}/{original_video_id}/content"
+        url = f"{api_base.rstrip('/')}/{encode_path_segment(original_video_id)}/content"
         if variant is not None:
-            url = f"{url}?variant={variant}"
+            url = f"{url}?variant={quote(str(variant), safe='')}"
 
         # No additional data needed for GET content request
         data: Dict[str, Any] = {}
@@ -249,7 +251,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Construct the URL for video remix
-        url = f"{api_base.rstrip('/')}/{original_video_id}/remix"
+        url = f"{api_base.rstrip('/')}/{encode_path_segment(original_video_id)}/remix"
 
         # Prepare the request data
         data = {"prompt": prompt}
@@ -393,7 +395,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Construct the URL for video delete
-        url = f"{api_base.rstrip('/')}/{original_video_id}"
+        url = f"{api_base.rstrip('/')}/{encode_path_segment(original_video_id)}"
 
         # No data needed for DELETE request
         data: Dict[str, Any] = {}
@@ -429,7 +431,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # For video retrieve, we just need to construct the URL
-        url = f"{api_base.rstrip('/')}/{original_video_id}"
+        url = f"{api_base.rstrip('/')}/{encode_path_segment(original_video_id)}"
 
         # No additional data needed for GET request
         data: Dict[str, Any] = {}
@@ -494,7 +496,7 @@ class OpenAIVideoConfig(BaseVideoConfig):
         litellm_params: GenericLiteLLMParams,
         headers: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base.rstrip('/')}/characters/{character_id}"
+        url = f"{api_base.rstrip('/')}/characters/{encode_path_segment(character_id)}"
         return url, {}
 
     def transform_video_get_character_response(

litellm/llms/pg_vector/vector_stores/transformation.py

@@ -1,5 +1,6 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.openai.vector_stores.transformation import OpenAIVectorStoreConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.router import GenericLiteLLMParams
@@ -81,7 +82,7 @@ class PGVectorStoreConfig(OpenAIVectorStoreConfig):
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{vector_store_id}/search"
+        url = f"{api_base}/{encode_path_segment(vector_store_id)}/search"
         _, request_body = super().transform_search_vector_store_request(
             vector_store_id=vector_store_id,
             query=query,

litellm/llms/predibase/chat/handler.py

@@ -17,6 +17,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     custom_prompt,
     prompt_factory,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
     get_async_httpx_client,
@@ -254,7 +255,7 @@ class PredibaseChatCompletion:
         elif "PREDIBASE_API_BASE" in os.environ:
             base_url = os.getenv("PREDIBASE_API_BASE", "")
 
-        completion_url = f"{base_url}/{tenant_id}/deployments/v2/llms/{model}"
+        completion_url = f"{base_url}/{encode_path_segment(tenant_id)}/deployments/v2/llms/{encode_path_segment(model)}"
 
         if optional_params.get("stream", False) is True:
             completion_url += "/generate_stream"

litellm/llms/ragflow/chat/transformation.py

@@ -13,6 +13,7 @@ Model name format:
 from typing import List, Optional, Tuple
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.openai.openai import OpenAIConfig
 from litellm.secret_managers.main import get_secret, get_secret_str
 from litellm.types.llms.openai import AllMessageValues
@@ -126,14 +127,11 @@ class RAGFlowConfig(OpenAIConfig):
             api_base = api_base[:-3]  # Remove /v1
 
         # Construct the RAGFlow-specific path
+        encoded_entity_id = encode_path_segment(entity_id)
         if endpoint_type == "chat":
-            path = f"/api/v1/chats_openai/{entity_id}/chat/completions"
+            path = f"/api/v1/chats_openai/{encoded_entity_id}/chat/completions"
         else:  # agent
-            path = f"/api/v1/agents_openai/{entity_id}/chat/completions"
-
-        # Ensure path starts with /
-        if not path.startswith("/"):
-            path = "/" + path
+            path = f"/api/v1/agents_openai/{encoded_entity_id}/chat/completions"
 
         return f"{api_base}{path}"
 

litellm/llms/runwayml/image_generation/transformation.py

@@ -9,6 +9,7 @@ from litellm.constants import (
     RUNWAYML_DEFAULT_API_VERSION,
     RUNWAYML_POLLING_TIMEOUT,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
@@ -222,7 +223,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig):
 
         # Build task status URL
         api_base = api_base.rstrip("/")
-        task_url = f"{api_base}/v1/tasks/{task_id}"
+        task_url = f"{api_base}/v1/tasks/{encode_path_segment(task_id)}"
 
         verbose_logger.debug(f"Polling RunwayML task: {task_url}")
 
@@ -271,7 +272,7 @@ class RunwayMLImageGenerationConfig(BaseImageGenerationConfig):
 
         # Build task status URL
         api_base = api_base.rstrip("/")
-        task_url = f"{api_base}/v1/tasks/{task_id}"
+        task_url = f"{api_base}/v1/tasks/{encode_path_segment(task_id)}"
 
         verbose_logger.debug(f"Polling RunwayML task (async): {task_url}")
 

litellm/llms/runwayml/text_to_speech/transformation.py

@@ -16,6 +16,7 @@ from litellm.constants import (
     RUNWAYML_DEFAULT_API_VERSION,
     RUNWAYML_POLLING_TIMEOUT,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.text_to_speech.transformation import (
     BaseTextToSpeechConfig,
     TextToSpeechRequestData,
@@ -312,7 +313,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig):
 
         # Build task status URL
         api_base = api_base.rstrip("/")
-        task_url = f"{api_base}/v1/tasks/{task_id}"
+        task_url = f"{api_base}/v1/tasks/{encode_path_segment(task_id)}"
 
         verbose_logger.debug(f"Polling RunwayML TTS task: {task_url}")
 
@@ -360,7 +361,7 @@ class RunwayMLTextToSpeechConfig(BaseTextToSpeechConfig):
 
         # Build task status URL
         api_base = api_base.rstrip("/")
-        task_url = f"{api_base}/v1/tasks/{task_id}"
+        task_url = f"{api_base}/v1/tasks/{encode_path_segment(task_id)}"
 
         verbose_logger.debug(f"Polling RunwayML TTS task (async): {task_url}")
 

litellm/llms/runwayml/videos/transformation.py

@@ -6,6 +6,7 @@ from httpx._types import RequestFiles
 
 import litellm
 from litellm.constants import RUNWAYML_DEFAULT_API_VERSION
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.llms.custom_httpx.http_handler import (
@@ -336,7 +337,7 @@ class RunwayMLVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Get task status to retrieve video URL
-        url = f"{api_base}/tasks/{original_video_id}"
+        url = f"{api_base}/tasks/{encode_path_segment(original_video_id)}"
 
         params: Dict[str, Any] = {}
 
@@ -497,7 +498,7 @@ class RunwayMLVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Construct the URL for task cancellation
-        url = f"{api_base}/tasks/{original_video_id}/cancel"
+        url = f"{api_base}/tasks/{encode_path_segment(original_video_id)}/cancel"
 
         data: Dict[str, Any] = {}
 
@@ -535,7 +536,7 @@ class RunwayMLVideoConfig(BaseVideoConfig):
         original_video_id = extract_original_video_id(video_id)
 
         # Construct the full URL for task status retrieval
-        url = f"{api_base}/tasks/{original_video_id}"
+        url = f"{api_base}/tasks/{encode_path_segment(original_video_id)}"
 
         # Empty dict for GET request (no body)
         data: Dict[str, Any] = {}

litellm/llms/vertex_ai/batches/handler.py

@@ -4,6 +4,7 @@ from typing import Any, Coroutine, Dict, Optional, Union
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import (
     _get_httpx_client,
     get_async_httpx_client,
@@ -169,7 +170,7 @@ class VertexAIBatchPrediction(VertexLLM):
         )
 
         # Append batch_id to the URL
-        default_api_base = f"{default_api_base}/{batch_id}"
+        default_api_base = f"{default_api_base}/{encode_path_segment(batch_id)}"
 
         if len(default_api_base.split(":")) > 1:
             endpoint = default_api_base.split(":")[-1]
@@ -401,7 +402,9 @@ class VertexAIBatchPrediction(VertexLLM):
             vertex_project=vertex_project or project_id,
         )
 
-        retrieve_api_base_default = f"{default_api_base}/{batch_id}"
+        retrieve_api_base_default = (
+            f"{default_api_base}/{encode_path_segment(batch_id)}"
+        )
         cancel_api_base_default = f"{retrieve_api_base_default}:cancel"
 
         _, api_base = self._check_custom_proxy(

litellm/llms/vertex_ai/common_utils.py

@@ -9,6 +9,7 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
 from litellm.litellm_core_utils.prompt_templates.common_utils import unpack_defs
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 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
@@ -275,11 +276,14 @@ def _get_embedding_url(
     endpoint = "embedContent" if uses_embed_content else "predict"
 
     base_url = get_vertex_base_url(vertex_location)
+    proj = encode_path_segment(vertex_project)
+    loc = encode_path_segment(vertex_location)
+    model_seg = encode_url_path(model)
 
     if model.isdigit():
-        url = f"{base_url}/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
+        url = f"{base_url}/{vertex_api_version}/projects/{proj}/locations/{loc}/endpoints/{model_seg}:{endpoint}"
     else:
-        url = f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+        url = f"{base_url}/v1/projects/{proj}/locations/{loc}/publishers/google/models/{model_seg}:{endpoint}"
 
     return url, endpoint
 
@@ -297,6 +301,10 @@ def _get_vertex_url(
 
     model = litellm.VertexGeminiConfig.get_model_for_vertex_ai_url(model=model)
 
+    proj = encode_path_segment(vertex_project)
+    loc = encode_path_segment(vertex_location)
+    model_seg = encode_url_path(model)
+
     if mode == "chat":
         ### SET RUNTIME ENDPOINT ###
         endpoint = "generateContent"
@@ -310,10 +318,10 @@ def _get_vertex_url(
         # send to this url: url = f"{base_url}/{version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
         if model.isdigit():
             # It's a fine-tuned Gemini model - use endpoints/ path
-            url = f"{base_url}/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
+            url = f"{base_url}/{vertex_api_version}/projects/{proj}/locations/{loc}/endpoints/{model_seg}:{endpoint}"
         else:
             # Regular model - use publishers/google/models/ path
-            url = f"{base_url}/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+            url = f"{base_url}/{vertex_api_version}/projects/{proj}/locations/{loc}/publishers/google/models/{model_seg}:{endpoint}"
 
         if stream is True:
             url += "?alt=sse"
@@ -329,14 +337,14 @@ def _get_vertex_url(
         base_url = get_vertex_base_url(vertex_location)
         if model.isdigit():
             # Numeric model -> custom endpoint
-            url = f"{base_url}/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/endpoints/{model}:{endpoint}"
+            url = f"{base_url}/{vertex_api_version}/projects/{proj}/locations/{loc}/endpoints/{model_seg}:{endpoint}"
         else:
             # Regular model -> publisher model
-            url = f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+            url = f"{base_url}/v1/projects/{proj}/locations/{loc}/publishers/google/models/{model_seg}:{endpoint}"
     elif mode == "count_tokens":
         endpoint = "countTokens"
         base_url = get_vertex_base_url(vertex_location)
-        url = f"{base_url}/{vertex_api_version}/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model}:{endpoint}"
+        url = f"{base_url}/{vertex_api_version}/projects/{proj}/locations/{loc}/publishers/google/models/{model_seg}:{endpoint}"
     if not url or not endpoint:
         raise ValueError(f"Unable to get vertex url/endpoint for mode: {mode}")
     return url, endpoint
@@ -357,7 +365,7 @@ def _get_gemini_url(
         VertexGeminiConfig,
     )
 
-    _gemini_model_name = "models/{}".format(model)
+    _gemini_model_name = "models/{}".format(encode_url_path(model))
     api_version = (
         "v1alpha" if VertexGeminiConfig._is_gemini_3_or_newer(model) else "v1beta"
     )

litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py

@@ -9,6 +9,7 @@ from httpx._types import RequestFiles
 
 import litellm
 from litellm.images.utils import ImageEditRequestUtils
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
 from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
@@ -161,7 +162,7 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM):
 
         base_url = get_vertex_base_url(vertex_location)
 
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:generateContent"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/google/models/{encode_url_path(model_name)}:generateContent"
 
     def transform_image_edit_request(  # type: ignore[override]
         self,

litellm/llms/vertex_ai/image_edit/vertex_imagen_transformation.py

@@ -10,6 +10,7 @@ from httpx._types import RequestFiles
 
 import litellm
 from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
 from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
 from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
@@ -161,7 +162,7 @@ class VertexAIImagenImageEditConfig(BaseImageEditConfig, VertexLLM):
         else:
             base_url = get_vertex_base_url(vertex_location)
 
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:predict"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/google/models/{encode_url_path(model_name)}:predict"
 
     def transform_image_edit_request(  # type: ignore[override]
         self,

litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
@@ -162,7 +163,7 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM):
 
         base_url = get_vertex_base_url(vertex_location)
 
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:generateContent"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/google/models/{encode_url_path(model_name)}:generateContent"
 
     def validate_environment(
         self,

litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Any, List, Optional
 import httpx
 
 import litellm
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.image_generation.transformation import (
     BaseImageGenerationConfig,
 )
@@ -148,7 +149,7 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM):
 
         base_url = get_vertex_base_url(vertex_location)
 
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:predict"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/google/models/{encode_url_path(model_name)}:predict"
 
     def validate_environment(
         self,

litellm/llms/vertex_ai/ocr/deepseek_transformation.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Any, Dict, Optional
 import httpx
 
 from litellm._logging import verbose_logger
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.base_llm.ocr.transformation import (
     BaseOCRConfig,
     DocumentType,
@@ -125,7 +126,7 @@ class VertexAIDeepSeekOCRConfig(BaseOCRConfig):
 
         # Vertex AI DeepSeek OCR endpoint format
         # Format: https://{region}-aiplatform.googleapis.com/v1/projects/{project}/locations/{region}/endpoints/openapi/chat/completions
-        return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi/chat/completions"
+        return f"{api_base}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/endpoints/openapi/chat/completions"
 
     def transform_ocr_request(
         self,

litellm/llms/vertex_ai/ocr/transformation.py

@@ -9,6 +9,7 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import (
     async_convert_url_to_base64,
     convert_url_to_base64,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.ocr.transformation import DocumentType, OCRRequestData
 from litellm.llms.mistral.ocr.transformation import MistralOCRConfig
 from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
@@ -121,7 +122,7 @@ class VertexAIOCRConfig(MistralOCRConfig):
 
         # Vertex AI OCR endpoint format for Mistral publisher
         # Format: https://{region}-aiplatform.googleapis.com/v1/projects/{project}/locations/{region}/publishers/mistralai/models/{model}:rawPredict
-        return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/mistralai/models/{model}:rawPredict"
+        return f"{api_base}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/mistralai/models/{encode_url_path(model)}:rawPredict"
 
     def _convert_url_to_data_uri_sync(self, url: str) -> str:
         """

litellm/llms/vertex_ai/rag_engine/transformation.py

@@ -8,6 +8,7 @@ from typing import Any, Dict, Optional
 
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_SIZE
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
 from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
 from litellm.types.rag import RAGChunkingStrategy
@@ -39,7 +40,7 @@ class VertexAIRAGTransformation(VertexBase):
         Vertex AI RAG Engine primarily uses gRPC-based SDK.
         """
         base_url = get_vertex_base_url(vertex_location)
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{corpus_id}:importRagFiles"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/ragCorpora/{encode_path_segment(corpus_id)}:importRagFiles"
 
     def get_retrieve_contexts_url(
         self,
@@ -48,7 +49,7 @@ class VertexAIRAGTransformation(VertexBase):
     ) -> str:
         """Get the URL for retrieving contexts (search)."""
         base_url = get_vertex_base_url(vertex_location)
-        return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}:retrieveContexts"
+        return f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}:retrieveContexts"
 
     def transform_chunking_strategy_to_vertex_format(
         self,

litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py

@@ -8,6 +8,7 @@ their respective publisher-specific count-tokens endpoints.
 
 from typing import Any, Dict, Optional
 
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
 from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
 from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
@@ -72,10 +73,7 @@ class VertexAIPartnerModelsTokenCounter(VertexBase):
 
         # Construct the count-tokens endpoint
         # Format: /v1/projects/{project}/locations/{location}/publishers/{publisher}/models/count-tokens:rawPredict
-        endpoint = (
-            f"{base_url}/v1/projects/{project_id}/locations/{vertex_location}/"
-            f"publishers/{publisher}/models/count-tokens:rawPredict"
-        )
+        endpoint = f"{base_url}/v1/projects/{encode_path_segment(project_id)}/locations/{encode_path_segment(vertex_location)}/publishers/{encode_path_segment(publisher)}/models/count-tokens:rawPredict"
 
         return endpoint
 

litellm/llms/vertex_ai/vertex_llm_base.py

@@ -11,6 +11,7 @@ from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Tuple
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.asyncify import asyncify
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES, VertexPartnerProvider
@@ -273,23 +274,20 @@ class VertexBase:
 
         if api_base is None:
             api_base = get_vertex_base_url(vertex_location)
+        proj = encode_path_segment(vertex_project)
+        loc = encode_path_segment(vertex_location)
+        model_seg = encode_url_path(model)
         if partner == VertexPartnerProvider.llama:
-            return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi/chat/completions"
+            return f"{api_base}/v1/projects/{proj}/locations/{loc}/endpoints/openapi/chat/completions"
         elif partner == VertexPartnerProvider.mistralai:
-            if stream:
-                return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/mistralai/models/{model}:streamRawPredict"
-            else:
-                return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/mistralai/models/{model}:rawPredict"
+            action = "streamRawPredict" if stream else "rawPredict"
+            return f"{api_base}/v1/projects/{proj}/locations/{loc}/publishers/mistralai/models/{model_seg}:{action}"
         elif partner == VertexPartnerProvider.ai21:
-            if stream:
-                return f"{api_base}/v1beta1/projects/{vertex_project}/locations/{vertex_location}/publishers/ai21/models/{model}:streamRawPredict"
-            else:
-                return f"{api_base}/v1beta1/projects/{vertex_project}/locations/{vertex_location}/publishers/ai21/models/{model}:rawPredict"
+            action = "streamRawPredict" if stream else "rawPredict"
+            return f"{api_base}/v1beta1/projects/{proj}/locations/{loc}/publishers/ai21/models/{model_seg}:{action}"
         elif partner == VertexPartnerProvider.claude:
-            if stream:
-                return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:streamRawPredict"
-            else:
-                return f"{api_base}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/anthropic/models/{model}:rawPredict"
+            action = "streamRawPredict" if stream else "rawPredict"
+            return f"{api_base}/v1/projects/{proj}/locations/{loc}/publishers/anthropic/models/{model_seg}:{action}"
 
     def get_complete_vertex_url(
         self,

litellm/llms/vertex_ai/videos/transformation.py

@@ -14,6 +14,7 @@ from httpx._types import RequestFiles
 
 from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS
 from litellm.images.utils import ImageEditRequestUtils
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
 from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 from litellm.llms.vertex_ai.common_utils import (
     _convert_vertex_datetime_to_openai_datetime,
@@ -238,7 +239,7 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase):
         else:
             base_url = get_vertex_base_url(vertex_location)
 
-        url = f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}"
+        url = f"{base_url}/v1/projects/{encode_path_segment(vertex_project)}/locations/{encode_path_segment(vertex_location)}/publishers/google/models/{encode_url_path(model_name)}"
 
         return url
 
@@ -406,7 +407,7 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase):
         # Construct the full URL including model ID
         # URL format: https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/publishers/google/models/MODEL:fetchPredictOperation
         # Strip trailing slashes from api_base and append model
-        url = f"{api_base.rstrip('/')}/{model}:fetchPredictOperation"
+        url = f"{api_base.rstrip('/')}/{encode_url_path(model)}:fetchPredictOperation"
 
         # Request body contains the operation name
         params = {"operationName": operation_name}

litellm/llms/volcengine/responses/transformation.py

@@ -20,6 +20,7 @@ from litellm.litellm_core_utils.core_helpers import process_response_headers
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
     _safe_convert_created_field,
 )
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import (
@@ -300,7 +301,7 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig):
         litellm_params: GenericLiteLLMParams,
         headers: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{response_id}"
+        url = f"{api_base}/{encode_path_segment(response_id)}"
         data: Dict = {}
         return url, data
 
@@ -333,7 +334,7 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig):
         litellm_params: GenericLiteLLMParams,
         headers: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{response_id}"
+        url = f"{api_base}/{encode_path_segment(response_id)}"
         data: Dict = {}
         return url, data
 
@@ -372,7 +373,7 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig):
         limit: int = 20,
         order: Literal["asc", "desc"] = "desc",
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{response_id}/input_items"
+        url = f"{api_base}/{encode_path_segment(response_id)}/input_items"
         params: Dict[str, Any] = {}
         if after is not None:
             params["after"] = after
@@ -408,7 +409,7 @@ class VolcEngineResponsesAPIConfig(OpenAIResponsesAPIConfig):
         litellm_params: GenericLiteLLMParams,
         headers: dict,
     ) -> Tuple[str, Dict]:
-        url = f"{api_base}/{response_id}/cancel"
+        url = f"{api_base}/{encode_path_segment(response_id)}/cancel"
         data: Dict = {}
         return url, data
 

litellm/rag/ingestion/gemini_ingestion.py

@@ -10,6 +10,7 @@ from __future__ import annotations
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
 
 from litellm._logging import verbose_logger
+from litellm.llms.base_llm._url_utils import encode_path_segment
 from litellm.llms.custom_httpx.http_handler import (
     get_async_httpx_client,
     httpxSpecialProvider,
@@ -231,7 +232,7 @@ class GeminiRAGIngestion(BaseRAGIngestion):
         # base_url is like: https://generativelanguage.googleapis.com/v1beta
         # We need: https://generativelanguage.googleapis.com/upload/v1beta/{store_id}:uploadToFileSearchStore
         api_base = base_url.replace("/v1beta", "")  # Get base without version
-        url = f"{api_base}/upload/v1beta/{vector_store_id}:uploadToFileSearchStore"
+        url = f"{api_base}/upload/v1beta/{encode_path_segment(vector_store_id)}:uploadToFileSearchStore"
 
         # Build request body with chunking config and metadata if provided
         request_body: Dict[str, Any] = {"displayName": filename}

tests/test_litellm/llms/base_llm/test_url_utils.py

@@ -0,0 +1,710 @@
+"""Tests for ``encode_path_segment`` / ``encode_url_path`` and provider call sites."""
+
+import pytest
+
+from litellm.llms.base_llm._url_utils import encode_path_segment, encode_url_path
+
+
+SAMPLE_INPUT = "../../v1/messages/batches"
+SAMPLE_INPUT_ENCODED = "..%2F..%2Fv1%2Fmessages%2Fbatches"
+
+
+def _assert_input_is_encoded(url: str, prefix: str) -> None:
+    assert SAMPLE_INPUT_ENCODED in url
+    tail = url.split(prefix, 1)[1]
+    assert "../" not in tail, f"raw ``../`` after {prefix!r}: {tail!r}"
+
+
+class TestEncodePathSegment:
+    def test_encodes_dot_slash_sequences(self):
+        assert encode_path_segment(SAMPLE_INPUT) == SAMPLE_INPUT_ENCODED
+
+    def test_encodes_query_and_fragment(self):
+        assert encode_path_segment("file?admin=1") == "file%3Fadmin%3D1"
+        assert encode_path_segment("file#frag") == "file%23frag"
+
+    def test_leaves_normal_ids_unchanged(self):
+        assert encode_path_segment("file-abc123") == "file-abc123"
+        assert encode_path_segment("file_abc123") == "file_abc123"
+
+    def test_rejects_bare_dotdot(self):
+        with pytest.raises(ValueError):
+            encode_path_segment("..")
+        with pytest.raises(ValueError):
+            encode_path_segment(".")
+
+    def test_rejects_none_and_empty(self):
+        with pytest.raises(ValueError, match="identifier is required"):
+            encode_path_segment(None)
+        with pytest.raises(ValueError, match="identifier is required"):
+            encode_path_segment("")
+
+
+class TestEncodeUrlPath:
+    def test_preserves_legitimate_slashes_and_at(self):
+        assert (
+            encode_url_path("@cf/meta/llama-3.1-8b-instruct")
+            == "@cf/meta/llama-3.1-8b-instruct"
+        )
+        assert encode_url_path("google/gemma-3-4b-it") == "google/gemma-3-4b-it"
+
+    def test_rejects_dotdot_segment(self):
+        with pytest.raises(ValueError):
+            encode_url_path("../../etc/passwd")
+        with pytest.raises(ValueError):
+            encode_url_path("@cf/../secret")
+
+    def test_rejects_single_dot_segment(self):
+        with pytest.raises(ValueError):
+            encode_url_path("./secret")
+
+    def test_rejects_empty_segments(self):
+        with pytest.raises(ValueError):
+            encode_url_path("foo//bar")
+        with pytest.raises(ValueError):
+            encode_url_path("/foo")
+        with pytest.raises(ValueError):
+            encode_url_path("foo/")
+
+    def test_encodes_query_fragment_and_colon(self):
+        assert encode_url_path("model?x=1") == "model%3Fx%3D1"
+        assert encode_url_path("model#frag") == "model%23frag"
+        assert encode_url_path("evil.com:80/x") == "evil.com%3A80/x"
+
+    def test_none_becomes_empty(self):
+        assert encode_url_path(None) == ""
+
+
+class TestAnthropicFilesEncoding:
+    def _config(self):
+        from litellm.llms.anthropic.files.transformation import AnthropicFilesConfig
+
+        return AnthropicFilesConfig()
+
+    def test_retrieve(self):
+        url, _ = self._config().transform_retrieve_file_request(
+            file_id=SAMPLE_INPUT,
+            optional_params={},
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/v1/files/")
+
+    def test_delete(self):
+        url, _ = self._config().transform_delete_file_request(
+            file_id=SAMPLE_INPUT,
+            optional_params={},
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/v1/files/")
+
+    def test_content(self):
+        url, _ = self._config().transform_file_content_request(
+            file_content_request={"file_id": SAMPLE_INPUT},
+            optional_params={},
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/v1/files/")
+
+    @pytest.mark.parametrize(
+        "method_name",
+        [
+            "transform_retrieve_file_request",
+            "transform_delete_file_request",
+        ],
+    )
+    def test_missing_file_id_raises(self, method_name):
+        with pytest.raises(ValueError, match="identifier is required"):
+            getattr(self._config(), method_name)(
+                file_id="", optional_params={}, litellm_params={}
+            )
+
+    def test_missing_file_id_content_raises(self):
+        with pytest.raises(ValueError, match="identifier is required"):
+            self._config().transform_file_content_request(
+                file_content_request={},  # no file_id
+                optional_params={},
+                litellm_params={},
+            )
+
+
+class TestAnthropicBatchesEncoding:
+    def test_retrieve_url(self):
+        from litellm.llms.anthropic.batches.transformation import AnthropicBatchesConfig
+
+        url = AnthropicBatchesConfig().get_retrieve_batch_url(
+            api_base="https://api.anthropic.com",
+            batch_id=SAMPLE_INPUT,
+            optional_params={},
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/batches/")
+
+
+class TestAnthropicSkillsEncoding:
+    def test_get_complete_url(self):
+        from litellm.llms.anthropic.skills.transformation import AnthropicSkillsConfig
+
+        url = AnthropicSkillsConfig().get_complete_url(
+            api_base="https://api.anthropic.com",
+            endpoint="skills",
+            skill_id=SAMPLE_INPUT,
+        )
+        _assert_input_is_encoded(url, "/v1/skills/")
+
+
+class TestOpenAIVideosEncoding:
+    def _config(self):
+        from litellm.llms.openai.videos.transformation import OpenAIVideoConfig
+        from litellm.types.router import GenericLiteLLMParams
+
+        return OpenAIVideoConfig(), GenericLiteLLMParams()
+
+    def test_content(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_video_content_request(
+            video_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/videos",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/videos/")
+
+    def test_content_variant_is_encoded(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_video_content_request(
+            video_id="vid_ok",
+            api_base="https://api.openai.com/v1/videos",
+            litellm_params=params,
+            headers={},
+            variant="bad&inject=1",
+        )
+        assert "?variant=bad%26inject%3D1" in url
+
+    def test_delete(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_video_delete_request(
+            video_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/videos",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/videos/")
+
+    def test_status_retrieve(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_video_status_retrieve_request(
+            video_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/videos",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/videos/")
+
+    def test_remix(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_video_remix_request(
+            video_id=SAMPLE_INPUT,
+            prompt="x",
+            api_base="https://api.openai.com/v1/videos",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/videos/")
+
+
+class TestOpenAIContainersEncoding:
+    def _config(self):
+        from litellm.llms.openai.containers.transformation import (
+            OpenAIContainerConfig,
+        )
+        from litellm.types.router import GenericLiteLLMParams
+
+        return OpenAIContainerConfig(), GenericLiteLLMParams()
+
+    def test_retrieve(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_container_retrieve_request(
+            container_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/containers",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/containers/")
+
+    def test_delete(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_container_delete_request(
+            container_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/containers",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/containers/")
+
+    def test_file_list(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_container_file_list_request(
+            container_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/containers",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/containers/")
+
+    def test_file_content(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_container_file_content_request(
+            container_id="cntr_ok",
+            file_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/containers",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/cntr_ok/files/")
+
+
+class TestOpenAIVectorStoresEncoding:
+    def test_search(self):
+        from litellm.llms.openai.vector_stores.transformation import (
+            OpenAIVectorStoreConfig,
+        )
+
+        url, _ = OpenAIVectorStoreConfig().transform_search_vector_store_request(
+            vector_store_id=SAMPLE_INPUT,
+            query="x",
+            vector_store_search_optional_params={},
+            api_base="https://api.openai.com/v1/vector_stores",
+            litellm_logging_obj=None,
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/v1/vector_stores/")
+
+
+class TestOpenAIVectorStoreFilesEncoding:
+    def _config(self):
+        from litellm.llms.openai.vector_store_files.transformation import (
+            OpenAIVectorStoreFilesConfig,
+        )
+
+        return OpenAIVectorStoreFilesConfig()
+
+    def test_get_complete_url(self):
+        url = self._config().get_complete_url(
+            api_base="https://api.openai.com/v1",
+            vector_store_id=SAMPLE_INPUT,
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/vector_stores/")
+
+    def test_retrieve(self):
+        url, _ = self._config().transform_retrieve_vector_store_file_request(
+            vector_store_id="vs_ok",
+            file_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/vector_stores/vs_ok/files",
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+    def test_content(self):
+        url, _ = self._config().transform_retrieve_vector_store_file_content_request(
+            vector_store_id="vs_ok",
+            file_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/vector_stores/vs_ok/files",
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+    def test_update(self):
+        url, _ = self._config().transform_update_vector_store_file_request(
+            vector_store_id="vs_ok",
+            file_id=SAMPLE_INPUT,
+            update_request={"attributes": None},
+            api_base="https://api.openai.com/v1/vector_stores/vs_ok/files",
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+    def test_delete(self):
+        url, _ = self._config().transform_delete_vector_store_file_request(
+            vector_store_id="vs_ok",
+            file_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/vector_stores/vs_ok/files",
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+
+class TestGeminiInteractionsEncoding:
+    def _config(self):
+        from litellm.llms.gemini.interactions.transformation import (
+            GoogleAIStudioInteractionsConfig,
+        )
+        from litellm.types.router import GenericLiteLLMParams
+
+        return (
+            GoogleAIStudioInteractionsConfig(),
+            GenericLiteLLMParams(api_key="sk-test"),
+        )
+
+    def test_get(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_get_interaction_request(
+            interaction_id=SAMPLE_INPUT,
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/interactions/")
+
+    def test_delete(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_delete_interaction_request(
+            interaction_id=SAMPLE_INPUT,
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/interactions/")
+
+    def test_cancel(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_cancel_interaction_request(
+            interaction_id=SAMPLE_INPUT,
+            api_base="https://generativelanguage.googleapis.com",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/interactions/")
+
+
+class TestBedrockCountTokensEncoding:
+    def _config(self):
+        from litellm.llms.bedrock.count_tokens.transformation import (
+            BedrockCountTokensConfig,
+        )
+
+        return BedrockCountTokensConfig()
+
+    def test_endpoint(self):
+        url = self._config().get_bedrock_count_tokens_endpoint(
+            model=SAMPLE_INPUT,
+            aws_region_name="us-east-1",
+            api_base=None,
+            aws_bedrock_runtime_endpoint=None,
+        )
+        _assert_input_is_encoded(url, "/model/")
+
+    def test_versioned_model_id_preserves_colon(self):
+        url = self._config().get_bedrock_count_tokens_endpoint(
+            model="amazon.nova-pro-v1:0",
+            aws_region_name="us-east-1",
+            api_base=None,
+            aws_bedrock_runtime_endpoint=None,
+        )
+        assert "/model/amazon.nova-pro-v1:0/count-tokens" in url
+
+
+class TestBedrockInvokeOpenAIEncoding:
+    def _config(self):
+        from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import (
+            AmazonBedrockOpenAIConfig,
+        )
+
+        return AmazonBedrockOpenAIConfig()
+
+    def test_versioned_model_id_preserves_colon(self):
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model="bedrock/openai/amazon.nova-pro-v1:0",
+            optional_params={"aws_region_name": "us-east-1"},
+            litellm_params={},
+            stream=False,
+        )
+        assert "/model/amazon.nova-pro-v1:0/invoke" in url
+
+    def test_traversal_input_is_encoded(self):
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base=None,
+            api_key=None,
+            model="bedrock/openai/../../foo",
+            optional_params={"aws_region_name": "us-east-1"},
+            litellm_params={},
+            stream=False,
+        )
+        assert "..%2F..%2Ffoo" in url
+        assert "/model/../" not in url
+
+
+class TestCloudflareEncoding:
+    def test_rejects_dot_segment(self, monkeypatch):
+        from litellm.llms.cloudflare.chat.transformation import CloudflareChatConfig
+
+        monkeypatch.setenv("CLOUDFLARE_ACCOUNT_ID", "acct123")
+        cfg = CloudflareChatConfig()
+        with pytest.raises(ValueError):
+            cfg.get_complete_url(
+                api_base=None,
+                api_key="x",
+                model=SAMPLE_INPUT,
+                optional_params={},
+                litellm_params={},
+                stream=False,
+            )
+
+    def test_legitimate_model_preserved(self, monkeypatch):
+        from litellm.llms.cloudflare.chat.transformation import CloudflareChatConfig
+
+        monkeypatch.setenv("CLOUDFLARE_ACCOUNT_ID", "acct123")
+        cfg = CloudflareChatConfig()
+        url = cfg.get_complete_url(
+            api_base=None,
+            api_key="x",
+            model="@cf/meta/llama-3.1-8b-instruct",
+            optional_params={},
+            litellm_params={},
+            stream=False,
+        )
+        assert url.endswith("@cf/meta/llama-3.1-8b-instruct")
+
+
+class TestBytezEncoding:
+    def test_rejects_dot_segment(self):
+        from litellm.llms.bytez.chat.transformation import BytezChatConfig
+
+        with pytest.raises(ValueError):
+            BytezChatConfig().get_complete_url(
+                api_base=None,
+                api_key="x",
+                model=SAMPLE_INPUT,
+                optional_params={},
+                litellm_params={},
+                stream=False,
+            )
+
+    def test_legitimate_model_preserved(self):
+        from litellm.llms.bytez.chat.transformation import BytezChatConfig
+
+        url = BytezChatConfig().get_complete_url(
+            api_base=None,
+            api_key="x",
+            model="google/gemma-3-4b-it",
+            optional_params={},
+            litellm_params={},
+            stream=False,
+        )
+        assert url.endswith("google/gemma-3-4b-it")
+
+
+class TestRagflowEncoding:
+    def test_rejects_dot_segment(self):
+        from litellm.llms.ragflow.chat.transformation import RAGFlowConfig
+
+        cfg = RAGFlowConfig()
+        with pytest.raises(ValueError):
+            cfg.get_complete_url(
+                api_base="http://ragflow.example",
+                api_key="x",
+                model="ragflow/chat/../../v1/messages/batches/llama",
+                optional_params={},
+                litellm_params={},
+                stream=False,
+            )
+
+    def test_query_chars_in_entity_id_are_encoded(self):
+        from litellm.llms.ragflow.chat.transformation import RAGFlowConfig
+
+        cfg = RAGFlowConfig()
+        url = cfg.get_complete_url(
+            api_base="http://ragflow.example",
+            api_key="x",
+            model="ragflow/chat/id?admin=1/llama",
+            optional_params={},
+            litellm_params={},
+            stream=False,
+        )
+        assert "id%3Fadmin%3D1" in url
+        assert "?admin=1" not in url
+
+
+class TestPGVectorEncoding:
+    def test_search(self):
+        from litellm.llms.pg_vector.vector_stores.transformation import (
+            PGVectorStoreConfig,
+        )
+
+        url, _ = PGVectorStoreConfig().transform_search_vector_store_request(
+            vector_store_id=SAMPLE_INPUT,
+            query="x",
+            vector_store_search_optional_params={},
+            api_base="http://pg.example/v1/vector_stores",
+            litellm_logging_obj=None,
+            litellm_params={},
+        )
+        _assert_input_is_encoded(url, "/v1/vector_stores/")
+
+
+class TestContainerHandlerBuildUrl:
+    def test_path_params_encoded(self):
+        from litellm.llms.custom_httpx.container_handler import _build_url
+
+        url = _build_url(
+            api_base="https://api.openai.com/v1/containers",
+            path_template="/containers/{container_id}/files/{file_id}",
+            path_params={"container_id": "cntr_ok", "file_id": SAMPLE_INPUT},
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+
+class TestOpenAIEvalsEncoding:
+    def _config(self):
+        from litellm.llms.openai.evals.transformation import OpenAIEvalsConfig
+
+        return OpenAIEvalsConfig()
+
+    def test_get_complete_url(self):
+        cfg = self._config()
+        url = cfg.get_complete_url(
+            api_base="https://api.openai.com",
+            endpoint="evals",
+            eval_id=SAMPLE_INPUT,
+        )
+        _assert_input_is_encoded(url, "/v1/evals/")
+
+    def test_get_run(self):
+        from litellm.types.router import GenericLiteLLMParams
+
+        url, _ = self._config().transform_get_run_request(
+            eval_id="eval_ok",
+            run_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com",
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/runs/")
+
+
+class TestOpenAIResponsesEncoding:
+    def _config(self):
+        from litellm.llms.openai.responses.transformation import (
+            OpenAIResponsesAPIConfig,
+        )
+        from litellm.types.router import GenericLiteLLMParams
+
+        return OpenAIResponsesAPIConfig(), GenericLiteLLMParams()
+
+    def test_delete(self):
+        cfg, params = self._config()
+        url, _ = cfg.transform_delete_response_api_request(
+            response_id=SAMPLE_INPUT,
+            api_base="https://api.openai.com/v1/responses",
+            litellm_params=params,
+            headers={},
+        )
+        _assert_input_is_encoded(url, "/v1/responses/")
+
+
+class TestManusFilesEncoding:
+    def _config(self):
+        from litellm.llms.manus.files.transformation import ManusFilesConfig
+
+        return ManusFilesConfig()
+
+    def test_retrieve(self):
+        url, _ = self._config().transform_retrieve_file_request(
+            file_id=SAMPLE_INPUT,
+            optional_params={},
+            litellm_params={"api_base": "https://api.manus.im/v1/files"},
+        )
+        _assert_input_is_encoded(url, "/files/")
+
+
+class TestAzureAIAgentsEncoding:
+    def _handler(self):
+        from litellm.llms.azure_ai.agents.handler import AzureAIAgentsHandler
+
+        return AzureAIAgentsHandler.__new__(AzureAIAgentsHandler)
+
+    def test_messages_url(self):
+        handler = self._handler()
+        url = handler._build_messages_url(
+            api_base="https://ai.example/api/projects/p",
+            thread_id=SAMPLE_INPUT,
+            api_version="2025-05-01",
+        )
+        _assert_input_is_encoded(url, "/threads/")
+
+    def test_run_status_url(self):
+        handler = self._handler()
+        url = handler._build_run_status_url(
+            api_base="https://ai.example/api/projects/p",
+            thread_id="thread_ok",
+            run_id=SAMPLE_INPUT,
+            api_version="2025-05-01",
+        )
+        _assert_input_is_encoded(url, "/runs/")
+
+
+class TestVertexVideosEncoding:
+    def test_operation_status_rejects_dot_segments(self):
+        from litellm.llms.vertex_ai.videos.transformation import (
+            VertexAIVideoConfig,
+        )
+        from litellm.types.router import GenericLiteLLMParams
+
+        cfg = VertexAIVideoConfig()
+        with pytest.raises(ValueError):
+            cfg.transform_video_status_retrieve_request(
+                video_id="../../v1/models",
+                api_base="https://aiplatform.googleapis.com",
+                litellm_params=GenericLiteLLMParams(),
+                headers={},
+            )
+
+
+class TestNvidiaNimEncoding:
+    def test_rejects_dot_segment(self):
+        from litellm.llms.nvidia_nim.rerank.transformation import (
+            NvidiaNimRerankConfig,
+        )
+
+        cfg = NvidiaNimRerankConfig()
+        with pytest.raises(ValueError):
+            cfg.get_complete_url(
+                api_base="https://integrate.api.nvidia.com/v1",
+                model="nvidia_nim/../../v1/models",
+                optional_params={},
+            )
+
+    def test_legitimate_model_preserved(self):
+        from litellm.llms.nvidia_nim.rerank.transformation import (
+            NvidiaNimRerankConfig,
+        )
+
+        url = NvidiaNimRerankConfig().get_complete_url(
+            api_base="https://integrate.api.nvidia.com",
+            model="nvidia_nim/nvidia/nv-rerankqa-mistral-4b-v3",
+            optional_params={},
+        )
+        assert url.endswith("nvidia/nv-rerankqa-mistral-4b-v3/reranking")
+
+
+class TestHuggingfaceEmbeddingEncoding:
+    def test_ids_encoded(self):
+        assert encode_url_path("BAAI/bge-large-en-v1.5") == "BAAI/bge-large-en-v1.5"
+
+
+class TestElevenLabsEncoding:
+    def test_voice_id_encoded(self):
+        from litellm.llms.elevenlabs.text_to_speech.transformation import (
+            ElevenLabsTextToSpeechConfig,
+        )
+
+        cfg = ElevenLabsTextToSpeechConfig()
+        url = cfg.get_complete_url(
+            model="eleven_multilingual_v2",
+            api_base="https://api.elevenlabs.io",
+            litellm_params={
+                cfg.ELEVENLABS_VOICE_ID_KEY: SAMPLE_INPUT,
+            },
+        )
+        assert SAMPLE_INPUT_ENCODED in url
+        assert "../" not in url.split("/text-to-speech/", 1)[1]