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Merge pull request #50463 from BerriAI/litellm_backport_50300_lts_1_108_x

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fix(guardrails): backport #50300 to lts/1.108.x
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yujonglee 2026-11-07 10:40:24 +09:00 • committed by GitHub
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7 changed files with 921 additions and 224 deletions
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132
litellm/integrations/custom_guardrail.py
View file

@ -1,6 +1,7 @@
import contextvars
import copy
import hashlib
import json
import os
import secrets
from collections.abc import Iterable, Mapping, Sequence
@ -9,6 +10,7 @@ from types import MappingProxyType
from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, Optional, cast, get_args
import httpx
from pydantic import BaseModel, TypeAdapter
from litellm._internal_context import with_service_target
from litellm._logging import verbose_logger
@ -71,6 +73,7 @@ GUARDRAIL_SESSIONS_TARGET: Final = "guardrail_sessions"
_PRE_CALL_EXECUTED_TOKEN: Final = secrets.token_hex(16)
_PRE_CALL_RAISED_TOKEN: Final = secrets.token_hex(16)
_DEPLOYMENT_HOOK_RAN_TOKEN: Final = secrets.token_hex(16)
_POST_CALL_CHECKED_TOKEN: Final = secrets.token_hex(16)
_GUARDRAIL_BLOCK_STATUS_CODES: Final = frozenset({400, 403, 422})
@ -239,6 +242,98 @@ def _request_carries_marker(data: Mapping[str, object], marker: str | None) -> b
return False
def _record_guardrail_marker(
data: dict[str, object], # mutable-ok: the request payload the marker is recorded on
marker: str | None,
) -> None:
if marker is None:
return
for meta_key in ("metadata", "litellm_metadata"):
meta = data.get(meta_key)
if isinstance(meta, dict):
executed = meta.get(PRE_CALL_EXECUTED_GUARDRAILS_KEY)
if isinstance(executed, list):
if marker not in executed:
executed.append(marker)
else:
meta[PRE_CALL_EXECUTED_GUARDRAILS_KEY] = [marker]
return
data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]}
_RESPONSE_METADATA_KEYS: Final = frozenset(
{
"id",
"created",
"created_at",
"model",
"usage",
"usage_info",
"system_fingerprint",
"_hidden_params",
"_response_ms",
}
)
def _response_fingerprint(response: object) -> str | None:
"""Digest of everything but metadata set between layers, so an unknown field counts as content."""
try:
fields: Final[Mapping[str, object]] = (
response.model_dump(mode="json")
if isinstance(response, BaseModel)
else _REQUEST_FIELDS.validate_python(response)
)
content: Final = {key: value for key, value in fields.items() if key not in _RESPONSE_METADATA_KEYS}
if not content:
return None
return hashlib.sha256(
json.dumps(content, sort_keys=True, separators=(",", ":"), allow_nan=False).encode()
).hexdigest()
except (TypeError, ValueError):
return None
_RESPONSE_GUARDRAIL_CHECKS_KEY: Final = "guardrail_checks"
def _str_keyed_dict(value: object) -> Mapping[str, object] | None:
if not isinstance(value, dict):
return None
return cast("Mapping[str, object]", value) # cast-ok: response bodies and _hidden_params are keyed by field name
def _response_hidden_params(response: object) -> Mapping[str, object] | None:
as_dict: Final = _str_keyed_dict(response)
return _str_keyed_dict(
as_dict.get("_hidden_params") if as_dict is not None else getattr(response, "_hidden_params", None)
)
def _write_response_marker(response: object, marker: str | None) -> None:
"""Record a check on the response itself, so it survives the router copying request metadata per hop."""
if marker is None:
return
hidden: Final = _response_hidden_params(response)
if not isinstance(hidden, dict):
return
hidden[_RESPONSE_GUARDRAIL_CHECKS_KEY] = tuple(
_str_collection(hidden.get(_RESPONSE_GUARDRAIL_CHECKS_KEY)) | {marker}
)
def _response_carries_marker(response: object, marker: str | None) -> bool:
hidden: Final = _response_hidden_params(response)
return (
marker is not None
and hidden is not None
and marker in _str_collection(hidden.get(_RESPONSE_GUARDRAIL_CHECKS_KEY))
)
_REQUEST_FIELDS: Final = TypeAdapter(Mapping[str, object])
class CustomGuardrail(CustomLogger):
# If True, during_call runs async_moderation_hook instead of the unified apply_guardrail path.
use_native_during_call_hook: ClassVar[bool] = False
@ -867,20 +962,7 @@ class CustomGuardrail(CustomLogger):
With ``raised``, records instead that the hook raised as a policy pipeline
step the pipeline then moved past. The deployment-level hook still runs it.
"""
marker: Final = self._pre_call_marker(raised=raised)
if marker is None:
return
for meta_key in ("metadata", "litellm_metadata"):
meta = data.get(meta_key)
if isinstance(meta, dict):
executed = meta.get(PRE_CALL_EXECUTED_GUARDRAILS_KEY)
if isinstance(executed, list):
if marker not in executed:
executed.append(marker)
else:
meta[PRE_CALL_EXECUTED_GUARDRAILS_KEY] = [marker]
return
data["metadata"] = {PRE_CALL_EXECUTED_GUARDRAILS_KEY: [marker]}
_record_guardrail_marker(data, self._pre_call_marker(raised=raised))
def pre_call_hook_ran_or_raised(self, data: Mapping[str, object]) -> bool:
"""Whether this guardrail's ``async_pre_call_hook`` ran for this request, whatever it decided."""
@ -1077,10 +1159,26 @@ class CustomGuardrail(CustomLogger):
cast("dict[str, object]", request_data) # cast-ok: proxy request payloads are str-keyed
)
if not self._is_valid_response_type(result):
return None
returned: Final = result if self._is_valid_response_type(result) else None
if target is self:
return returned
checked: Final = returned if returned is not None else response
marker: Final = self._post_call_check_marker(checked)
_record_guardrail_marker(
cast("dict[str, object]", request_data), # cast-ok: proxy request payloads are str-keyed
marker,
)
_write_response_marker(checked, marker)
return returned
return result
def _post_call_check_marker(self, response: object) -> str | None:
fingerprint: Final = _response_fingerprint(response)
return self._marker(f"{_POST_CALL_CHECKED_TOKEN}:{fingerprint}") if fingerprint is not None else None
def post_call_already_checked(self, data: Mapping[str, object], response: object) -> bool:
"""Whether a deployment-level hook of this request already ran this guardrail on this exact response."""
marker: Final = self._post_call_check_marker(response)
return _request_carries_marker(data, marker) or _response_carries_marker(response, marker)
async def async_logging_hook(
self,

8
litellm/proxy/utils.py
View file

@ -328,6 +328,7 @@ _VIEW_SETUP_POLL_INTERVAL_SECONDS: Final = 5.0
_VIEW_SETUP_DEADLINE_SECONDS: Final = 15 * 60.0
_VIEW_SETUP_GATE_TABLE: Final = '"LiteLLM_SpendLogs"'
_VIEW_SETUP_GATE_PROBE_ROWS: Final = TypeAdapter(tuple[Mapping[str, bool], ...])
_REQUEST_FIELDS: Final = TypeAdapter(Mapping[str, object])
_ViewSetupOutcome: TypeAlias = Literal["ready", "timed_out"]
_ViewSetupAttempt: TypeAlias = Literal["ready", "table_missing"] | Exception
@ -4143,6 +4144,7 @@ class ProxyLogging:
response = pipeline_response # rebind-ok: adopt the pipeline's replacement response, same contract as the callback loops below
pipeline_managed: Final = pipeline_managed_guardrail_names(data, "post_call")
request_view: Final = _REQUEST_FIELDS.validate_python(data)
guardrail_callbacks, other_callbacks = _partition_post_call_callbacks()
try:
# Merge model-level guardrails before checking which guardrails to run
@ -4173,6 +4175,9 @@ class ProxyLogging:
):
continue
if callback.post_call_already_checked(request_view, response) is True:
continue
guardrail_response: Any | None = None
if "apply_guardrail" in type(callback).__dict__ and not callback.use_native_lifecycle_hooks:
@ -4246,10 +4251,13 @@ class ProxyLogging:
immediately before awaiting, and the unified hook pops it before its first
suspension point, so concurrent guardrails never race on that key.
"""
request_view: Final = _REQUEST_FIELDS.validate_python(data)
async def _run_one(callback: CustomGuardrail) -> None:
if callback.should_run_guardrail(data=guardrail_data, event_type=GuardrailEventHooks.post_call) is not True:
return
if callback.post_call_already_checked(request_view, response) is True:
return
if "apply_guardrail" in type(callback).__dict__ and not callback.use_native_lifecycle_hooks:
data["guardrail_to_apply"] = callback
await self._run_guardrail_with_metrics(

2
packaging/litellm-core/pyproject.toml
View file

@ -11,7 +11,7 @@ authors = [
]
dependencies = [
"fastuuid>=0.14.0,<1.0",
"filelock>=3.16.1,<4.0",
"filelock>=3.16.1,<5",
"httpx[http2]>=0.28.0,<1.0",
"openai>=2.20.0,<4.0.0",
"python-dateutil>=2.8.2,<3.0",

4
pyproject.toml
View file

@ -15,7 +15,7 @@ dependencies = [
# When changing a floor, verify it installs + imports on every supported
# Python with: `uv pip install --resolution=lowest-direct .`
"fastuuid>=0.14.0,<1.0",
"filelock>=3.16.1,<4.0",
"filelock>=3.16.1,<5",
"httpx[http2]>=0.28.0,<1.0",
"openai>=2.20.0,<4.0.0",
"python-dotenv>=1.0.0,<2.0",
@ -358,6 +358,8 @@ constraint-dependencies = [
"httplib2>=0.32.0",
"setuptools>=84.1.0",
"dnspython>=2.9.0; python_version >= '3.11'",
"filelock<3.20.4; python_version < '3.11'",
"filelock>=4.2.1; python_version >= '3.11'",
]
override-dependencies = [
# a2a-sdk 1.x requires packaging>=24.0; lunary 1.4.x still caps at <24.0.

566
tests/unit/integrations/test_custom_guardrail_post_call_once.py Normal file
View file

@ -0,0 +1,566 @@
import asyncio
import json
from collections.abc import AsyncIterable, Callable, Iterator, Mapping
from dataclasses import dataclass
from types import MappingProxyType
from typing import Final, Literal
from unittest.mock import AsyncMock, patch
import httpx
import pytest
import respx
from fastapi import HTTPException, Request, Response
from fastapi.responses import StreamingResponse
from mcp.types import Tool as MCPTool
from pydantic import BaseModel, TypeAdapter
import litellm
from litellm.caching.caching import Cache
from litellm.integrations.custom_guardrail import CustomGuardrail
from litellm.llms.base_llm.ocr.transformation import OCRResponse
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache
from litellm.proxy.utils import ProxyLogging
from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler
from litellm.router import Router
from litellm.types.guardrails import GuardrailEventHooks
from litellm.types.utils import CallTypes, Choices, GenericGuardrailAPIInputs, LLMResponseTypes, Message, ModelResponse
pytestmark = pytest.mark.respx(assert_all_called=False)
REPLY: Final = "hi from the model"
REWRITTEN: Final = "rewritten by the proxy"
MASKED: Final = "masked by G"
Route = Literal["acompletion", "aresponses", "anthropic_messages"]
_OPENAI_API_BASE: Final = "https://api.openai.com/v1"
_FAILING_API_BASE: Final = "https://primary.invalid/v1"
_MCP_API_BASE: Final = "https://mcp-rounds.invalid/v1"
_CHAT_REPLY: Final[Mapping[str, object]] = {
"id": "chatcmpl-1",
"object": "chat.completion",
"created": 0,
"model": "gpt-4o",
"choices": [{"index": 0, "message": {"role": "assistant", "content": REPLY}, "finish_reason": "stop"}],
"usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
}
_CHAT_SSE: Final = (
"".join(
f"data: {json.dumps(chunk)}\n\n"
for chunk in (
{
"id": "chatcmpl-1",
"object": "chat.completion.chunk",
"created": 0,
"model": "gpt-4o",
"choices": [{"index": 0, "delta": {"role": "assistant", "content": REPLY}, "finish_reason": None}],
},
{
"id": "chatcmpl-1",
"object": "chat.completion.chunk",
"created": 0,
"model": "gpt-4o",
"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
},
)
)
+ "data: [DONE]\n\n"
)
_RESPONSES_REPLY: Final[Mapping[str, object]] = {
"id": "resp_1",
"object": "response",
"created_at": 0,
"status": "completed",
"model": "gpt-4o",
"output": [
{
"type": "message",
"id": "msg_1",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": REPLY, "annotations": []}],
}
],
"usage": {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2},
}
_RESPONSES_SSE: Final = "".join(
f"data: {json.dumps(event)}\n\n"
for event in (
{"type": "response.created", "sequence_number": 0, "response": {**_RESPONSES_REPLY, "output": []}},
{
"type": "response.output_text.delta",
"sequence_number": 1,
"item_id": "msg_1",
"output_index": 0,
"content_index": 0,
"delta": REPLY,
},
{"type": "response.completed", "sequence_number": 2, "response": _RESPONSES_REPLY},
)
)
_TOOL_CALL_ROUND: Final[Mapping[str, object]] = {
**_RESPONSES_REPLY,
"id": "resp_tool_call",
"output": [
{
"type": "function_call",
"id": "fc_1",
"call_id": "call_echo",
"name": "echo",
"arguments": '{"value": "PING"}',
"status": "completed",
}
],
}
_JSON_OBJECT: Final = TypeAdapter(dict[str, object])
class _CountedGuardrail(CustomGuardrail):
checked: tuple[str, ...] = ()
class ApplyGuardrailG(_CountedGuardrail):
async def apply_guardrail(
self,
inputs: GenericGuardrailAPIInputs,
request_data: dict[str, object],
input_type: Literal["request", "response"],
logging_obj: object = None,
) -> GenericGuardrailAPIInputs:
self.checked = (*self.checked, " ".join(inputs.get("texts") or ()))
return inputs
def _reply_text(response: LLMResponseTypes) -> str:
match response:
case ModelResponse(choices=[Choices(message=Message(content=str() as text))]):
return text
case _:
return str(response)
class OwnHookG(_CountedGuardrail):
async def async_post_call_success_hook(
self, data: dict[str, object], user_api_key_dict: UserAPIKeyAuth, response: LLMResponseTypes
) -> None:
self.checked = (*self.checked, _reply_text(response))
class InheritedApplyGuardrailG(ApplyGuardrailG):
pass
class MaskingG(ApplyGuardrailG):
async def apply_guardrail(
self,
inputs: GenericGuardrailAPIInputs,
request_data: dict[str, object],
input_type: Literal["request", "response"],
logging_obj: object = None,
) -> GenericGuardrailAPIInputs:
checked: Final = await super().apply_guardrail(inputs, request_data, input_type, logging_obj)
return {**checked, "texts": [MASKED for _ in checked.get("texts") or ()]}
class BlockingG(ApplyGuardrailG):
async def apply_guardrail(
self,
inputs: GenericGuardrailAPIInputs,
request_data: dict[str, object],
input_type: Literal["request", "response"],
logging_obj: object = None,
) -> GenericGuardrailAPIInputs:
await super().apply_guardrail(inputs, request_data, input_type, logging_obj)
raise HTTPException(status_code=400, detail="blocked by G")
class RewritingGuardrail(CustomGuardrail):
async def apply_guardrail(
self,
inputs: GenericGuardrailAPIInputs,
request_data: dict[str, object],
input_type: Literal["request", "response"],
logging_obj: object = None,
) -> GenericGuardrailAPIInputs:
return {**inputs, "texts": [REWRITTEN for _ in inputs.get("texts") or ()]}
class ToolCallBlockingGuardrail(CustomGuardrail):
async def apply_guardrail(
self,
inputs: GenericGuardrailAPIInputs,
request_data: dict[str, object],
input_type: Literal["request", "response"],
logging_obj: object = None,
) -> GenericGuardrailAPIInputs:
if input_type == "response" and inputs.get("tool_calls"):
raise HTTPException(status_code=400, detail="tool calls are not allowed")
return inputs
def _guardrail_g(
style: type[_CountedGuardrail] = ApplyGuardrailG, default_on: bool = False, run_in_parallel: bool = False
) -> _CountedGuardrail:
return style(
guardrail_name="G",
event_hook=GuardrailEventHooks.post_call,
default_on=default_on,
run_in_parallel=run_in_parallel,
)
def _known_gap(action_item: str) -> pytest.MarkDecorator:
return pytest.mark.xfail(
raises=AssertionError,
strict=True,
reason=f"broken before #40154, out of scope for LIT-10481, tracked as RCA {action_item}",
)
def _deployment(model_name: str, guardrails: tuple[str, ...], api_base: str) -> dict[str, object]:
return {
"model_name": model_name,
"litellm_params": {
"model": "openai/gpt-4o",
"api_key": "sk-test",
"api_base": api_base,
**({"guardrails": list(guardrails)} if guardrails else {}),
},
}
def _router(primary: tuple[str, ...], fallback: tuple[str, ...] | None = None, cached: bool = False) -> Router:
if fallback is None:
return Router(model_list=[_deployment("gpt", primary, _OPENAI_API_BASE)], cache_responses=cached)
return Router(
model_list=[
_deployment("gpt", primary, _FAILING_API_BASE),
_deployment("gpt-backup", fallback, _OPENAI_API_BASE),
],
fallbacks=[{"gpt": ["gpt-backup"]}],
num_retries=0,
)
def _json_or_sse(json_reply: Mapping[str, object], sse_reply: str) -> Callable[[httpx.Request], httpx.Response]:
def reply(request: httpx.Request) -> httpx.Response:
if _JSON_OBJECT.validate_json(request.content).get("stream"):
return httpx.Response(200, headers={"content-type": "text/event-stream"}, content=sse_reply.encode())
return httpx.Response(200, json=dict(json_reply))
return reply
async def _drain(stream: AsyncIterable[object]) -> str:
return "".join([str(chunk) async for chunk in stream])
@pytest.fixture(autouse=True)
def provider(respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch) -> Iterator[respx.MockRouter]:
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
litellm.in_memory_llm_clients_cache.flush_cache()
ProxyLogging._callback_capabilities_cache.clear() # pyright: ignore[reportPrivateUsage] # keyed on id(), which a collected guardrail from an earlier test can reuse
for endpoint in ("chat/completions", "responses"):
respx_mock.post(f"{_FAILING_API_BASE}/{endpoint}").mock(
return_value=httpx.Response(500, json={"error": {"message": "primary down"}})
)
respx_mock.post(f"{_OPENAI_API_BASE}/chat/completions").mock(side_effect=_json_or_sse(_CHAT_REPLY, _CHAT_SSE))
respx_mock.post(f"{_OPENAI_API_BASE}/responses").mock(side_effect=_json_or_sse(_RESPONSES_REPLY, _RESPONSES_SSE))
yield respx_mock
litellm.in_memory_llm_clients_cache.flush_cache()
@dataclass(frozen=True, slots=True)
class ProxyCase:
route: Route = "acompletion"
stream: bool = False
primary: tuple[str, ...] = ("G",)
fallback: tuple[str, ...] | None = None
request: tuple[str, ...] = ()
default_on: bool = False
style: type[_CountedGuardrail] = ApplyGuardrailG
cached: bool = False
client_sees: str = REPLY
checks: tuple[str, ...] = (REPLY,)
PROXY_CASES: Final = (
pytest.param(ProxyCase(), id="A-chat"),
pytest.param(ProxyCase(route="aresponses"), id="A-responses"),
pytest.param(ProxyCase(route="anthropic_messages"), id="A-messages"),
pytest.param(ProxyCase(request=("G",)), id="A-chat-model-and-request"),
pytest.param(ProxyCase(style=MaskingG, client_sees=MASKED), id="A-chat-g-masks-its-own-output"),
pytest.param(ProxyCase(stream=True), id="B-chat-stream"),
pytest.param(ProxyCase(route="aresponses", stream=True), id="B-responses-stream"),
pytest.param(ProxyCase(route="anthropic_messages", stream=True), id="B-messages-stream"),
pytest.param(ProxyCase(primary=(), fallback=("G",)), id="E-only-on-fallback"),
pytest.param(
ProxyCase(request=("rewriter",), client_sees=REWRITTEN, checks=(REPLY, REWRITTEN)),
id="G-rewritten-after-check",
),
pytest.param(ProxyCase(fallback=("G",)), id="H-on-both-deployments"),
pytest.param(ProxyCase(primary=(), request=("G",)), id="I-request-chat"),
pytest.param(ProxyCase(route="aresponses", primary=(), request=("G",)), id="I-request-responses"),
pytest.param(ProxyCase(style=OwnHookG), id="J-A-chat-own-hook", marks=_known_gap("AI-5")),
pytest.param(ProxyCase(primary=(), fallback=("G",), style=OwnHookG), id="E-only-on-fallback-own-hook"),
pytest.param(ProxyCase(primary=(), request=("G",), style=OwnHookG), id="I-request-chat-own-hook"),
pytest.param(
ProxyCase(primary=(), request=("G",), style=InheritedApplyGuardrailG),
id="K-I-request-chat-inherited-apply-guardrail",
marks=_known_gap("AI-2"),
),
pytest.param(ProxyCase(primary=("other",), default_on=True), id="L-default-on"),
)
_ROUTE_PATHS: Final[Mapping[Route, str]] = MappingProxyType(
{
"acompletion": "/v1/chat/completions",
"aresponses": "/v1/responses",
"anthropic_messages": "/v1/messages",
}
)
_ROUTE_BODIES: Final[Mapping[Route, Mapping[str, object]]] = MappingProxyType(
{
"acompletion": {"messages": [{"role": "user", "content": "hello"}]},
"aresponses": {"input": "hello"},
"anthropic_messages": {"messages": [{"role": "user", "content": "hello"}], "max_tokens": 32},
}
)
async def _proxy_reply(case: ProxyCase) -> str:
from litellm.proxy.proxy_server import ProxyConfig, select_data_generator
path: Final = _ROUTE_PATHS[case.route]
router: Final = _router(case.primary, case.fallback, case.cached)
response: Final = await ProxyBaseLLMRequestProcessing(
data={
"model": "gpt",
**_ROUTE_BODIES[case.route],
**({"stream": True} if case.stream else {}),
**({"guardrails": list(case.request)} if case.request else {}),
}
).base_process_llm_request(
request=Request({"type": "http", "method": "POST", "path": path, "headers": [], "query_string": b""}),
fastapi_response=Response(),
user_api_key_dict=UserAPIKeyAuth(api_key="sk-test", request_route=path),
route_type=case.route,
proxy_logging_obj=ProxyLogging(user_api_key_cache=UserApiKeyCache()),
general_settings={},
proxy_config=ProxyConfig(),
select_data_generator=select_data_generator,
llm_router=router,
is_streaming_request=case.stream,
)
text: Final = (
await _drain(response.body_iterator)
if isinstance(response, StreamingResponse)
else json.dumps(response.model_dump() if isinstance(response, BaseModel) else response)
)
await asyncio.sleep(0)
return text
@pytest.mark.parametrize("case", PROXY_CASES)
async def test_a_proxy_request_checks_each_distinct_response_once(
monkeypatch: pytest.MonkeyPatch, case: ProxyCase
) -> None:
guardrail: Final = _guardrail_g(case.style, default_on=case.default_on)
rewriter: Final = RewritingGuardrail(guardrail_name="rewriter", event_hook=GuardrailEventHooks.post_call)
monkeypatch.setattr(litellm, "callbacks", [rewriter, guardrail])
reply: Final = await _proxy_reply(case)
assert case.client_sees in reply
assert sorted(guardrail.checked) == sorted(case.checks)
@pytest.mark.parametrize("route", ["acompletion", "aresponses"])
async def test_a_cache_hit_is_checked_like_a_fresh_response(
monkeypatch: pytest.MonkeyPatch, provider: respx.MockRouter, route: Route
) -> None:
guardrail: Final = _guardrail_g()
monkeypatch.setattr(litellm, "callbacks", [guardrail])
monkeypatch.setattr(litellm, "cache", Cache(type="local"))
case: Final = ProxyCase(route=route, cached=True)
replies: Final = (await _proxy_reply(case), await _proxy_reply(case))
assert provider.calls.call_count == 1, "the second request must be served from the cache"
assert all(REPLY in reply for reply in replies)
assert guardrail.checked == (REPLY, REPLY)
async def test_a_blocking_verdict_blocks_the_proxy_response_after_one_check(monkeypatch: pytest.MonkeyPatch) -> None:
guardrail: Final = _guardrail_g(BlockingG)
monkeypatch.setattr(litellm, "callbacks", [guardrail])
with pytest.raises(HTTPException, check=lambda e: e.detail == "blocked by G"):
await _proxy_reply(ProxyCase())
assert guardrail.checked == (REPLY,)
SdkCaller = Literal["sdk-call", "sdk-router"]
@dataclass(frozen=True, slots=True)
class SdkCase:
caller: SdkCaller = "sdk-call"
stream: bool = False
style: type[_CountedGuardrail] = ApplyGuardrailG
SDK_CASES: Final = (
pytest.param(SdkCase(), id="C-sdk-call"),
pytest.param(SdkCase(caller="sdk-router"), id="C-sdk-router"),
pytest.param(SdkCase(style=OwnHookG), id="C-sdk-call-own-hook"),
pytest.param(SdkCase(stream=True), id="D-sdk-call-stream", marks=_known_gap("AI-4")),
)
async def _sdk_reply(case: SdkCase) -> str:
messages: Final = [{"role": "user", "content": "hello"}]
response: Final = await (
_router(("G",)).acompletion(model="gpt", messages=messages, stream=case.stream)
if case.caller == "sdk-router"
else litellm.acompletion(
model="openai/gpt-4o", api_key="sk-test", guardrails=["G"], messages=messages, stream=case.stream
)
)
text: Final = await _drain(response) if isinstance(response, AsyncIterable) else str(response)
await asyncio.sleep(0)
return text
@pytest.mark.parametrize("case", SDK_CASES)
async def test_an_sdk_call_checks_its_response_once(monkeypatch: pytest.MonkeyPatch, case: SdkCase) -> None:
guardrail: Final = _guardrail_g(case.style)
monkeypatch.setattr(litellm, "callbacks", [guardrail])
reply: Final = await _sdk_reply(case)
assert REPLY in reply
assert guardrail.checked == (REPLY,)
async def test_mcp_auto_execute_checks_the_tool_call_round_before_running_its_tools(
monkeypatch: pytest.MonkeyPatch, provider: respx.MockRouter
) -> None:
monkeypatch.setattr(
litellm, "callbacks", [ToolCallBlockingGuardrail(guardrail_name="G", event_hook=GuardrailEventHooks.post_call)]
)
provider.post(f"{_MCP_API_BASE}/responses").mock(
side_effect=[httpx.Response(200, json=_TOOL_CALL_ROUND), httpx.Response(200, json=_RESPONSES_REPLY)]
)
echo_tool: Final = MCPTool.model_validate(
{"name": "echo", "inputSchema": {"type": "object", "properties": {"value": {"type": "string"}}}},
by_name=False,
)
run_tools: Final = AsyncMock(return_value=[{"tool_call_id": "call_echo", "result": "PING", "name": "echo"}])
with (
patch.object(
LiteLLM_Proxy_MCP_Handler,
"process_mcp_tools_without_openai_transform",
AsyncMock(return_value=([echo_tool], {"echo": "demo"})),
),
patch.object(LiteLLM_Proxy_MCP_Handler, "execute_tool_calls", run_tools),
pytest.raises(HTTPException, check=lambda e: e.detail == "tool calls are not allowed"),
):
await litellm.aresponses(
model="openai/gpt-4o",
input="Call echo with PING",
tools=[{"type": "mcp", "server_url": "litellm_proxy/mcp/demo", "require_approval": "never"}],
api_key="sk-test",
api_base=_MCP_API_BASE,
guardrails=["G"],
proxy_server_request={"url": "http://localhost:4000/v1/responses", "method": "POST"},
litellm_metadata={"user_api_key_request_route": "/v1/responses"},
)
run_tools.assert_not_awaited()
def _same_response(reply: ModelResponse) -> ModelResponse:
return reply
def _model_renamed(reply: ModelResponse) -> ModelResponse:
return reply.model_copy(update={"model": "public-alias"})
def _rewritten(_: ModelResponse) -> ModelResponse:
return ModelResponse(choices=[{"message": {"role": "assistant", "content": REWRITTEN}}])
PROXY_LOOP_CASES: Final = (
pytest.param(_same_response, (REPLY,), id="A-same-response"),
pytest.param(_model_renamed, (REPLY,), id="A-model-renamed"),
pytest.param(_rewritten, (REPLY, REWRITTEN), id="G-rewritten"),
)
@pytest.mark.parametrize("parallel", [False, True], ids=["sequential", "parallel"])
@pytest.mark.parametrize(("proxy_sees", "checks"), PROXY_LOOP_CASES)
async def test_each_proxy_post_call_loop_skips_only_output_the_deployment_checked(
monkeypatch: pytest.MonkeyPatch,
parallel: bool,
proxy_sees: Callable[[ModelResponse], ModelResponse],
checks: tuple[str, ...],
) -> None:
guardrail: Final = _guardrail_g(run_in_parallel=parallel)
monkeypatch.setattr(litellm, "callbacks", [guardrail])
data: Final = {
"model": "gpt",
"proxy_server_request": {"url": "http://localhost:4000/v1/chat/completions"},
"metadata": {"guardrails": ["G"]},
}
reply: Final = ModelResponse(model="provider-model", choices=[{"message": {"role": "assistant", "content": REPLY}}])
await guardrail.async_post_call_success_deployment_hook(
request_data={**data, "guardrails": ["G"]}, response=reply, call_type=CallTypes.acompletion
)
await ProxyLogging(user_api_key_cache=UserApiKeyCache()).post_call_success_hook(
data=data, response=proxy_sees(reply), user_api_key_dict=UserAPIKeyAuth()
)
assert sorted(guardrail.checked) == sorted(checks)
def _ocr_reply(markdown: str) -> OCRResponse:
return OCRResponse.model_validate(
{"pages": [{"index": 0, "markdown": markdown, "images": [], "dimensions": None}], "model": "mistral-ocr-latest"}
)
@pytest.mark.parametrize(
("proxy_sees", "checks"),
[
pytest.param(REPLY, (REPLY,), id="A-ocr-same-pages"),
pytest.param(REWRITTEN, (REPLY, REWRITTEN), id="G-ocr-rewritten-pages"),
],
)
async def test_an_ocr_response_is_skipped_only_when_its_pages_were_checked(
monkeypatch: pytest.MonkeyPatch, proxy_sees: str, checks: tuple[str, ...]
) -> None:
guardrail: Final = _guardrail_g()
monkeypatch.setattr(litellm, "callbacks", [guardrail])
data: Final = {
"model": "ocr",
"document": {"type": "document_url", "document_url": "https://example.com/doc.pdf"},
"proxy_server_request": {"url": "http://localhost:4000/v1/ocr"},
"metadata": {"guardrails": ["G"], "user_api_key_request_route": "/v1/ocr"},
}
await guardrail.async_post_call_success_deployment_hook(
request_data={**data, "guardrails": ["G"]}, response=_ocr_reply(REPLY), call_type=CallTypes.aocr
)
await ProxyLogging(user_api_key_cache=UserApiKeyCache()).post_call_success_hook(
data=data, response=_ocr_reply(proxy_sees), user_api_key_dict=UserAPIKeyAuth(request_route="/v1/ocr")
)
assert sorted(guardrail.checked) == sorted(checks)

1
tests/unit/proxy/utils/proxy_logging/test_guardrail_pipeline.py
View file

@ -696,6 +696,7 @@ def _moderation_guardrail() -> MagicMock:
cb.should_run_guardrail = MagicMock(return_value=True)
cb.async_moderation_hook = AsyncMock(return_value=None)
cb.async_post_call_success_hook = AsyncMock(return_value=None)
cb.post_call_already_checked = MagicMock(return_value=False)
cb.run_in_parallel = False
return cb

432
uv.lock generated
View file

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