chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,881 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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import subprocess
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import tempfile
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from vllm.assets.audio import AudioAsset
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from vllm.entrypoints.openai.run_batch import (
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BatchRequestOutput,
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download_bytes_from_url,
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)
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CHAT_MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
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EMBEDDING_MODEL_NAME = "intfloat/multilingual-e5-small"
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RERANKER_MODEL_NAME = "BAAI/bge-reranker-v2-m3"
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REASONING_MODEL_NAME = "Qwen/Qwen3-0.6B"
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SPEECH_LARGE_MODEL_NAME = "openai/whisper-large-v3"
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SPEECH_SMALL_MODEL_NAME = "openai/whisper-small"
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INPUT_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": CHAT_MODEL_NAME,
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant.",
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},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": CHAT_MODEL_NAME,
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"messages": [
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{
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"role": "system",
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"content": "You are an unhelpful assistant.",
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},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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{
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"custom_id": "request-3",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": "NonExistModel",
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"messages": [
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{
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"role": "system",
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"content": "You are an unhelpful assistant.",
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},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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{
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"custom_id": "request-4",
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"method": "POST",
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"url": "/bad_url",
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"body": {
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"model": CHAT_MODEL_NAME,
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"messages": [
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{
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"role": "system",
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"content": "You are an unhelpful assistant.",
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},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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{
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"custom_id": "request-5",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"stream": "True",
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"model": CHAT_MODEL_NAME,
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"messages": [
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{
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"role": "system",
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"content": "You are an unhelpful assistant.",
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},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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]
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)
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INVALID_INPUT_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"invalid_field": "request-1",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": CHAT_MODEL_NAME,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": CHAT_MODEL_NAME,
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"messages": [
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{"role": "system", "content": "You are an unhelpful assistant."},
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{"role": "user", "content": "Hello world!"},
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],
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"max_tokens": 1000,
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},
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},
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]
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)
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INPUT_EMBEDDING_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/embeddings",
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"body": {
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"model": EMBEDDING_MODEL_NAME,
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"input": "You are a helpful assistant.",
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/embeddings",
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"body": {
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"model": EMBEDDING_MODEL_NAME,
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"input": "You are an unhelpful assistant.",
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},
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},
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{
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"custom_id": "request-3",
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"method": "POST",
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"url": "/v1/embeddings",
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"body": {
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"model": EMBEDDING_MODEL_NAME,
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"input": "Hello world!",
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},
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},
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{
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"custom_id": "request-4",
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"method": "POST",
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"url": "/v1/embeddings",
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"body": {
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"model": "NonExistModel",
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"input": "Hello world!",
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},
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},
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]
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)
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_SCORE_RERANK_DOCUMENTS = [
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"The capital of Brazil is Brasilia.",
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"The capital of France is Paris.",
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]
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INPUT_SCORE_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/score",
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"body": {
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"model": RERANKER_MODEL_NAME,
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"queries": "What is the capital of France?",
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"documents": _SCORE_RERANK_DOCUMENTS,
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/score",
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"body": {
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"model": RERANKER_MODEL_NAME,
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"queries": "What is the capital of France?",
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"documents": _SCORE_RERANK_DOCUMENTS,
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},
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},
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]
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)
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INPUT_RERANK_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/rerank",
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"body": {
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"model": RERANKER_MODEL_NAME,
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"query": "What is the capital of France?",
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"documents": _SCORE_RERANK_DOCUMENTS,
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/rerank",
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"body": {
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"model": RERANKER_MODEL_NAME,
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"query": "What is the capital of France?",
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"documents": _SCORE_RERANK_DOCUMENTS,
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v2/rerank",
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"body": {
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"model": RERANKER_MODEL_NAME,
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"query": "What is the capital of France?",
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"documents": _SCORE_RERANK_DOCUMENTS,
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},
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},
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]
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)
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INPUT_REASONING_BATCH = "\n".join(
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json.dumps(req)
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for req in [
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": REASONING_MODEL_NAME,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Solve this math problem: 2+2=?"},
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],
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},
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},
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{
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"custom_id": "request-2",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": REASONING_MODEL_NAME,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What is the capital of France?"},
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],
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},
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},
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]
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)
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MINIMAL_WAV_BASE64 = "UklGRigAAABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQQAAAAAAP9/"
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INPUT_TRANSCRIPTION_BATCH = (
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json.dumps(
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/audio/transcriptions",
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"body": {
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"model": SPEECH_LARGE_MODEL_NAME,
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"file_url": f"data:audio/wav;base64,{MINIMAL_WAV_BASE64}",
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"response_format": "json",
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},
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}
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)
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+ "\n"
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)
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INPUT_TRANSCRIPTION_HTTP_BATCH = (
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json.dumps(
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/audio/transcriptions",
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"body": {
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"model": SPEECH_LARGE_MODEL_NAME,
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"file_url": AudioAsset("mary_had_lamb").url,
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"language": "en",
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"response_format": "json",
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},
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}
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)
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+ "\n"
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)
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INPUT_TRANSLATION_BATCH = (
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json.dumps(
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/audio/translations",
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"body": {
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"model": SPEECH_SMALL_MODEL_NAME,
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"file_url": AudioAsset("mary_had_lamb").url,
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"response_format": "text",
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"language": "it",
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"to_language": "en",
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"temperature": 0.0,
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},
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}
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)
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+ "\n"
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)
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WEATHER_TOOL = {
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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},
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},
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"required": ["location"],
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},
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},
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}
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INPUT_TOOL_CALLING_BATCH = json.dumps(
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{
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"custom_id": "request-1",
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"method": "POST",
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"url": "/v1/chat/completions",
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"body": {
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"model": REASONING_MODEL_NAME,
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"messages": [
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{"role": "user", "content": "What is the weather in San Francisco?"},
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],
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"tools": [WEATHER_TOOL],
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"tool_choice": "required",
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"max_tokens": 1000,
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},
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}
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)
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def test_empty_file():
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
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input_file.write("")
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
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"-i",
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input_file.name,
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"-o",
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output_file.name,
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"--model",
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EMBEDDING_MODEL_NAME,
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],
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)
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proc.communicate()
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proc.wait()
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assert proc.returncode == 0, f"{proc=}"
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contents = output_file.read()
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assert contents.strip() == ""
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def test_completions():
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with (
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tempfile.NamedTemporaryFile("w") as input_file,
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tempfile.NamedTemporaryFile("r") as output_file,
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):
|
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input_file.write(INPUT_BATCH)
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input_file.flush()
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proc = subprocess.Popen(
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[
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"vllm",
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"run-batch",
|
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"-i",
|
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input_file.name,
|
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"-o",
|
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output_file.name,
|
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"--model",
|
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CHAT_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
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for line in contents.strip().split("\n"):
|
||||
# Ensure that the output format conforms to the openai api.
|
||||
# Validation should throw if the schema is wrong.
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
|
||||
def test_completions_invalid_input():
|
||||
"""
|
||||
Ensure that we fail when the input doesn't conform to the openai api.
|
||||
"""
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INVALID_INPUT_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
CHAT_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode != 0, f"{proc=}"
|
||||
|
||||
|
||||
def test_embeddings():
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_EMBEDDING_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
EMBEDDING_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
# Ensure that the output format conforms to the openai api.
|
||||
# Validation should throw if the schema is wrong.
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("input_batch", [INPUT_SCORE_BATCH, INPUT_RERANK_BATCH])
|
||||
def test_score(input_batch):
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(input_batch)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
RERANKER_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
# Ensure that the output format conforms to the openai api.
|
||||
# Validation should throw if the schema is wrong.
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
# Ensure that there is no error in the response.
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
|
||||
def test_reasoning_parser():
|
||||
"""
|
||||
Test that reasoning_parser parameter works correctly in run_batch.
|
||||
"""
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_REASONING_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
REASONING_MODEL_NAME,
|
||||
"--reasoning-parser",
|
||||
"qwen3",
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
# Ensure that the output format conforms to the openai api.
|
||||
# Validation should throw if the schema is wrong.
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
# Ensure that there is no error in the response.
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
# Check that reasoning is present and not empty
|
||||
reasoning = line_dict["response"]["body"]["choices"][0]["message"][
|
||||
"reasoning"
|
||||
]
|
||||
assert reasoning is not None
|
||||
assert len(reasoning) > 0
|
||||
|
||||
|
||||
def test_transcription():
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_TRANSCRIPTION_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
SPEECH_LARGE_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
print(f"\n\ncontents: {contents}\n\n")
|
||||
for line in contents.strip().split("\n"):
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
response_body = line_dict["response"]["body"]
|
||||
assert response_body is not None
|
||||
assert "text" in response_body
|
||||
assert "usage" in response_body
|
||||
|
||||
|
||||
def test_transcription_http_url():
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_TRANSCRIPTION_HTTP_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
SPEECH_LARGE_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
response_body = line_dict["response"]["body"]
|
||||
assert response_body is not None
|
||||
assert "text" in response_body
|
||||
assert "usage" in response_body
|
||||
|
||||
transcription_text = response_body["text"]
|
||||
assert "Mary had a little lamb" in transcription_text
|
||||
|
||||
|
||||
def test_translation():
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_TRANSLATION_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
SPEECH_SMALL_MODEL_NAME,
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
response_body = line_dict["response"]["body"]
|
||||
assert response_body is not None
|
||||
assert "text" in response_body
|
||||
|
||||
translation_text = response_body["text"]
|
||||
translation_text_lower = str(translation_text).strip().lower()
|
||||
assert "mary" in translation_text_lower or "lamb" in translation_text_lower
|
||||
|
||||
|
||||
def test_tool_calling():
|
||||
"""
|
||||
Test that tool calling works correctly in run_batch.
|
||||
Verifies that requests with tools return tool_calls in the response.
|
||||
"""
|
||||
with (
|
||||
tempfile.NamedTemporaryFile("w") as input_file,
|
||||
tempfile.NamedTemporaryFile("r") as output_file,
|
||||
):
|
||||
input_file.write(INPUT_TOOL_CALLING_BATCH)
|
||||
input_file.flush()
|
||||
proc = subprocess.Popen(
|
||||
[
|
||||
"vllm",
|
||||
"run-batch",
|
||||
"-i",
|
||||
input_file.name,
|
||||
"-o",
|
||||
output_file.name,
|
||||
"--model",
|
||||
REASONING_MODEL_NAME,
|
||||
"--enable-auto-tool-choice",
|
||||
"--tool-call-parser",
|
||||
"hermes",
|
||||
],
|
||||
)
|
||||
proc.communicate()
|
||||
proc.wait()
|
||||
assert proc.returncode == 0, f"{proc=}"
|
||||
|
||||
contents = output_file.read()
|
||||
for line in contents.strip().split("\n"):
|
||||
if not line.strip(): # Skip empty lines
|
||||
continue
|
||||
# Ensure that the output format conforms to the openai api.
|
||||
# Validation should throw if the schema is wrong.
|
||||
BatchRequestOutput.model_validate_json(line)
|
||||
|
||||
# Ensure that there is no error in the response.
|
||||
line_dict = json.loads(line)
|
||||
assert isinstance(line_dict, dict)
|
||||
assert line_dict["error"] is None
|
||||
|
||||
# Check that tool_calls are present in the response
|
||||
# With tool_choice="required", the model must call a tool
|
||||
response_body = line_dict["response"]["body"]
|
||||
assert response_body is not None
|
||||
message = response_body["choices"][0]["message"]
|
||||
assert "tool_calls" in message
|
||||
tool_calls = message.get("tool_calls")
|
||||
# With tool_choice="required", tool_calls must be present and non-empty
|
||||
assert tool_calls is not None
|
||||
assert isinstance(tool_calls, list)
|
||||
assert len(tool_calls) > 0
|
||||
# Verify tool_calls have the expected structure
|
||||
for tool_call in tool_calls:
|
||||
assert "id" in tool_call
|
||||
assert "type" in tool_call
|
||||
assert tool_call["type"] == "function"
|
||||
assert "function" in tool_call
|
||||
assert "name" in tool_call["function"]
|
||||
assert "arguments" in tool_call["function"]
|
||||
# Verify the tool name matches our tool definition
|
||||
assert tool_call["function"]["name"] == "get_current_weather"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Unit tests for download_bytes_from_url SSRF protection
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _make_aiohttp_mocks(response_data: bytes = b"fake-data", status: int = 200):
|
||||
"""Create mock objects that simulate aiohttp.ClientSession context managers."""
|
||||
mock_resp = MagicMock()
|
||||
mock_resp.status = status
|
||||
mock_resp.read = AsyncMock(return_value=response_data)
|
||||
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
|
||||
mock_resp.__aexit__ = AsyncMock(return_value=False)
|
||||
|
||||
mock_session = MagicMock()
|
||||
mock_session.get = MagicMock(return_value=mock_resp)
|
||||
mock_session.__aenter__ = AsyncMock(return_value=mock_session)
|
||||
mock_session.__aexit__ = AsyncMock(return_value=False)
|
||||
return mock_session
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_data_url_bypasses_domain_check():
|
||||
"""data: URLs must work regardless of the domain allowlist."""
|
||||
data_url = f"data:audio/wav;base64,{MINIMAL_WAV_BASE64}"
|
||||
result = await download_bytes_from_url(
|
||||
data_url, allowed_media_domains=["example.com"]
|
||||
)
|
||||
assert isinstance(result, bytes)
|
||||
assert len(result) > 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_rejects_disallowed_domain():
|
||||
"""HTTP URLs whose hostname is not in the allowlist must be rejected."""
|
||||
url = "https://evil.internal/secret"
|
||||
with pytest.raises(ValueError, match="allowed domains"):
|
||||
await download_bytes_from_url(url, allowed_media_domains=["example.com"])
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_rejects_cloud_metadata_ip():
|
||||
"""Cloud metadata endpoints must be blocked when an allowlist is set."""
|
||||
url = "http://169.254.169.254/latest/meta-data/"
|
||||
with pytest.raises(ValueError, match="allowed domains"):
|
||||
await download_bytes_from_url(url, allowed_media_domains=["example.com"])
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_rejects_internal_ip():
|
||||
"""Private-range IPs must be blocked when an allowlist is set."""
|
||||
for internal_url in [
|
||||
"http://10.0.0.1/secret",
|
||||
"http://192.168.1.1/admin",
|
||||
"http://127.0.0.1:8080/internal",
|
||||
]:
|
||||
with pytest.raises(ValueError, match="allowed domains"):
|
||||
await download_bytes_from_url(
|
||||
internal_url, allowed_media_domains=["example.com"]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_allows_permitted_domain():
|
||||
"""HTTP URLs whose hostname IS in the allowlist must be fetched."""
|
||||
url = "https://example.com/audio.wav"
|
||||
expected = b"audio-bytes"
|
||||
mock_session = _make_aiohttp_mocks(expected)
|
||||
|
||||
with patch(
|
||||
"vllm.entrypoints.openai.run_batch.aiohttp.ClientSession",
|
||||
return_value=mock_session,
|
||||
):
|
||||
result = await download_bytes_from_url(
|
||||
url, allowed_media_domains=["example.com"]
|
||||
)
|
||||
assert result == expected
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_no_allowlist_permits_any_domain():
|
||||
"""Without an allowlist all HTTP URLs must be attempted (backward compat)."""
|
||||
url = "https://any-domain.example.org/file.wav"
|
||||
expected = b"some-data"
|
||||
mock_session = _make_aiohttp_mocks(expected)
|
||||
|
||||
with patch(
|
||||
"vllm.entrypoints.openai.run_batch.aiohttp.ClientSession",
|
||||
return_value=mock_session,
|
||||
):
|
||||
result = await download_bytes_from_url(url, allowed_media_domains=None)
|
||||
assert result == expected
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_empty_allowlist_denies_all():
|
||||
"""An empty allowlist must deny all HTTP URLs (least privilege)."""
|
||||
url = "https://any-domain.example.org/file.wav"
|
||||
with pytest.raises(ValueError, match="allowed domains"):
|
||||
await download_bytes_from_url(url, allowed_media_domains=[])
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_unsupported_scheme():
|
||||
"""Unsupported URL schemes must be rejected regardless of allowlist."""
|
||||
with pytest.raises(ValueError, match="Unsupported URL scheme"):
|
||||
await download_bytes_from_url("ftp://example.com/file.wav")
|
||||
|
||||
with pytest.raises(ValueError, match="Unsupported URL scheme"):
|
||||
await download_bytes_from_url(
|
||||
"ftp://example.com/file.wav",
|
||||
allowed_media_domains=["example.com"],
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_download_bytes_backslash_bypass():
|
||||
"""Backslash-@ URL confusion must not bypass the allowlist.
|
||||
|
||||
urllib3.parse_url() and aiohttp/yarl disagree on backslash-before-@.
|
||||
The fix normalizes through urllib3 before handing to aiohttp.
|
||||
"""
|
||||
bypass_url = "http://allowed.example.com\\@evil.internal/secret"
|
||||
with pytest.raises(ValueError, match="allowed domains"):
|
||||
await download_bytes_from_url(
|
||||
bypass_url, allowed_media_domains=["evil.internal"]
|
||||
)
|
||||
Reference in New Issue
Block a user