diff --git a/tests/kernels/test_flex_attention.py b/tests/kernels/test_flex_attention.py
index 7053a8697..b04f5c62c 100644
--- a/tests/kernels/test_flex_attention.py
+++ b/tests/kernels/test_flex_attention.py
@@ -2,13 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Integration tests for FlexAttention backend vs default backend"""
 
-import random
-
-import numpy as np
 import pytest
 import torch
 from packaging import version
 
+from tests.utils import set_random_seed
 from tests.v1.attention.utils import (
     BatchSpec,
     create_common_attn_metadata,
@@ -27,15 +25,6 @@ MINIMUM_TORCH_VERSION = version.parse("2.7.0")
 DIRECT_BUILD_VERSION = version.parse("2.9.dev0")
 
 
-def set_seed(seed):
-    """Set seeds for reproducibility"""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed_all(seed)
-
-
 @pytest.mark.skipif(
     not torch.cuda.is_available() or TORCH_VERSION < MINIMUM_TORCH_VERSION,
     reason="CUDA not available or PyTorch version < 2.7",
@@ -57,7 +46,7 @@ def test_flex_attention_vs_default_backend(vllm_runner):
     ]
 
     # Run with flex attention
-    set_seed(seed)
+    set_random_seed(seed)
     with vllm_runner(
         model_name,
         runner="generate",
@@ -71,7 +60,7 @@ def test_flex_attention_vs_default_backend(vllm_runner):
         )
 
     # Run with default backend
-    set_seed(seed)
+    set_random_seed(seed)
     with vllm_runner(
         model_name,
         runner="generate",
diff --git a/tests/utils.py b/tests/utils.py
index b3d21ac55..d263c7ef0 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -59,7 +59,10 @@ from vllm.tokenizers import get_tokenizer
 from vllm.utils.argparse_utils import FlexibleArgumentParser
 from vllm.utils.mem_constants import GB_bytes
 from vllm.utils.network_utils import get_open_port
-from vllm.utils.torch_utils import cuda_device_count_stateless
+from vllm.utils.torch_utils import (
+    cuda_device_count_stateless,
+    set_random_seed,  # noqa: F401 - re-exported for use in test files
+)
 
 FP8_DTYPE = current_platform.fp8_dtype()
 
diff --git a/tests/v1/logits_processors/test_custom_offline.py b/tests/v1/logits_processors/test_custom_offline.py
index e3ddb6138..59317e918 100644
--- a/tests/v1/logits_processors/test_custom_offline.py
+++ b/tests/v1/logits_processors/test_custom_offline.py
@@ -1,11 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import random
 from typing import Any
 
 import pytest
 
-from tests.utils import create_new_process_for_each_test
+from tests.utils import create_new_process_for_each_test, set_random_seed
 from tests.v1.logits_processors.utils import (
     DUMMY_LOGITPROC_ARG,
     DUMMY_LOGITPROC_FQCN,
@@ -135,7 +134,7 @@ def test_custom_logitsprocs(monkeypatch, logitproc_source: CustomLogitprocSource
 
     # Test that logitproc info is passed to workers
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1")
-    random.seed(40)
+    set_random_seed(40)
 
     # Choose LLM args based on logitproc source
     if logitproc_source == CustomLogitprocSource.LOGITPROC_SOURCE_NONE:
@@ -194,7 +193,7 @@ def test_custom_logitsprocs_req(monkeypatch):
 
     # Test that logitproc info is passed to workers
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1")
-    random.seed(40)
+    set_random_seed(40)
     _run_test(
         {"logits_processors": [WrappedPerReqLogitsProcessor]}, logitproc_loaded=True
     )
@@ -237,7 +236,7 @@ def test_rejects_custom_logitsprocs(
                         logitproc from
     """
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
-    random.seed(40)
+    set_random_seed(40)
 
     test_params: dict[str, dict[str, Any]] = {
         "pooling": {
diff --git a/vllm/utils/torch_utils.py b/vllm/utils/torch_utils.py
index 19833292c..6c44c00db 100644
--- a/vllm/utils/torch_utils.py
+++ b/vllm/utils/torch_utils.py
@@ -333,6 +333,8 @@ def set_random_seed(seed: int | None) -> None:
         random.seed(seed)
         np.random.seed(seed)
         torch.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(seed)
 
 
 def create_kv_caches_with_random_flash(