diff --git a/src/lightning/fabric/strategies/launchers/multiprocessing.py b/src/lightning/fabric/strategies/launchers/multiprocessing.py
index 3b3e180e63f41..d1a7f097d0c90 100644
--- a/src/lightning/fabric/strategies/launchers/multiprocessing.py
+++ b/src/lightning/fabric/strategies/launchers/multiprocessing.py
@@ -195,17 +195,29 @@ def _check_bad_cuda_fork() -> None:
     Lightning users.
 
     """
-    if not torch.cuda.is_initialized():
-        return
-
-    message = (
-        "Lightning can't create new processes if CUDA is already initialized. Did you manually call"
-        " `torch.cuda.*` functions, have moved the model to the device, or allocated memory on the GPU any"
-        " other way? Please remove any such calls, or change the selected strategy."
-    )
-    if _IS_INTERACTIVE:
-        message += " You will have to restart the Python kernel."
-    raise RuntimeError(message)
+    # Use PyTorch's internal check for bad fork state, which is more accurate than just checking if CUDA
+    # is initialized. This allows passive CUDA initialization (e.g., from library imports or device queries)
+    # while still catching actual problematic cases where CUDA context was created before forking.
+    _is_in_bad_fork = getattr(torch.cuda, "_is_in_bad_fork", None)
+    if _is_in_bad_fork is not None and callable(_is_in_bad_fork) and _is_in_bad_fork():
+        message = (
+            "Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, "
+            "you must use the 'spawn' start method or avoid CUDA initialization in the main process."
+        )
+        if _IS_INTERACTIVE:
+            message += " You will have to restart the Python kernel."
+        raise RuntimeError(message)
+
+    # Fallback to the old check if _is_in_bad_fork is not available (older PyTorch versions)
+    if _is_in_bad_fork is None and torch.cuda.is_initialized():
+        message = (
+            "Lightning can't create new processes if CUDA is already initialized. Did you manually call"
+            " `torch.cuda.*` functions, have moved the model to the device, or allocated memory on the GPU any"
+            " other way? Please remove any such calls, or change the selected strategy."
+        )
+        if _IS_INTERACTIVE:
+            message += " You will have to restart the Python kernel."
+        raise RuntimeError(message)
 
 
 def _disable_module_memory_sharing(data: Any) -> Any:
diff --git a/tests/tests_fabric/strategies/launchers/test_multiprocessing.py b/tests/tests_fabric/strategies/launchers/test_multiprocessing.py
index 5bb85e070f17d..7f7414a161903 100644
--- a/tests/tests_fabric/strategies/launchers/test_multiprocessing.py
+++ b/tests/tests_fabric/strategies/launchers/test_multiprocessing.py
@@ -98,6 +98,17 @@ def test_check_for_bad_cuda_fork(mp_mock, _, start_method):
         launcher.launch(function=Mock())
 
 
+@pytest.mark.parametrize("start_method", ["fork", "forkserver"])
+@mock.patch("torch.cuda._is_in_bad_fork", return_value=True)
+@mock.patch("lightning.fabric.strategies.launchers.multiprocessing.mp")
+def test_check_for_bad_cuda_fork_with_is_in_bad_fork(mp_mock, _, start_method):
+    """Test the new _is_in_bad_fork detection when available."""
+    mp_mock.get_all_start_methods.return_value = [start_method]
+    launcher = _MultiProcessingLauncher(strategy=Mock(), start_method=start_method)
+    with pytest.raises(RuntimeError, match="Cannot re-initialize CUDA in forked subprocess"):
+        launcher.launch(function=Mock())
+
+
 def test_check_for_missing_main_guard():
     launcher = _MultiProcessingLauncher(strategy=Mock(), start_method="spawn")
     with (