diff --git a/docs/requirements.txt b/docs/requirements.txt
index e5bedf8552..5acc437391 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -22,7 +22,7 @@ sphinx-autodoc-typehints==1.11.1
 pandas
 einops
 transformers<4.22; python_version <= '3.10'  # https://github.com/Project-MONAI/MONAI/issues/5157
-mlflow>=1.28.0
+mlflow>=1.28.0, <=2.11.3
 clearml>=1.10.0rc0
 tensorboardX
 imagecodecs; platform_system == "Linux" or platform_system == "Darwin"
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 2855b56ad5..bfa8961ecf 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -34,7 +34,7 @@ pandas
 requests
 einops
 transformers>=4.36.0
-mlflow>=1.28.0
+mlflow>=1.28.0, <=2.11.3
 clearml>=1.10.0rc0
 matplotlib!=3.5.0
 tensorboardX
diff --git a/setup.cfg b/setup.cfg
index d7cb703d25..c8ae1630f7 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -66,7 +66,7 @@ all =
     pandas
     einops
     transformers<4.22; python_version <= '3.10'
-    mlflow>=1.28.0
+    mlflow>=1.28.0, <=2.11.3
     clearml>=1.10.0rc0
     matplotlib
     tensorboardX
@@ -125,7 +125,7 @@ einops =
 transformers =
     transformers<4.22; python_version <= '3.10'
 mlflow =
-    mlflow
+    mlflow>=1.28.0, <=2.11.3
 matplotlib =
     matplotlib
 clearml =
diff --git a/tests/test_clip_intensity_percentiles.py b/tests/test_clip_intensity_percentiles.py
index 82471e25ce..f584a0bb41 100644
--- a/tests/test_clip_intensity_percentiles.py
+++ b/tests/test_clip_intensity_percentiles.py
@@ -18,6 +18,7 @@
 from monai.transforms import ClipIntensityPercentiles
 from monai.transforms.utils import soft_clip
 from monai.transforms.utils_pytorch_numpy_unification import clip, percentile
+from monai.utils.type_conversion import convert_to_tensor
 from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose
 
 
@@ -29,7 +30,7 @@ def test_hard_clipping_two_sided(self, p):
         im = p(self.imt)
         result = hard_clipper(im)
         lower, upper = percentile(im, (5, 95))
-        expected = clip(im, lower, upper)
+        expected = clip(convert_to_tensor(im), lower, upper)
         assert_allclose(result, p(expected), type_test="tensor", rtol=1e-7, atol=0)
 
     @parameterized.expand([[p] for p in TEST_NDARRAYS])
diff --git a/tests/test_clip_intensity_percentilesd.py b/tests/test_clip_intensity_percentilesd.py
index 2b49383182..97c08f9f4e 100644
--- a/tests/test_clip_intensity_percentilesd.py
+++ b/tests/test_clip_intensity_percentilesd.py
@@ -19,6 +19,7 @@
 from monai.transforms import ClipIntensityPercentilesd
 from monai.transforms.utils import soft_clip
 from monai.transforms.utils_pytorch_numpy_unification import clip, percentile
+from monai.utils.type_conversion import convert_to_tensor
 from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D, NumpyImageTestCase3D, assert_allclose
 
 
@@ -31,7 +32,7 @@ def test_hard_clipping_two_sided(self, p):
         im = p(self.imt)
         result = hard_clipper({key: im})
         lower, upper = percentile(im, (5, 95))
-        expected = clip(im, lower, upper)
+        expected = clip(convert_to_tensor(im), lower, upper)
         assert_allclose(result[key], p(expected), type_test="tensor", rtol=1e-7, atol=0)
 
     @parameterized.expand([[p] for p in TEST_NDARRAYS])