diff --git a/ivy/functional/backends/jax/statistical.py b/ivy/functional/backends/jax/statistical.py
index a0b31da7eca77..bff5fca9a2d1e 100644
--- a/ivy/functional/backends/jax/statistical.py
+++ b/ivy/functional/backends/jax/statistical.py
@@ -183,6 +183,7 @@ def cumprod(
         return jnp.flip(x, axis=axis)
 
 
+@with_unsupported_dtypes({"0.4.23 and below": "bool"}, backend_version)
 def cumsum(
     x: JaxArray,
     axis: int = 0,
diff --git a/ivy/functional/backends/tensorflow/statistical.py b/ivy/functional/backends/tensorflow/statistical.py
index f79a665fb2e99..f111a2804e5a1 100644
--- a/ivy/functional/backends/tensorflow/statistical.py
+++ b/ivy/functional/backends/tensorflow/statistical.py
@@ -200,6 +200,7 @@ def cumprod(
     return tf.math.cumprod(x, axis, exclusive, reverse)
 
 
+@with_unsupported_dtypes({"2.15.0 and below": "bool"}, backend_version)
 def cumsum(
     x: Union[tf.Tensor, tf.Variable],
     axis: int = 0,
diff --git a/ivy/functional/backends/torch/statistical.py b/ivy/functional/backends/torch/statistical.py
index 7ec801d192d5d..73c2b03fde600 100644
--- a/ivy/functional/backends/torch/statistical.py
+++ b/ivy/functional/backends/torch/statistical.py
@@ -290,8 +290,8 @@ def cumprod(
 # TODO: bfloat16 support is added in PyTorch 1.12.1
 @with_unsupported_dtypes(
     {
-        "1.12.1 and below": ("uint8", "float16", "bfloat16"),
-        "1.12.1 and above": ("uint8", "float16"),
+        "1.12.1 and below": ("uint8", "bool", "float16", "bfloat16"),
+        "1.12.1 and above": ("uint8", "bool", "float16"),
     },
     backend_version,
 )
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
index 47d8ef1d894dd..143db6235ea77 100644
--- a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
@@ -153,6 +153,7 @@ def test_cumprod(
     dtype_x_axis_castable=_get_castable_dtype(),
     exclusive=st.booleans(),
     reverse=st.booleans(),
+    test_gradients=st.just(False),
 )
 def test_cumsum(
     *,