diff --git a/backends/cadence/utils/facto_util.py b/backends/cadence/utils/facto_util.py
index 001fc882685..b5c5683ab5d 100644
--- a/backends/cadence/utils/facto_util.py
+++ b/backends/cadence/utils/facto_util.py
@@ -266,6 +266,9 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
             tensor_constraints.extend(
                 [
                     cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
+                    # Avoid NaN/Inf values that expose clamp NaN handling bugs
+                    cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
+                    cp.Value.Le(lambda deps, dtype, struct: 2**4),
                 ]
             )
         case "rsqrt.default":
@@ -456,6 +459,7 @@ def apply_scalar_contraints(op_name: str) -> list[ScalarDtype]:
             | "mul.Scalar"
             | "div.Scalar"
             | "constant_pad_nd.default"
+            | "clamp.default"
         ):
             return [ScalarDtype.int]
         case "full.default":
@@ -483,7 +487,32 @@ def facto_testcase_gen(  # noqa: C901
                         cp.Size.Le(lambda deps, r, d: 2**2),
                     ]
                 )
-            if in_spec.name == "max_val":  # hardtanh
+            # Special handling for clamp.default to ensure min < max with sufficient gap (at least 2) and never None
+            if op_name == "clamp.default":
+                if in_spec.name == "min":
+                    # min must always be provided (not None) and bounded, leave room for max
+                    spec.inspec[index].constraints.extend(
+                        [
+                            cp.Optional.Eq(lambda deps: False),  # Never None
+                            cp.Value.Ge(lambda deps, dtype: -(2**4)),
+                            cp.Value.Le(
+                                lambda deps, dtype: 2**4 - 2
+                            ),  # Leave room for max (at least 2 units)
+                        ]
+                    )
+                elif in_spec.name == "max":
+                    # max must always be provided (not None), be >= min + 2 (sufficient gap), and bounded
+                    spec.inspec[index].deps = [0, 1]  # deps on input tensor and min
+                    spec.inspec[index].constraints.extend(
+                        [
+                            cp.Optional.Eq(lambda deps: False),  # Never None
+                            cp.Value.Ge(
+                                lambda deps, dtype: deps[1] + 2
+                            ),  # max >= min + 2 (sufficient gap)
+                            cp.Value.Le(lambda deps, dtype: 2**4),
+                        ]
+                    )
+            elif in_spec.name == "max_val":  # hardtanh
                 spec.inspec[index].deps = [0, 1]
                 spec.inspec[index].constraints.extend(
                     [cp.Value.Ge(lambda deps, _: deps[1])]