diff --git a/csrc/device_lower/utils.cpp b/csrc/device_lower/utils.cpp
index 581fb77253c..1b4d14fe1cd 100644
--- a/csrc/device_lower/utils.cpp
+++ b/csrc/device_lower/utils.cpp
@@ -2130,7 +2130,46 @@ IterDomain* getConcreteLoopID(IterDomain* id) {
     return promotion;
   } else {
     const auto& ca_map = FusionInfoGuard::current()->caMap();
-    return ca_map.getConcreteMappedID(id, IdMappingMode::LOOP);
+    auto disjoint_set = ca_map.disjointSetOf(id, IdMappingMode::LOOP);
+    auto concrete = ca_map.getConcreteMappedID(id, IdMappingMode::LOOP);
+
+    // The following code is a WAR fix of issue-5326
+    // The CA map's concrete ID may have an incompatible extent.
+    // Similar to IdModel's loop promotion, we should prefer non-broadcast IDs
+    // with the largest extent in the loop group.
+    //
+    // Check if the concrete ID has a broadcast or size-one extent while
+    // other IDs in the group have larger extents.
+    bool concrete_is_broadcast_or_one = concrete->isBroadcast() ||
+        (concrete->extent()->isConstInt() &&
+         concrete->extent()->evaluate().as<int64_t>() == 1);
+
+    if (concrete_is_broadcast_or_one && disjoint_set->vector().size() > 1) {
+      // Look for a non-broadcast ID with a larger extent in the same loop group
+      IterDomain* better_concrete = nullptr;
+      int64_t max_extent = 1;
+
+      for (auto loop_id : disjoint_set->vector()) {
+        if (loop_id->isBroadcast()) {
+          continue;
+        }
+
+        if (loop_id->extent()->isConstInt()) {
+          auto extent_val = loop_id->extent()->evaluate().as<int64_t>();
+          if (extent_val > max_extent) {
+            max_extent = extent_val;
+            better_concrete = loop_id;
+          }
+        }
+      }
+
+      // If we found a better candidate, use it instead
+      if (better_concrete != nullptr && better_concrete != concrete) {
+        concrete = better_concrete;
+      }
+    }
+
+    return concrete;
   }
 }
 
diff --git a/tests/python/direct/test_repro.py b/tests/python/direct/test_repro.py
index eabad2eb72b..0bfe970311f 100644
--- a/tests/python/direct/test_repro.py
+++ b/tests/python/direct/test_repro.py
@@ -4544,3 +4544,50 @@ def nvfuser_fusion_id0(fd: FusionDefinition) -> None:
         torch.testing.make_tensor((24578,), dtype=torch.bfloat16, device="cuda:0"),
     ]
     fd.validate(inputs)
+
+
+def test_ca_map_concrete_loop_id(nvfuser_direct_test):
+    def nvfuser_fusion_id10(fd: FusionDefinition) -> None:
+        T0 = fd.define_tensor(
+            shape=[16, 1, 1],
+            contiguity=[True, None, None],
+            dtype=DataType.Float,
+            is_cpu=False,
+        )
+        T1 = fd.define_tensor(
+            shape=[1, 1, 1024],
+            contiguity=[None, None, True],
+            dtype=DataType.Float,
+            is_cpu=False,
+        )
+        T2 = fd.ops.sub(T0, T0)
+        T3 = fd.ops.exp(T2)
+        T4 = fd.ops.reciprocal(T3)
+        T5 = fd.ops.mul(T3, T4)
+        T6 = fd.ops.broadcast(T5, is_broadcast_dim=[False, False, True, False])
+        S7 = fd.ops.size(T5, dim=1)
+        S8 = fd.define_scalar(16, dtype=DataType.Int)
+        S9 = fd.define_scalar(64, dtype=DataType.Int)
+        T11 = fd.ops.reshape(T1, new_shape=[S7, S7, S8, S9])
+        T12 = fd.ops.permute(T11, dims=[0, 2, 1, 3])
+        T13 = fd.ops.squeeze(T12, dims=[0], squeeze_expanded=True)
+        T14 = fd.ops.permute(T13, dims=[0, 2, 1])
+        T15 = fd.ops.broadcast(T14, is_broadcast_dim=[False, True, False, False])
+        T16 = fd.ops.mul(T6, T15)
+        T17 = fd.ops.squeeze(T16, dims=[3], squeeze_expanded=True)
+        T18 = fd.ops.broadcast(T17, is_broadcast_dim=[True, False, False, False])
+        T19 = fd.ops.permute(T18, dims=[0, 2, 1, 3])
+        S20 = fd.define_scalar(1024, dtype=DataType.Int)
+        T22 = fd.ops.reshape(T19, new_shape=[S7, S7, S20])
+        fd.add_output(T5)
+        fd.add_output(T13)
+        fd.add_output(T22)
+
+    with FusionDefinition() as fd:
+        nvfuser_fusion_id10(fd)
+
+    inputs = [
+        torch.testing.make_tensor((16, 1, 1), dtype=torch.float32, device="cuda:0"),
+        torch.testing.make_tensor((1, 1, 1024), dtype=torch.float32, device="cuda:0"),
+    ]
+    fd.validate(inputs)