diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/AliasAwareOutputExpression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/AliasAwareOutputExpression.scala
index e1a9e8b5ea810..e7d2ab8538a42 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/AliasAwareOutputExpression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/AliasAwareOutputExpression.scala
@@ -107,6 +107,12 @@ trait AliasAwareQueryOutputOrdering[T <: QueryPlan[T]]
           .flatMap(projectExpression)
           .filter(e => orderingSet.add(e.canonicalized))
           .take(aliasCandidateLimit)
+          // Materialize the bounded result into a strict collection. The `LazyList` above is only
+          // needed so `take` can short-circuit candidate generation; storing `sameOrderExpressions`
+          // as an unforced `LazyList` lets each plan node re-wrap the child ordering's lazy list,
+          // and across a deep projection chain that nesting overflows the stack when the ordering
+          // is later serialized or deeply traversed.
+          .toList
         if (sameOrderings.nonEmpty) {
           Some(sortOrder.copy(child = sameOrderings.head,
             sameOrderExpressions = sameOrderings.tail))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala
index 944ee3b059092..c8212eeb071c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoinExec.scala
@@ -117,7 +117,10 @@ case class BroadcastHashJoinExec private(
       case e: Expression if streamedKeyToBuildKeyMapping.contains(e.canonicalized) =>
         e +: streamedKeyToBuildKeyMapping(e.canonicalized)
     }.asInstanceOf[LazyList[HashPartitioningLike]]
-      .take(conf.broadcastHashJoinOutputPartitioningExpandLimit))
+      .take(conf.broadcastHashJoinOutputPartitioningExpandLimit)
+      // Materialize the bounded expansion into a strict `List` -- never an unforced `LazyList`,
+      // which chained across nodes can overflow the stack when serialized or deeply traversed.
+      .toList)
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ProjectedOrderingAndPartitioningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ProjectedOrderingAndPartitioningSuite.scala
index ec13d48d45f84..d55e5bd68187b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ProjectedOrderingAndPartitioningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ProjectedOrderingAndPartitioningSuite.scala
@@ -84,6 +84,23 @@ class ProjectedOrderingAndPartitioningSuite
     }
   }
 
+  test("SPARK-58323: AliasAware output ordering is strict, not lazy") {
+    // A multi-alias projection makes `sameOrderExpressions` non-trivial. It must be a strict
+    // collection: the underlying `multiTransform` produces a `LazyList`, and storing it unforced
+    // lets each plan node re-wrap the child ordering's lazy list. Across a deep projection chain
+    // that nesting overflows the stack when the ordering is later serialized or deeply traversed.
+    withSQLConf(SQLConf.EXPRESSION_PROJECTION_CANDIDATE_LIMIT.key -> "5") {
+      // id -> {x, y, z} gives sameOrderExpressions.
+      val df = spark.range(2).orderBy($"id").selectExpr("id as x", "id as y", "id as z")
+      val outputOrdering = df.queryExecution.optimizedPlan.outputOrdering
+      assert(outputOrdering.head.sameOrderExpressions.nonEmpty)
+      outputOrdering.foreach { so =>
+        assert(!so.sameOrderExpressions.isInstanceOf[LazyList[_]],
+          s"sameOrderExpressions must be strict, was ${so.sameOrderExpressions.getClass.getName}")
+      }
+    }
+  }
+
   test("SPARK-42049: Improve AliasAwareOutputExpression - ordering - multi-references") {
     val df = spark.range(2).selectExpr("id as a", "id as b")
       .orderBy($"a" + $"b").selectExpr("a as x", "b as y")