diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
index 2f78dd30b3af7..9ed400bd9eafe 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
@@ -644,4 +644,5 @@ private class AFTCostFun(
  */
 private[regression] case class AFTPoint(features: Vector, label: Double, censor: Double) {
   require(censor == 1.0 || censor == 0.0, "censor of class AFTPoint must be 1.0 or 0.0")
+  require(label > 0.0, "label of AFTPoint must be positive")
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
index 0fdfdf37cf38d..4eb4fc50ef3c4 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
@@ -19,14 +19,15 @@ package org.apache.spark.ml.regression
 
 import scala.util.Random
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.ml.util.TestingUtils._
 import org.apache.spark.mllib.random.{ExponentialGenerator, WeibullGenerator}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.{DataFrame, Row, SparkSession}
 
 class AFTSurvivalRegressionSuite
   extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@@ -400,6 +401,19 @@ class AFTSurvivalRegressionSuite
     val trainer = new AFTSurvivalRegression()
     trainer.fit(dataset)
   }
+
+  test("SPARK-19234: Fail fast on zero-valued labels") {
+    val dataset = spark.createDataFrame(Seq(
+      (1.218, 1.0, Vectors.dense(1.560, -0.605)),
+      (0.000, 0.0, Vectors.dense(0.346, 2.158)), // ← generates error; zero labels invalid
+      (4.199, 0.0, Vectors.dense(0.795, -0.226)))).toDF("label", "censor", "features")
+    val aft = new AFTSurvivalRegression()
+    withClue("label of AFTPoint must be positive") {
+      intercept[SparkException] {
+        aft.fit(dataset)
+      }
+    }
+  }
 }
 
 object AFTSurvivalRegressionSuite {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
index f1ed568d5e60a..9795550f64ce2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
@@ -64,7 +64,7 @@ object MLTestingUtils extends SparkFunSuite {
     actuals.foreach(actual => check(expected, actual))
 
     val dfWithStringLabels = spark.createDataFrame(Seq(
-      ("0", 1, Vectors.dense(0, 2, 3), 0.0)
+      ("1", 1, Vectors.dense(0, 2, 3), 0.0)
     )).toDF("label", "weight", "features", "censor")
     val thrown = intercept[IllegalArgumentException] {
       estimator.fit(dfWithStringLabels)
@@ -156,7 +156,6 @@ object MLTestingUtils extends SparkFunSuite {
       featuresColName: String = "features",
       censorColName: String = "censor"): Map[NumericType, DataFrame] = {
     val df = spark.createDataFrame(Seq(
-      (0, Vectors.dense(0)),
       (1, Vectors.dense(1)),
       (2, Vectors.dense(2)),
       (3, Vectors.dense(3)),