Add an array filter to our serialize/deserialize methods and narrow down previous filter

ml-algorithms/build.gradle

@@ -18,6 +18,8 @@ repositories {
 dependencies {
     compileOnly group: 'org.opensearch', name: 'opensearch', version: "${opensearch_version}"
     implementation project(':opensearch-ml-common')
+    implementation "org.opensearch.client:opensearch-rest-client:${opensearch_version}"
+    implementation "org.opensearch:common-utils:${common_utils_version}"
     implementation group: 'org.apache.commons', name: 'commons-text', version: '1.10.0'
     implementation group: 'org.reflections', name: 'reflections', version: '0.9.12'
     implementation group: 'org.tribuo', name: 'tribuo-clustering-kmeans', version: '4.2.1'
@@ -35,6 +37,7 @@ dependencies {
     testImplementation group: 'org.mockito', name: 'mockito-core', version: '4.4.0'
     testImplementation group: 'org.mockito', name: 'mockito-inline', version: '4.4.0'
     implementation group: 'com.google.guava', name: 'guava', version: '31.0.1-jre'
+    implementation group: 'com.google.code.gson', name: 'gson', version: '2.9.1'
     implementation platform("ai.djl:bom:0.19.0")
     implementation group: 'ai.djl.pytorch', name: 'pytorch-model-zoo'
     implementation group: 'ai.djl', name: 'api'

ml-algorithms/src/main/java/org/opensearch/ml/engine/utils/ModelSerDeSer.java

@@ -14,21 +14,56 @@
 import java.io.ByteArrayOutputStream;
 import java.io.IOException;
 import java.io.ObjectOutputStream;
+import java.io.ObjectInputFilter;
 import java.util.Base64;
 
 @UtilityClass
 public class ModelSerDeSer {
-    // Welcome list includes OpenSearch ml plugin classes, JDK common classes and Tribuo libraries.
+    // Accept list includes OpenSearch ml plugin classes, JDK common classes and Tribuo libraries.
     public static final String[] ACCEPT_CLASS_PATTERNS = {
             "java.lang.*",
             "java.util.*",
             "java.time.*",
+            "org.tribuo.*",
+            "com.oracle.labs.mlrg.olcut.provenance.*",
+            "com.oracle.labs.mlrg.olcut.util.*",
+            "[I",
+            "[Z",
+            "[J",
+            "[C",
+            "[D",
+            "[F",
+            "[Ljava.lang.*",
+            "[Lorg.tribuo.*",
+            "[Llibsvm.*",
+            "[[I",
+            "[[Z",
+            "[[J",
+            "[[C",
+            "[[D",
+            "[[F",
+            "[[Ljava.lang.*",
+            "[[Lorg.tribuo.*",
+            "[[Llibsvm.*",
             "org.opensearch.ml.*",
-            "*org.tribuo.*",
             "libsvm.*",
-            "com.oracle.labs.*",
-            "[*",
-            "com.amazon.randomcutforest.*"
+    };
+
+    public static final String[] REJECT_CLASS_PATTERNS = {
+            "java.util.logging.*",
+            "java.util.zip.*",
+            "java.util.jar.*",
+            "java.util.random.*",
+            "java.util.spi.*",
+            "java.util.stream.*",
+            "java.util.regex.*",
+            "java.util.concurrent.*",
+            "java.util.function.*",
+            "java.util.prefs.*",
+            "java.time.zone.*",
+            "java.time.format.*",
+            "java.time.temporal.*",
+            "java.time.chrono.*",
     };
 
     public static String serializeToBase64(Object model) {
@@ -47,11 +82,15 @@ public static byte[] serialize(Object model) {
         }
     }
 
+    // This method has been tested in K-means, Linear Regression, Logistic regression, Anomaly Detection and Random Cut Forest summarization and passed.
     public static Object deserialize(byte[] modelBin) {
         try (ByteArrayInputStream inputStream = new ByteArrayInputStream(modelBin);
              ValidatingObjectInputStream validatingObjectInputStream = new ValidatingObjectInputStream(inputStream)){
             // Validate the model class type to avoid deserialization attack.
-            validatingObjectInputStream.accept(ACCEPT_CLASS_PATTERNS);
+            validatingObjectInputStream
+                    .accept(ACCEPT_CLASS_PATTERNS)
+                    .reject(REJECT_CLASS_PATTERNS)
+                    .setObjectInputFilter(ObjectInputFilter.Config.createFilter("maxdepth=20;maxrefs=5000;maxbytes=10000000;maxarray=100000"));
             return validatingObjectInputStream.readObject();
         } catch (IOException | ClassNotFoundException e) {
             throw new ModelSerDeSerException("Failed to deserialize model.", e.getCause());

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/ad/AnomalyDetectionLibSVMTest.java

@@ -24,11 +24,13 @@
 import org.opensearch.ml.common.input.parameter.ad.AnomalyDetectionLibSVMParams;
 import org.opensearch.ml.common.FunctionName;
 import org.opensearch.ml.common.output.MLPredictionOutput;
+import org.opensearch.ml.engine.utils.ModelSerDeSer;
 import org.tribuo.Dataset;
 import org.tribuo.Example;
 import org.tribuo.Feature;
 import org.tribuo.anomaly.Event;
 import org.tribuo.anomaly.example.AnomalyDataGenerator;
+import org.tribuo.common.libsvm.LibSVMModel;
 
 import java.util.ArrayList;
 import java.util.Iterator;
@@ -117,6 +119,13 @@ public void train() {
         Assert.assertNotNull(model.getContent());
     }
 
+    @Test
+    public void testModelSerDeSer() {
+        MLModel model = anomalyDetection.train(trainDataFrameInput);
+        LibSVMModel deserializedModel = (LibSVMModel) ModelSerDeSer.deserialize(model);
+        Assert.assertNotNull(deserializedModel);
+    }
+
     @Test
     public void trainWithFullParams() {
         AnomalyDetectionLibSVMParams parameters = AnomalyDetectionLibSVMParams.builder().gamma(gamma).nu(nu).cost(1.0).coeff(0.01).epsilon(0.001).degree(1).kernelType(AnomalyDetectionLibSVMParams.ADKernelType.LINEAR).build();

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/clustering/RCFSummarizeTest.java

@@ -5,6 +5,7 @@
 
 package org.opensearch.ml.engine.algorithms.clustering;
 
+import com.amazon.randomcutforest.returntypes.SampleSummary;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Rule;
@@ -17,6 +18,7 @@
 import org.opensearch.ml.common.input.parameter.clustering.RCFSummarizeParams;
 import org.opensearch.ml.common.FunctionName;
 import org.opensearch.ml.common.output.MLPredictionOutput;
+import org.opensearch.ml.engine.utils.ModelSerDeSer;
 
 import static org.opensearch.ml.engine.helper.MLTestHelper.constructTestDataFrame;
 
@@ -61,6 +63,13 @@ public void predictWithTrivalModelExpectBoNorminalOutput() {
         predictions.forEach(row -> Assert.assertTrue(row.getValue(0).intValue() == 0 || row.getValue(0).intValue() == 1));
     }
 
+    @Test
+    public void testModelSerDeSer() {
+        MLModel model = rcfSummarize.train(trainDataFrameInput);
+        SampleSummary deserializedModel = ((SerializableSummary) ModelSerDeSer.deserialize(model)).getSummary();
+        Assert.assertNotNull(deserializedModel);
+    }
+
     @Test
     public void trainAndPredictWithRegularInputExpectNotNullOutput() {
         RCFSummarizeParams parameters = RCFSummarizeParams.builder()

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/regression/LogisticRegressionTest.java

@@ -17,6 +17,8 @@
 import org.opensearch.ml.common.input.MLInput;
 import org.opensearch.ml.common.input.parameter.regression.LogisticRegressionParams;
 import org.opensearch.ml.common.output.MLPredictionOutput;
+import org.opensearch.ml.engine.utils.ModelSerDeSer;
+import org.tribuo.classification.Label;
 
 import static org.opensearch.ml.engine.helper.LogisticRegressionHelper.constructLogisticRegressionPredictionDataFrame;
 import static org.opensearch.ml.engine.helper.LogisticRegressionHelper.constructLogisticRegressionTrainDataFrame;
@@ -109,6 +111,14 @@ public void predict() {
         Assert.assertEquals(2, predictions.size());
     }
 
+    @Test
+    public void testModelSerDeSer() {
+        LogisticRegression classification = new LogisticRegression(parameters);
+        MLModel model = classification.train(trainDataFrameInput);
+        org.tribuo.Model<Label> deserializedModel = (org.tribuo.Model<Label>) ModelSerDeSer.deserialize(model);
+        Assert.assertNotNull(deserializedModel);
+    }
+
     @Test
     public void predictWithoutModel() {
         exceptionRule.expect(IllegalArgumentException.class);