CVE-2020-15209 (published 2020-09-25T19:15Z)

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Severity: MEDIUM
CVSS v2 base score: 4.3 (AV:N/AC:M/Au:N/C:N/I:N/A:P)
CVSS v3 base score: 5.9, MEDIUM (CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
cwe_id: CWE-476
cwe_name: NULL Pointer Dereference
description: The product dereferences a pointer that it expects to be valid but is NULL.
extended_description: 
            
            
            
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: Mihai Maruseac
author_date: 2020-09-18 14:12:45-07:00
msg: [tflite] Ensure input tensors don't have `nullptr` buffers.

A crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. That is, by carefully changing the buffer index in the flatbuffer serialization, we can force the TFLite interpreter to consider a read-only tensor to be a read-write one and assume that there is an operator that has this tensor as output, writing to it and allocating memory before the tensor is used as input. If this does not happen, we get memory corruption.

PiperOrigin-RevId: 332524692
Change-Id: I57ef175152a29020af9ab041dc959e5631dce40f
repository: repo_url: https://github.com/tensorflow/tensorflow
repo_name: tensorflow/tensorflow
description: An Open Source Machine Learning Framework for Everyone
repo_language: C++
owner: tensorflow
file_changes: filename: BUILD
change_type: ModificationType.MODIFY
diff: @@ -242,6 +242,7 @@ cc_library(
         ":arena_planner",
         ":external_cpu_backend_context",
         ":graph_info",
+        ":kernel_api",
         ":memory_planner",
         ":minimal_logging",
         ":shared_library",
@@ -469,6 +470,7 @@ cc_test(
         "testdata/add_shared_tensors.bin",
         "testdata/empty_model.bin",
         "testdata/multi_add_flex.bin",
+        "testdata/segment_sum_invalid_buffer.bin",
         "testdata/sparse_tensor.bin",
         "testdata/test_min_runtime.bin",
         "testdata/test_model.bin",

programming_language: Python
filename: subgraph.cc
change_type: ModificationType.MODIFY
diff: @@ -19,6 +19,7 @@ limitations under the License.
 #include <cstdint>
 
 #include "tensorflow/lite/arena_planner.h"
+#include "tensorflow/lite/builtin_ops.h"
 #include "tensorflow/lite/c/common.h"
 #include "tensorflow/lite/context_util.h"
 #include "tensorflow/lite/core/api/tensor_utils.h"
@@ -1030,6 +1031,19 @@ TfLiteStatus Subgraph::Invoke() {
           tensor->data_is_stale) {
         TF_LITE_ENSURE_STATUS(EnsureTensorDataIsReadable(tensor_index));
       }
+      if (tensor->data.raw == nullptr && tensor->bytes > 0) {
+        if (registration.builtin_code == kTfLiteBuiltinReshape && i == 1) {
+          // In general, having a tensor here with no buffer will be an error.
+          // However, for the reshape operator, the second input tensor is only
+          // used for the shape, not for the data. Thus, null buffer is ok.
+          continue;
+        } else {
+          // In all other cases, we need to return an error as otherwise we will
+          // trigger a null pointer dereference (likely).
+          ReportError("Input tensor %d lacks data", tensor_index);
+          return kTfLiteError;
+        }
+      }
     }
 
     if (check_cancelled_func_ != nullptr &&

programming_language: C++
filename: model_test.cc
change_type: ModificationType.MODIFY
diff: @@ -438,24 +438,48 @@ TEST(BasicFlatBufferModel, TestParseModelWithSparseTensor) {
 }
 
 // TODO(b/150072943): Add malformed model with sparse tensor tests.
-TEST(BasicFlatBufferModel, TestHandleMalformedModel) {
-  const auto model_paths = {
-      // These models use the same tensor as both input and ouput of a node
-      "tensorflow/lite/testdata/add_shared_tensors.bin",
-  };
-
-  for (const auto& model_path : model_paths) {
-    std::unique_ptr<tflite::FlatBufferModel> model =
-        FlatBufferModel::BuildFromFile(model_path);
-    ASSERT_NE(model, nullptr);
-
-    tflite::ops::builtin::BuiltinOpResolver resolver;
-    InterpreterBuilder builder(*model, resolver);
-    std::unique_ptr<Interpreter> interpreter;
-    ASSERT_EQ(builder(&interpreter), kTfLiteOk);
-    ASSERT_NE(interpreter, nullptr);
-    ASSERT_NE(interpreter->AllocateTensors(), kTfLiteOk);
-  }
+
+// The models here have at least a node that uses the same tensor as input and
+// output. This causes segfaults when trying to eval the operator, hence we try
+// to prevent this scenario. The earliest place we can check this is in
+// `AllocateTensors`, hence the test checks that `interpreter->AllocateTensors`
+// detects these bad models.
+TEST(BasicFlatBufferModel, TestHandleMalformedModelReuseTensor) {
+  const auto model_path =
+      "tensorflow/lite/testdata/add_shared_tensors.bin";
+
+  std::unique_ptr<tflite::FlatBufferModel> model =
+      FlatBufferModel::BuildFromFile(model_path);
+  ASSERT_NE(model, nullptr);
+
+  tflite::ops::builtin::BuiltinOpResolver resolver;
+  InterpreterBuilder builder(*model, resolver);
+  std::unique_ptr<Interpreter> interpreter;
+  ASSERT_EQ(builder(&interpreter), kTfLiteOk);
+  ASSERT_NE(interpreter, nullptr);
+  ASSERT_NE(interpreter->AllocateTensors(), kTfLiteOk);
+}
+
+// The models here have a buffer index for a tensor pointing to a null buffer.
+// This results in the tensor being interpreted as read-write, but the model
+// assumes the tensor is read-only. As such, `interpreter->Invoke()` would
+// segfault if no precondition check is added. The test checks that the
+// precondition check exists.
+TEST(BasicFlatBufferModel, TestHandleMalformedModelInvalidBuffer) {
+  const auto model_path =
+      "tensorflow/lite/testdata/segment_sum_invalid_buffer.bin";
+
+  std::unique_ptr<tflite::FlatBufferModel> model =
+      FlatBufferModel::BuildFromFile(model_path);
+  ASSERT_NE(model, nullptr);
+
+  tflite::ops::builtin::BuiltinOpResolver resolver;
+  InterpreterBuilder builder(*model, resolver);
+  std::unique_ptr<Interpreter> interpreter;
+  ASSERT_EQ(builder(&interpreter), kTfLiteOk);
+  ASSERT_NE(interpreter, nullptr);
+  ASSERT_EQ(interpreter->AllocateTensors(), kTfLiteOk);
+  ASSERT_NE(interpreter->Invoke(), kTfLiteOk);
 }
 
 // TODO(aselle): Add tests for serialization of builtin op data types.

programming_language: C++