CVE-2022-35959 (published 2022-09-16T20:15Z)

TensorFlow is an open source platform for machine learning. The implementation of `AvgPool3DGradOp` does not fully validate the input `orig_input_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 9178ac9d6389bdc54638ab913ea0e419234d14eb. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

CVSS v3 base score: 7.5, HIGH (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H)

Weakness information:
cwe_id: CWE-617
cwe_name: Reachable Assertion
description: The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.
extended_description: 
            While assertion is good for catching logic errors and reducing the chances of reaching more serious vulnerability conditions, it can still lead to a denial of service.
            For example, if a server handles multiple simultaneous connections, and an assert() occurs in one single connection that causes all other connections to be dropped, this is a reachable assertion that leads to a denial of service.
         
         

Fix information:
repo_url: https://github.com/tensorflow/tensorflow
commit_details: repo_url: https://github.com/tensorflow/tensorflow
author: A. Unique TensorFlower
author_date: 2022-07-15 14:49:36-07:00
msg: Fix security vulnerability with AvgPool3DGrad.

PiperOrigin-RevId: 461244371
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: @@ -403,6 +403,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:xla_builder",
         "//tensorflow/compiler/xla/client:xla_computation",
         "//tensorflow/compiler/xla/service:hlo",
+        "//tensorflow/core/util:overflow",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",

programming_language: Python
filename: xla_op_kernel.cc
change_type: ModificationType.MODIFY
diff: @@ -30,6 +30,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/status_macros.h"
 #include "tensorflow/core/common_runtime/dma_helper.h"
 #include "tensorflow/core/platform/errors.h"
+#include "tensorflow/core/util/overflow.h"
 
 namespace tensorflow {
 
@@ -443,6 +444,16 @@ Status XlaOpKernelContext::ConstantInputAsShape(int index, TensorShape* shape,
   TF_RETURN_IF_ERROR(ConstantInput(index, &literal, mode));
   std::vector<int64_t> dims;
   TF_RETURN_IF_ERROR(LiteralToInt64Vector(literal, &dims));
+
+  int64_t num_elements = 1;
+  for (auto i = dims.begin(); i != dims.end(); ++i) {
+    num_elements = MultiplyWithoutOverflow(num_elements, *i);
+    if (num_elements < 0)
+      return errors::InvalidArgument(
+          "The total elements specified by orig_input_shape is too large.",
+          "Encountered overflow after multiplying", *i,
+          ", result: ", num_elements);
+  }
   *shape = TensorShape(dims);
   return OkStatus();
 }

programming_language: C++
filename: pooling_ops_3d.cc
change_type: ModificationType.MODIFY
diff: @@ -523,7 +523,7 @@ class AvgPooling3dGradOp : public OpKernel {
     TensorShape output_shape;
     auto shape_vec = tensor_in_shape.vec<int32>();
     for (int64_t i = 0; i < tensor_in_shape.NumElements(); ++i) {
-      output_shape.AddDim(shape_vec(i));
+      OP_REQUIRES_OK(context, output_shape.AddDimWithStatus(shape_vec(i)));
     }
 
     Tensor* output;

programming_language: C++
filename: BUILD
change_type: ModificationType.MODIFY
diff: @@ -500,6 +500,7 @@ cuda_py_test(
     srcs = ["pooling_ops_3d_test.py"],
     deps = [
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:dtypes",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:nn_grad",
         "//tensorflow/python:nn_ops",

programming_language: Python
filename: pooling_ops_3d_test.py
change_type: ModificationType.MODIFY
diff: @@ -18,6 +18,7 @@ import numpy as np
 
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import test_util
@@ -67,7 +68,7 @@ class PoolingTest(test.TestCase):
     # Initializes the input tensor with array containing incrementing
     # numbers from 1.
     x = [f * 1.0 for f in range(1, total_size + 1)]
-    with self.cached_session(use_gpu=use_gpu) as sess:
+    with self.cached_session(use_gpu=use_gpu):
       t = constant_op.constant(x, shape=input_sizes)
       window = [1] + list(window) + [1]
       strides = [1] + list(strides) + [1]
@@ -124,6 +125,23 @@ class PoolingTest(test.TestCase):
         padding="SAME",
         expected=expected_output)
 
+  def testMaxPool3dGrad(self):
+    with self.assertRaises(
+        (errors.ResourceExhaustedError, errors.InvalidArgumentError)):
+      with self.cached_session():
+        orig_input_shape = constant_op.constant(
+            1879048192, shape=[5], dtype=dtypes.int32)
+        grad = constant_op.constant(
+            1, shape=[1, 3, 2, 4, 2], dtype=dtypes.float32)
+        t = gen_nn_ops.AvgPool3DGrad(
+            orig_input_shape=orig_input_shape,
+            grad=grad,
+            ksize=[1, 1, 1, 1, 1],
+            strides=[1, 1, 1, 1, 1],
+            padding="SAME",
+            data_format="NDHWC")
+        self.evaluate(t)
+
   def testMaxPool3dValidPadding(self):
     expected_output = [40.0, 41.0, 42.0]
     self._VerifyValues(

programming_language: Python