CVE-2022-35981 (published 2022-09-16T22:15Z)

TensorFlow is an open source platform for machine learning. `FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4. 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: 2022-07-18 14:57:29-07:00
msg: Fix security vulnerability with FractionalMaxPoolGrad

PiperOrigin-RevId: 461722693
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: fractional_max_pool_op.cc
change_type: ModificationType.MODIFY
diff: @@ -19,12 +19,13 @@ limitations under the License.
 #include <random>
 #include <vector>
 
-#include "tensorflow/core/kernels/fractional_pool_common.h"
-
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/op_requires.h"
+#include "tensorflow/core/kernels/fractional_pool_common.h"
 #include "tensorflow/core/lib/random/random.h"
+#include "tensorflow/core/platform/errors.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/util/guarded_philox_random.h"
@@ -352,7 +353,9 @@ class FractionalMaxPoolGradOp : public OpKernel {
         output_size[2] * output_size[1] * output_size[0];
     for (int64_t i = 0; i < num_reshaped_cols; ++i) {
       for (int64_t j = 0; j < output_size[3]; ++j) {
-        DCHECK_EQ(tensor_out_dup_mat(j, i), tensor_out_mat(j, i));
+        OP_REQUIRES(context, tensor_out_dup_mat(j, i) == tensor_out_mat(j, i),
+                    errors::InvalidArgument(
+                        "tensor_out_dup is not the same as tensor_out"));
       }
     }
 
@@ -369,11 +372,12 @@ class FractionalMaxPoolGradOp : public OpKernel {
 
     for (int index = 0; index < num_total_outputs; ++index) {
       int input_backprop_index = out_arg_max_flat(index);
-      // According to maxpooling_op.cc, the performance impact below is small.
-      CHECK(input_backprop_index >= 0 &&
-            input_backprop_index < num_total_inputs)
-          << "Invalid input backprop index: " << input_backprop_index << ", "
-          << num_total_inputs;
+      OP_REQUIRES(
+          context,
+          input_backprop_index >= 0 && input_backprop_index < num_total_inputs,
+          errors::InvalidArgument(
+              "Invalid input backprop index: ", input_backprop_index, ", ",
+              num_total_inputs));
       input_backprop_flat(input_backprop_index) += out_backprop_flat(index);
     }
   }

programming_language: C++
filename: fractional_max_pool_op_test.py
change_type: ModificationType.MODIFY
diff: @@ -124,7 +124,7 @@ class FractionalMaxPoolTest(test.TestCase):
     Returns:
       None
     """
-    with self.cached_session() as sess:
+    with self.cached_session():
       p, r, c = nn_ops.fractional_max_pool_v2(
           input_tensor,
           pooling_ratio,
@@ -155,7 +155,7 @@ class FractionalMaxPoolTest(test.TestCase):
           overlapping))
       rand_mat = self._PRNG.randint(10, size=tensor_shape)
       pooling_ratio = [1, math.sqrt(2), math.sqrt(2), 1]
-      with self.cached_session() as sess:
+      with self.cached_session():
         p, r, c = nn_ops.fractional_max_pool_v2(
             rand_mat,
             pooling_ratio,
@@ -630,6 +630,29 @@ class FractionalMaxPoolGradTest(test.TestCase):
       self.assertAllClose(expected_input_backprop_overlapping,
                           input_backprop_overlapping)
 
+  def testInvalidSeqRaiseErrorForFractionalMaxPoolGrad(self):
+    with self.assertRaises(errors.InvalidArgumentError):
+      with self.cached_session() as _:
+        overlapping = True
+        orig_input = constant_op.constant(
+            .453409232, shape=[1, 7, 13, 1], dtype=dtypes.float32)
+        orig_output = constant_op.constant(
+            .453409232, shape=[1, 7, 13, 1], dtype=dtypes.float32)
+        out_backprop = constant_op.constant(
+            .453409232, shape=[1, 7, 13, 1], dtype=dtypes.float32)
+        row_pooling_sequence = constant_op.constant(
+            0, shape=[5], dtype=dtypes.int64)
+        col_pooling_sequence = constant_op.constant(
+            0, shape=[5], dtype=dtypes.int64)
+        t = gen_nn_ops.FractionalMaxPoolGrad(
+            orig_input=orig_input,
+            orig_output=orig_output,
+            out_backprop=out_backprop,
+            row_pooling_sequence=row_pooling_sequence,
+            col_pooling_sequence=col_pooling_sequence,
+            overlapping=overlapping)
+        self.evaluate(t)
+
 
 if __name__ == "__main__":
   test.main()

programming_language: Python