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Moderate severityNVD Advisory· Published Sep 16, 2022· Updated Apr 23, 2025

`CHECK` failures in `FractionalAvgPoolGrad` in TensorFlow

CVE-2022-35963

Description

TensorFlow is an open source platform for machine learning. The implementation of FractionalAvgPoolGrad does not fully validate the input orig_input_tensor_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 03a659d7be9a1154fdf5eeac221e5950fec07dad. 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.

Affected packages

Versions sourced from the GitHub Security Advisory.

PackageAffected versionsPatched versions
tensorflowPyPI
< 2.7.22.7.2
tensorflowPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflowPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-cpuPyPI
< 2.7.22.7.2
tensorflow-cpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-cpuPyPI
>= 2.9.0, < 2.9.12.9.1
tensorflow-gpuPyPI
< 2.7.22.7.2
tensorflow-gpuPyPI
>= 2.8.0, < 2.8.12.8.1
tensorflow-gpuPyPI
>= 2.9.0, < 2.9.12.9.1

Affected products

1

Patches

1
03a659d7be9a

Fix security vulnerability with FractionalAvgPoolGrad

https://github.com/tensorflow/tensorflowA. Unique TensorFlowerJul 21, 2022via ghsa
commit
2 files changed · +49 −2
  • tensorflow/core/kernels/fractional_avg_pool_op.cc+28 −2 modified
    @@ -12,22 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+
 #define EIGEN_USE_THREADS
 
 #include <algorithm>
 #include <cmath>
 #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/kernels/fractional_pool_common.h"
 #include "tensorflow/core/lib/random/random.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/util/guarded_philox_random.h"
+#include "tensorflow/core/util/overflow.h"
 
 namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
@@ -241,7 +242,32 @@ class FractionalAvgPoolGradOp : public OpKernel {
                     orig_input_tensor_shape.NumElements() == 4,
                 errors::InvalidArgument("original input tensor shape must be"
                                         "1-dimensional and 4 elements"));
+    int64_t num_elements = 1;
+    for (int i = 0; i < orig_input_tensor_shape.dims(); i++) {
+      OP_REQUIRES(context, orig_input_tensor_shape.dim_size(i) > 0,
+                  errors::InvalidArgument(
+                      "orig_input_tensor_shape must be positive, got: ",
+                      orig_input_tensor_shape.dim_size(i)));
+      num_elements = MultiplyWithoutOverflow(
+          num_elements, orig_input_tensor_shape.dim_size(i));
+      OP_REQUIRES(
+          context, num_elements > 0,
+          errors::InvalidArgument(
+              "The total elements specified by orig_input_tensor_shape",
+              " is too large. Encountered overflow after multiplying ",
+              orig_input_tensor_shape.dim_size(i), ", result: ", num_elements));
+    }
+
     const Tensor& out_backprop = context->input(1);
+    OP_REQUIRES(context, out_backprop.dims() == 4,
+                errors::InvalidArgument("out_backprop must be 4-dimensional"));
+    for (int i = 0; i < out_backprop.dims(); i++) {
+      OP_REQUIRES(context, out_backprop.dim_size(i) > 0,
+                  errors::InvalidArgument(
+                      "out_backprop must be positive for all dimension, got:",
+                      out_backprop.dim_size(i)));
+    }
+
     const Tensor& row_seq_tensor = context->input(2);
     const Tensor& col_seq_tensor = context->input(3);
  • tensorflow/python/kernel_tests/nn_ops/fractional_avg_pool_op_test.py+21 −0 modified
    @@ -541,6 +541,27 @@ def testLargePoolingRatioThroughGradientError(self):
           delta=1e-2)
       self.assertLess(gradient_error, error_margin)
 
+  def testInvalidSeqRaiseErrorForFractionalAvgPoolGrad(self):
+    with self.assertRaises((errors.InvalidArgumentError, ValueError)):
+      with self.cached_session() as _:
+        overlapping = True
+        orig_input_tensor_shape = constant_op.constant(
+            -1879048192, shape=[4], dtype=dtypes.int64)
+        out_backprop = constant_op.constant([],
+                                            shape=[0, 0, 0, 0],
+                                            dtype=dtypes.float64)
+        row_pooling_sequence = constant_op.constant(
+            1, shape=[4], dtype=dtypes.int64)
+        col_pooling_sequence = constant_op.constant(
+            1, shape=[4], dtype=dtypes.int64)
+        t = gen_nn_ops.fractional_avg_pool_grad(
+            orig_input_tensor_shape=orig_input_tensor_shape,
+            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()

Vulnerability mechanics

Generated by null/stub on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

5

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