CVE-2022-35966 (C++, published 2022-09-16T21:15Z)
Description: value: TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. 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.
Severity: nan (CVSS v3 base score: 7.5)
Weakness: NVD-CWE-noinfo \u2014 Insufficient Information
There is insufficient information about the issue to classify it; details are unkown or unspecified.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/core/kernels/quantized_pooling_ops.cc
tensorflow/core/kernels/quantized_pooling_ops_test.cc
tensorflow/python/kernel_tests/quantization_ops/quantization_ops_test.py
Commit message: Fix QuantizedAvgPool invalid rank issue.

Passing in an invalid rank in eager mode led to a runtime crash.
Updated the runtime error to match the one generated by the
shape function for better consistency.

PiperOrigin-RevId: 462419362


Vulnerable code:
#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/platform/errors.h"
    const float min_input = context->input(1).flat<float>()(0);
    const float max_input = context->input(2).flat<float>()(0);
    auto min_input_tensor = context->input(1);
    auto max_input_tensor = context->input(2);
    OP_REQUIRES(
        context, min_input_tensor.NumElements() == 1,
        errors::InvalidArgument(
            "min_input must be a scalar float value, got tensor with shape ",
            min_input_tensor.shape()));
    OP_REQUIRES(
        context, max_input_tensor.NumElements() == 1,
        errors::InvalidArgument(
            "max_input must be a scalar float value, got tensor with shape ",
            max_input_tensor.shape()));
    const float min_input = context->input(1).flat<float>()(0);
    const float max_input = context->input(2).flat<float>()(0);
  AddInputFromArray<float>(TensorShape({1}), {input_min});
  AddInputFromArray<float>(TensorShape({1}), {input_max});
  AddInputFromArray<float>(TensorShape({1}), {input_min});
  AddInputFromArray<float>(TensorShape({1}), {input_max});

Fixed code:
#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/platform/errors.h"
    const Tensor& min_input_tensor = context->input(1);
    const Tensor& max_input_tensor = context->input(2);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(min_input_tensor.shape()),
                errors::InvalidArgument(
                    "min_input shape must be rank 0 but is rank ",
                    min_input_tensor.dims(),
                    ", received shape: ", min_input_tensor.shape()));
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(max_input_tensor.shape()),
                errors::InvalidArgument(
                    "max_input shape must be rank 0 but is rank ",
                    max_input_tensor.dims(),
                    ", received shape: ", max_input_tensor.shape()));
    const float min_input = context->input(1).scalar<float>()();
    const float max_input = context->input(2).scalar<float>()();
    const Tensor& min_input_tensor = context->input(1);
    const Tensor& max_input_tensor = context->input(2);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(min_input_tensor.shape()),
                errors::InvalidArgument(
                    "min_input shape must be rank 0 but is rank ",
                    min_input_tensor.dims(),
                    ", received shape: ", min_input_tensor.shape()));
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(max_input_tensor.shape()),
                errors::InvalidArgument(
                    "max_input shape must be rank 0 but is rank ",
                    max_input_tensor.dims(),
                    ", received shape: ", max_input_tensor.shape()));
    const float min_input = context->input(1).scalar<float>()();
    const float max_input = context->input(2).scalar<float>()();
  AddInputFromArray<float>(TensorShape({}), {input_min});
  AddInputFromArray<float>(TensorShape({}), {input_max});
  AddInputFromArray<float>(TensorShape({}), {input_min});
  AddInputFromArray<float>(TensorShape({}), {input_max});
class QuantizedAvgPoolingOpTest(test_util.TensorFlowTestCase):

  @test_util.run_in_graph_and_eager_modes
  def test_invalid_inputs(self):
    inputs = constant_op.constant(
        np.uint8(0), shape=[3, 3, 3, 3], dtype=dtypes.quint8)
    ksize = [1, 1, 1, 1]
    strides = [1, 1, 1, 1]
    padding = "SAME"

    with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
                                "must be.* rank 0"):
      self.evaluate(
          nn_ops.quantized_avg_pool(
              input=inputs,
              min_input=[],
              max_input=1.0,
              ksize=ksize,
              strides=strides,
              padding=padding))

    with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
                                "must be.* rank 0"):
      self.evaluate(
          nn_ops.quantized_avg_pool(
              input=inputs,
              min_input=0.0,
              max_input=[],
              ksize=ksize,
              strides=strides,
              padding=padding))


class QuantizedMaxPoolingOpTest(test_util.TensorFlowTestCase):

  @test_util.run_in_graph_and_eager_modes
  def test_invalid_inputs(self):
    inputs = constant_op.constant(
        np.uint8(0), shape=[3, 3, 3, 3], dtype=dtypes.quint8)
    ksize = [1, 1, 1, 1]
    strides = [1, 1, 1, 1]
    padding = "SAME"

    with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
                                "must be.* rank 0"):
      self.evaluate(
          nn_ops.quantized_max_pool(
              input=inputs,
              min_input=[],
              max_input=1.0,
              ksize=ksize,
              strides=strides,
              padding=padding))

    with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
                                "must be.* rank 0"):
      self.evaluate(
          nn_ops.quantized_max_pool(
              input=inputs,
              min_input=0.0,
              max_input=[],
              ksize=ksize,
              strides=strides,
              padding=padding))