CVE-2022-35952 (Python, published 2022-09-16T20:15Z)
Description: value: TensorFlow is an open source platform for machine learning. The `UnbatchGradOp` function takes an argument `id` that is assumed to be a scalar. A nonscalar `id` can trigger a `CHECK` failure and crash the program. It also requires its argument `batch_index` to contain three times the number of elements as indicated in its `batch_index.dim_size(0)`. An incorrect `batch_index` can trigger a `CHECK` failure and crash the program. We have patched the issue in GitHub commit 5f945fc6409a3c1e90d6970c9292f805f6e6ddf2. 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: CWE-617 \u2014 Reachable Assertion
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.
Repository: https://github.com/tensorflow/tensorflow
Files changed:
tensorflow/core/kernels/batch_kernels.cc
tensorflow/python/ops/batch_ops_test.py
Commit message: Fix security vulnerability with UnbatchGradKernel

PiperOrigin-RevId: 460992964


Vulnerable code:

Fixed code:
    const Tensor& batch_key_t = context->input(3);
    if (batch_key_t.NumElements() != 1) {
      return errors::InvalidArgument("Expected `id` to be scalar. Received ",
                                     batch_key_t.DebugString());
    }
      if (batch_index_t.NumElements() != batch_index_t.dim_size(0) * 3) {
        return errors::InvalidArgument(
            "batch_index should contain ", batch_index_t.dim_size(0) * 3,
            " elements. Received ", batch_index_t.NumElements());
      }
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import errors
from tensorflow.python.ops import random_ops
  def testUnbatchGradInvalidId(self):
    with self.assertRaises(errors.InvalidArgumentError):
      self.evaluate(
          gen_batch_ops.unbatch_grad(
              original_input=constant_op.constant([1]),
              batch_index=constant_op.constant([
                  [0, 0, 0],
              ], dtype=dtypes.int64),
              grad=constant_op.constant([
                  1,
              ]),
              id=constant_op.constant([
                  1,
                  1,
              ], dtype=dtypes.int64)))

  def testUnbatchGradInvalidBatchId(self):
    with self.assertRaises(errors.InvalidArgumentError):
      self.evaluate(
          gen_batch_ops.unbatch_grad(
              original_input=constant_op.constant([1]),
              batch_index=constant_op.constant([
                  [0, 0],
              ], dtype=dtypes.int64),
              grad=constant_op.constant([
                  1,
              ]),
              id=constant_op.constant([
                  1,
              ], dtype=dtypes.int64)))

  def testUnbatchGradInvalidArgs(self):
    original_input = random_ops.random_uniform(
        shape=(3, 1), dtype=dtypes.float64, maxval=None)
    batch_index = random_ops.random_uniform(
        shape=(3, 1), dtype=dtypes.int64, maxval=65536)
    grad = random_ops.random_uniform(
        shape=(3, 1), dtype=dtypes.float64, maxval=None)
    batch_id = random_ops.random_uniform(
        shape=(3, 1), dtype=dtypes.int64, maxval=65536)
    with self.assertRaises(errors.InvalidArgumentError):
      self.evaluate(
          gen_batch_ops.unbatch_grad(
              original_input=original_input,
              batch_index=batch_index,
              grad=grad,
              id=batch_id,
              container="",
              shared_name="",
              name=""))