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TensorFlowGHSA-79fv-9865-4qcv

Heap buffer overflow in `MaxPoolGrad`

TensorFlow

CVE-2021-29579 · Published May 21, 2021 · updated Jul 8, 2026

Low2.5
Fix: upgrade to 2.1.4 or later (4 fixed versions below)
GitHub advisory

Impact

The implementation of tf.raw_ops.MaxPoolGrad is vulnerable to a heap buffer overflow:

```python

import tensorflow as tf

orig_input = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)

orig_output = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)

grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

ksize = [1, 1, 1, 1]

strides = [1, 1, 1, 1]

padding = "SAME"

tf.raw_ops.MaxPoolGrad(

orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,

strides=strides, padding=padding, explicit_paddings=[])

`

The [implementation](https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid:

```cc

for (int index = out_start; index < out_end; ++index) {

int input_backprop_index = out_arg_max_flat(index);

FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);

input_backprop_flat(input_backprop_index) += out_backprop_flat(index);

}

`

Whereas accesses to input_backprop_flat are guarded by FastBoundsCheck, the indexing in out_backprop_flat can result in OOB access.

Patches

We have patched the issue in GitHub commit...

Affected versions

PackageAffectedFixed in
tensorflow
PyPI
< 2.1.42.1.4
>= 2.2.0, < 2.2.32.2.3
>= 2.3.0, < 2.3.32.3.3
>= 2.4.0, < 2.4.22.4.2
Details and references

### Impact The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf orig_input = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32) orig_output = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32) grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) ksize = [1, 1, 1, 1] strides = [1, 1, 1, 1] padding = "SAME" tf.raw_ops.MaxPoolGrad( orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize, strides=strides, padding=padding, explicit_paddings=[]) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid: ```cc for (int index = out_start; index < out_end; ++index) { int input_backprop_index = out_arg_max_flat(index); FastBoundsCheck(input_backprop_index - in_start, in_end - in_start); input_backprop_flat(input_backprop_index) += out_backprop_flat(index); } ``` Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. ### Patches We have patched the issue in GitHub commit [a74768f8e4efbda4def9f16ee7e13cf3922ac5f7](https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

CVSS 3.1
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Severity from
GitHub (reviewed advisory)
Weakness
CWE-119, CWE-787
Also known as
BIT-tensorflow-2021-29579, CVE-2021-29579, PYSEC-2021-216, PYSEC-2021-507, PYSEC-2021-705

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