---
id: CVE-2021-29576
aliases:
  - GHSA-7cqx-92hp-x6wh
  - BIT-tensorflow-2021-29576
  - PYSEC-2021-213
  - PYSEC-2021-504
  - PYSEC-2021-702
title: Heap buffer overflow in `MaxPool3DGradGrad`
summary: Heap buffer overflow in `MaxPool3DGradGrad`
severity: low
cvss: 2.5
cvssVector: 'CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - tensorflow < 2.1.4
  - 'tensorflow >= 2.2.0, < 2.2.3'
  - 'tensorflow >= 2.3.0, < 2.3.3'
  - 'tensorflow >= 2.4.0, < 2.4.2'
  - tensorflow-cpu < 2.1.4
  - 'tensorflow-cpu >= 2.2.0, < 2.2.3'
  - 'tensorflow-cpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-cpu >= 2.4.0, < 2.4.2'
  - tensorflow-gpu < 2.1.4
  - 'tensorflow-gpu >= 2.2.0, < 2.2.3'
  - 'tensorflow-gpu >= 2.3.0, < 2.3.3'
  - 'tensorflow-gpu >= 2.4.0, < 2.4.2'
patched:
  - tensorflow 2.1.4
  - tensorflow 2.2.3
  - tensorflow 2.3.3
  - tensorflow 2.4.2
  - tensorflow-cpu 2.1.4
  - tensorflow-cpu 2.2.3
  - tensorflow-cpu 2.3.3
  - tensorflow-cpu 2.4.2
  - tensorflow-gpu 2.1.4
  - tensorflow-gpu 2.2.3
  - tensorflow-gpu 2.3.3
  - tensorflow-gpu 2.4.2
published: '2021-05-21'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-7cqx-92hp-x6wh'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29576'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-504.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-702.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-213.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00211
epssPercentile: 0.10068
ingestedAt: '2026-07-08T18:25:46.975Z'
---

## Overview

### Impact
The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow: 

```python
import tensorflow as tf

values = [0.01] * 11
orig_input = tf.constant(values, shape=[11, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"

tf.raw_ops.MaxPool3DGradGrad(
    orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
    strides=strides, padding=padding)
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully:

```cc
Pool3dParameters params{context,  ksize_,       stride_,
                        padding_, data_format_, tensor_in.shape()};
```

Since [the constructor](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values.

### Patches
We have patched the issue in GitHub commit [63c6a29d0f2d692b247f7bf81f8732d6442fad09](https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09).

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.

## Affected packages

- `tensorflow < 2.1.4`
- `tensorflow >= 2.2.0, < 2.2.3`
- `tensorflow >= 2.3.0, < 2.3.3`
- `tensorflow >= 2.4.0, < 2.4.2`
- `tensorflow-cpu < 2.1.4`
- `tensorflow-cpu >= 2.2.0, < 2.2.3`
- `tensorflow-cpu >= 2.3.0, < 2.3.3`
- `tensorflow-cpu >= 2.4.0, < 2.4.2`
- `tensorflow-gpu < 2.1.4`
- `tensorflow-gpu >= 2.2.0, < 2.2.3`
- `tensorflow-gpu >= 2.3.0, < 2.3.3`
- `tensorflow-gpu >= 2.4.0, < 2.4.2`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.1.4`
- `tensorflow 2.2.3`
- `tensorflow 2.3.3`
- `tensorflow 2.4.2`
- `tensorflow-cpu 2.1.4`
- `tensorflow-cpu 2.2.3`
- `tensorflow-cpu 2.3.3`
- `tensorflow-cpu 2.4.2`
- `tensorflow-gpu 2.1.4`
- `tensorflow-gpu 2.2.3`
- `tensorflow-gpu 2.3.3`
- `tensorflow-gpu 2.4.2`