---
id: CVE-2021-29588
aliases:
  - GHSA-vfr4-x8j2-3rf9
  - BIT-tensorflow-2021-29588
  - PYSEC-2021-225
  - PYSEC-2021-516
  - PYSEC-2021-714
title: Division by zero in TFLite's implementation of `TransposeConv`
summary: Division by zero in TFLite's implementation of `TransposeConv`
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-vfr4-x8j2-3rf9'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29588'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-516.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-714.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-225.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222
tags:
  - osv
  - pip
epss: 0.00201
epssPercentile: 0.08865
ingestedAt: '2026-07-08T18:25:53.317Z'
---

## Overview

### Impact
The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222):

```cc 
int height_col = (height + pad_t + pad_b - filter_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - filter_w) / stride_w + 1;
```

An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments.

### Patches
We have patched the issue in GitHub commit [801c1c6be5324219689c98e1bd3e0ca365ee834d](https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d).

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 members of the Aivul Team from Qihoo 360.

## 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`