---
id: CVE-2022-21730
aliases:
  - GHSA-vjg4-v33c-ggc4
  - BIT-tensorflow-2022-21730
  - PYSEC-2022-109
  - PYSEC-2022-54
  - PYSEC-2026-3247
title: Out of bounds read in Tensorflow
summary: Out of bounds read in Tensorflow
severity: high
cvss: 8.1
cvssVector: 'CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - tensorflow < 2.5.3
  - 'tensorflow >= 2.6.0, < 2.6.3'
  - 'tensorflow >= 2.7.0, < 2.7.1'
  - tensorflow-cpu < 2.5.3
  - 'tensorflow-cpu >= 2.6.0, < 2.6.3'
  - 'tensorflow-cpu >= 2.7.0, < 2.7.1'
  - tensorflow-gpu < 2.5.3
  - 'tensorflow-gpu >= 2.6.0, < 2.6.3'
  - 'tensorflow-gpu >= 2.7.0, < 2.7.1'
patched:
  - tensorflow 2.5.3
  - tensorflow 2.6.3
  - tensorflow 2.7.1
  - tensorflow-cpu 2.5.3
  - tensorflow-cpu 2.6.3
  - tensorflow-cpu 2.7.1
  - tensorflow-gpu 2.5.3
  - tensorflow-gpu 2.6.3
  - tensorflow-gpu 2.7.1
published: '2022-02-09'
updated: '2026-09-10'
sourceUpdated: '2026-09-10T03:49:13.277278061Z'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-vjg4-v33c-ggc4'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2022-21730'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-54.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-109.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360
tags:
  - osv
  - pip
epss: 0.00822
epssPercentile: 0.55813
ingestedAt: '2026-07-13T18:58:03.609Z'
---

## Overview

### Impact 
The [implementation of `FractionalAvgPoolGrad`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360) does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap:

```python
import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.FractionalAvgPoolGrad(
    orig_input_tensor_shape=[2,2,2,2],
    out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]],
    row_pooling_sequence=[-10,1,2,3],
    col_pooling_sequence=[1,2,3,4],
    overlapping=True)
  return y
    
test()
```

### Patches
We have patched the issue in GitHub commit [002408c3696b173863228223d535f9de72a101a9](https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9).

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.

## Affected packages

- `tensorflow < 2.5.3`
- `tensorflow >= 2.6.0, < 2.6.3`
- `tensorflow >= 2.7.0, < 2.7.1`
- `tensorflow-cpu < 2.5.3`
- `tensorflow-cpu >= 2.6.0, < 2.6.3`
- `tensorflow-cpu >= 2.7.0, < 2.7.1`
- `tensorflow-gpu < 2.5.3`
- `tensorflow-gpu >= 2.6.0, < 2.6.3`
- `tensorflow-gpu >= 2.7.0, < 2.7.1`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.5.3`
- `tensorflow 2.6.3`
- `tensorflow 2.7.1`
- `tensorflow-cpu 2.5.3`
- `tensorflow-cpu 2.6.3`
- `tensorflow-cpu 2.7.1`
- `tensorflow-gpu 2.5.3`
- `tensorflow-gpu 2.6.3`
- `tensorflow-gpu 2.7.1`