---
id: CVE-2022-23573
aliases:
  - GHSA-q85f-69q7-55h2
  - BIT-tensorflow-2022-23573
  - PYSEC-2022-137
  - PYSEC-2022-82
  - PYSEC-2026-3229
title: Uninitialized variable access in Tensorflow
summary: Uninitialized variable access in Tensorflow
severity: high
cvss: 7.6
cvssVector: 'CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/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: '2026-07-13'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-q85f-69q7-55h2'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2022-23573'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-82.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-137.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143
tags:
  - osv
  - pip
epss: 0.00761
epssPercentile: 0.53868
ingestedAt: '2026-07-13T18:58:01.929Z'
---

## Overview

### Impact
The [implementation of `AssignOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143) can result in copying unitialized data to a new tensor. This later results in undefined behavior.

The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized.
  
### Patches
We have patched the issue in GitHub commit [ef1d027be116f25e25bb94a60da491c2cf55bd0b](https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b).
    
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.


## 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`