---
id: CVE-2021-41225
aliases:
  - GHSA-7r94-xv9v-63jw
  - BIT-tensorflow-2021-41225
  - PYSEC-2021-417
  - PYSEC-2021-634
  - PYSEC-2021-832
title: A use of uninitialized value vulnerability in Tensorflow
summary: A use of uninitialized value vulnerability in Tensorflow
severity: medium
cvss: 5.5
cvssVector: 'CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H'
vendor: tensorflow
product: tensorflow
ecosystem: pip
affected:
  - 'tensorflow >= 2.6.0, < 2.6.1'
  - 'tensorflow >= 2.5.0, < 2.5.2'
  - tensorflow < 2.4.4
  - 'tensorflow-cpu >= 2.6.0, < 2.6.1'
  - 'tensorflow-cpu >= 2.5.0, < 2.5.2'
  - tensorflow-cpu < 2.4.4
  - 'tensorflow-gpu >= 2.6.0, < 2.6.1'
  - 'tensorflow-gpu >= 2.5.0, < 2.5.2'
  - tensorflow-gpu < 2.4.4
patched:
  - tensorflow 2.6.1
  - tensorflow 2.5.2
  - tensorflow 2.4.4
  - tensorflow-cpu 2.6.1
  - tensorflow-cpu 2.5.2
  - tensorflow-cpu 2.4.4
  - tensorflow-gpu 2.6.1
  - tensorflow-gpu 2.5.2
  - tensorflow-gpu 2.4.4
published: '2021-11-10'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-7r94-xv9v-63jw'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-41225'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-634.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-832.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-417.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00196
epssPercentile: 0.09253
ingestedAt: '2026-07-08T18:25:47.214Z'
---

## Overview

### Impact
TensorFlow's Grappler optimizer has a [use of unitialized variable](https://github.com/tensorflow/tensorflow/blob/3457a2b122e50b4d44ceaaed5a663d635e5c22df/tensorflow/core/grappler/optimizers/auto_parallel.cc#L155-L164):

```cc
  const NodeDef* dequeue_node;
  for (const auto& train_node : train_nodes) {
    if (IsDequeueOp(*train_node)) {
      dequeue_node = train_node;
      break;
    }
  }

  if (dequeue_node) {
    ...
  }
```

If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized.

### Patches
We have patched the issue in GitHub commit [68867bf01239d9e1048f98cbad185bf4761bedd3](https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3).

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 Qian Feng from Baidu Security Team.


## Affected packages

- `tensorflow >= 2.6.0, < 2.6.1`
- `tensorflow >= 2.5.0, < 2.5.2`
- `tensorflow < 2.4.4`
- `tensorflow-cpu >= 2.6.0, < 2.6.1`
- `tensorflow-cpu >= 2.5.0, < 2.5.2`
- `tensorflow-cpu < 2.4.4`
- `tensorflow-gpu >= 2.6.0, < 2.6.1`
- `tensorflow-gpu >= 2.5.0, < 2.5.2`
- `tensorflow-gpu < 2.4.4`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.6.1`
- `tensorflow 2.5.2`
- `tensorflow 2.4.4`
- `tensorflow-cpu 2.6.1`
- `tensorflow-cpu 2.5.2`
- `tensorflow-cpu 2.4.4`
- `tensorflow-gpu 2.6.1`
- `tensorflow-gpu 2.5.2`
- `tensorflow-gpu 2.4.4`