---
id: CVE-2021-29544
aliases:
  - GHSA-6g85-3hm8-83f9
  - BIT-tensorflow-2021-29544
  - PYSEC-2021-181
  - PYSEC-2021-472
  - PYSEC-2021-670
title: CHECK-fail in `QuantizeAndDequantizeV4Grad`
summary: CHECK-fail in `QuantizeAndDequantizeV4Grad`
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 >= 2.4.0, < 2.4.2'
  - 'tensorflow-cpu >= 2.4.0, < 2.4.2'
  - 'tensorflow-gpu >= 2.4.0, < 2.4.2'
patched:
  - tensorflow 2.4.2
  - tensorflow-cpu 2.4.2
  - tensorflow-gpu 2.4.2
published: '2021-05-21'
updated: '2026-07-08'
source: OSV
sourceUrl: 'https://osv.dev/vulnerability/GHSA-6g85-3hm8-83f9'
references:
  - url: >-
      https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9
  - url: 'https://nvd.nist.gov/vuln/detail/CVE-2021-29544'
  - url: >-
      https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-472.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-670.yaml
  - url: >-
      https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-181.yaml
  - url: 'https://github.com/tensorflow/tensorflow'
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163
  - url: >-
      https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306
tags:
  - osv
  - pip
epss: 0.0031
epssPercentile: 0.2116
ingestedAt: '2026-07-08T18:25:46.461Z'
---

## Overview

### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`:

```python
import tensorflow as tf

gradient_tensor = tf.constant([0.0], shape=[1])
input_tensor = tf.constant([0.0], shape=[1])
input_min = tf.constant([[0.0]], shape=[1, 1])
input_max = tf.constant([[0.0]], shape=[1, 1])

tf.raw_ops.QuantizeAndDequantizeV4Grad(
  gradients=gradient_tensor, input=input_tensor,
  input_min=input_min, input_max=input_max, axis=0)
```                     
                        
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to [`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306):

```cc 
template <typename Device, typename T>
struct QuantizeAndDequantizePerChannelGradientImpl {
  static void Compute(const Device& d,
                      typename TTypes<T, 3>::ConstTensor gradient,
                      typename TTypes<T, 3>::ConstTensor input,
                      const Tensor* input_min_tensor,
                      const Tensor* input_max_tensor,
                      typename TTypes<T, 3>::Tensor input_backprop,
                      typename TTypes<T>::Flat input_min_backprop,
                      typename TTypes<T>::Flat input_max_backprop) {
    ...
    auto input_min = input_min_tensor->vec<T>();
    auto input_max = input_max_tensor->vec<T>();
    ...
}
```

However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise.

### Patches
We have patched the issue in GitHub commit [20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

### 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 Yakun Zhang and Ying Wang of Baidu X-Team.

## Affected packages

- `tensorflow >= 2.4.0, < 2.4.2`
- `tensorflow-cpu >= 2.4.0, < 2.4.2`
- `tensorflow-gpu >= 2.4.0, < 2.4.2`

## Remediation

Upgrade to a patched release:

- `tensorflow 2.4.2`
- `tensorflow-cpu 2.4.2`
- `tensorflow-gpu 2.4.2`