CVE-2022-21727: TensorFlow Integer Overflow Vulnerability in Dequantize Operation

Background

TensorFlow is an open-source machine learning library widely used for building and training neural networks. It provides a flexible ecosystem for developing machine learning models, including tools for deep learning, data processing, and deployment. However, TensorFlow has a vulnerability in its shape inference implementation for the Dequantize operation, which can lead to integer overflow issues, potentially affecting the stability and security of applications using this library.

Vulnerability Detail

The vulnerability arises from the lack of upper bound checks on the 'axis' argument in the Dequantize operation. When an attacker provides an excessively large value for 'axis', it can trigger an integer overflow during the computation of 'axis + 1'. This can lead to unexpected behavior, crashes, or even arbitrary code execution, compromising the integrity and availability of the application. The vulnerability is particularly concerning because it can be exploited by manipulating input values, making it a significant risk for applications relying on TensorFlow for machine learning tasks.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a temporary workaround, avoid using the Dequantize operation with an 'axis' value that could lead to overflow. Ensure that the 'axis' argument is set to a valid range, specifically between -1 and the number of dimensions of the input tensor. Here is an example of how to safely use the Dequantize operation:

import tensorflow as tf

input = tf.constant([1, 1], dtype=tf.qint32)

@tf.function

def safe_dequantize():

y = tf.raw_ops.Dequantize(
    input=input,
    min_range=[1.0],
    max_range=[10.0],
    mode='MIN_COMBINED',
    narrow_range=False,
    axis=0,  # Ensure this is within the valid range
    dtype=tf.bfloat16)
return y

safe_dequantize()

Conclusion

In summary, CVE-2022-21727 is a critical vulnerability in TensorFlow that can lead to integer overflow issues in the Dequantize operation. Users are advised to update to the latest patched versions to mitigate risks. For those unable to update immediately, implementing safe coding practices can help reduce exposure to this vulnerability.

See every vulnerability recorded for tensorflow →

FAQ