CVE-2021-29552: TensorFlow UnsortedSegmentJoin CHECK-failure

Background

TensorFlow is an open-source machine learning framework that provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. It is widely used in various domains for tasks such as image recognition, natural language processing, and recommendation systems. TensorFlow includes a feature called UnsortedSegmentJoin, which allows users to join multiple segments of a tensor based on segment IDs. This feature is commonly used in data processing and analysis pipelines.

Vulnerability Detail

The vulnerability, identified as CVE-2021-29552, affects the UnsortedSegmentJoin function in TensorFlow. An attacker can exploit this vulnerability by controlling the values of the num_segments tensor argument. The implementation of UnsortedSegmentJoin assumes that the num_segments tensor is a valid scalar, but if the tensor is empty, a CHECK condition is invalidated, leading to process termination. This vulnerability can be leveraged to cause a denial of service (DoS) attack. The impact of this vulnerability is relatively low, with a CVSS score of 2.5.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can manually validate the num_segments tensor before passing it to the UnsortedSegmentJoin function. This can be done by checking the number of elements in the tensor and ensuring it is exactly one. Here is an example of how to implement this workaround in Python:

import tensorflow as tf

inputs = tf.constant([], dtype=tf.string)
segment_ids = tf.constant([], dtype=tf.int32)
num_segments = tf.constant([], dtype=tf.int32)
separator = ''

if tf.size(num_segments) != 1:
    raise ValueError('num_segments tensor must contain exactly one element')

# Proceed with UnsortedSegmentJoin
# ...

By manually validating the num_segments tensor, users can prevent the CHECK failure and mitigate the vulnerability.

Conclusion

The vulnerability in TensorFlow's UnsortedSegmentJoin function has been addressed in the latest patched versions. Users are advised to update their installations to mitigate the risk of a denial of service attack. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ