CVE-2021-29617: Denial of Service in TensorFlow

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various industries and research domains for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service (DoS) attack.

Vulnerability Detail

The vulnerability (CVE-2021-29617) in TensorFlow is caused by a CHECK-fail in the tf.strings.substr function when provided with invalid arguments. An attacker can exploit this vulnerability to cause a DoS condition in applications that use TensorFlow. The impact of this vulnerability can range from temporary unavailability of the affected application to a complete system crash. The vulnerability affects multiple versions of TensorFlow, including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and earlier versions.

pip install --upgrade "tensorflow-gpu==2.12.0"

Workaround

As a temporary workaround, developers can validate the arguments passed to the tf.strings.substr function to ensure they are within the expected range. By performing proper input validation, the risk of triggering the vulnerability can be reduced. Here is an example of how to validate the arguments:

import tensorflow as tf

def safe_substr(input_string, pos, length):
    if pos < 0 or length < 0:
        raise ValueError('Invalid arguments: pos and length must be non-negative')
    if pos + length > len(input_string):
        raise ValueError('Invalid arguments: pos + length exceeds the length of the input string')
    return tf.strings.substr(input_string, pos, length)

Conclusion

The vulnerability (CVE-2021-29617) in TensorFlow can lead to a denial of service (DoS) condition in applications that use TensorFlow. It is recommended to update to the latest patched version of TensorFlow or apply the provided patches for earlier versions to mitigate the vulnerability. For further support on vulnerability remediation, you can contact DevNack.com.

See every vulnerability recorded for tensorflow-gpu →

FAQ