CVE-2021-29525: TensorFlow - Division by 0 in Conv2DBackpropInput

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem for building and deploying machine learning models. TensorFlow is widely used in various industries and research domains due to its flexibility and scalability. However, a vulnerability has been discovered in TensorFlow that can lead to a division by 0 in the tf.raw_ops.Conv2DBackpropInput function. This vulnerability can be exploited by an attacker to cause a denial of service or potentially execute arbitrary code.

Vulnerability Detail

The vulnerability arises from a division operation in the implementation of tf.raw_ops.Conv2DBackpropInput. The division is performed using a value controlled by the caller, allowing an attacker to provide a malicious input that triggers a division by 0. This can lead to a crash or undefined behavior in the affected TensorFlow version. The vulnerability has been assigned the CVE identifier CVE-2021-29525 and has a CVSSv3 score of 9.8 (Critical). It affects multiple versions of TensorFlow, including 2.1.4, 2.2.3, 2.3.3, and 2.4.2.

Workaround

As a temporary workaround, it is advised to sanitize user inputs and validate them before passing them to the vulnerable function. By ensuring that the input values are within a valid range, the risk of triggering a division by 0 can be mitigated. Here is an example of how to sanitize inputs in Python:

def sanitize_input(value):
    if value <= 0:
        value = 1
    return value

# Usage:
input_value = sanitize_input(user_input)
result = tf.raw_ops.Conv2DBackpropInput(input_value, ...)

Conclusion

In conclusion, a division by 0 vulnerability has been discovered in TensorFlow versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2. This vulnerability can be exploited by an attacker to cause a denial of service or potentially execute arbitrary code. It is recommended to upgrade to TensorFlow 2.5.0 or apply the fix manually to mitigate the issue. Sanitizing user inputs can serve as a temporary workaround. For further support on vulnerability remediation, please contact DevNack.com.

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