CVE-2021-29527: TensorFlow - Division by 0 in QuantizedConv2D
Summary
CVE-2021-29527: Division by 0 in QuantizedConv2D. Affects tensorflow across 4 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-29527 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
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Background
TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive set of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various industries and has a large community of developers and users. However, a vulnerability has been discovered in the tf.raw_ops.QuantizedConv2D function, which can be exploited to trigger a division by 0.
Vulnerability Detail
The vulnerability exists in the implementation of the tf.raw_ops.QuantizedConv2D function in TensorFlow. The function performs a division by a quantity that is controlled by the caller, allowing an attacker to provide a value of 0 and trigger a division by 0 error. This can lead to unexpected behavior, crashes, or even potential security vulnerabilities in applications that rely on TensorFlow for machine learning tasks.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 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
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Workaround
As a temporary workaround, developers can manually modify the affected code to ensure that the division operation is not performed with a value of 0. This can be achieved by adding appropriate checks and handling cases where the divisor is 0. Here is an example of how the code can be modified:
# Original code
result = tf.raw_ops.QuantizedConv2D(inputs, filters, strides, padding)
# Modified code
if divisor != 0:
result = tf.raw_ops.QuantizedConv2D(inputs, filters, strides, padding)
else:
# Handle the case where divisor is 0
result = tf.zeros_like(inputs)
Conclusion
In conclusion, a vulnerability has been discovered in TensorFlow that allows an attacker to trigger a division by 0 error in the tf.raw_ops.QuantizedConv2D function. This vulnerability can have various impacts, including crashes and potential security vulnerabilities. It is recommended to update to the latest patched versions of TensorFlow or apply the provided workaround to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive set of tools and resources for developing and deploying machine learning models.
This vulnerability allows an attacker to trigger a division by 0 error in the tf.raw_ops.QuantizedConv2D function, potentially leading to crashes or security vulnerabilities in applications that use TensorFlow for machine learning tasks.
The vulnerability affects TensorFlow versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2. These versions are still within the supported range and will receive the fix.
To fix this vulnerability, it is recommended to update to the latest patched versions of TensorFlow, which include the fix. The fix will be included in TensorFlow 2.5.0, and for affected versions, it will be cherrypicked and included in TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
Yes, as a temporary workaround, developers can manually modify the affected code to ensure that the division operation is not performed with a value of 0. This involves adding appropriate checks and handling cases where the divisor is 0.
References
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