CVE-2021-37636: TensorFlow - Division by 0 Error in SparseDenseCwiseDiv
Summary
CVE-2021-37636: Floating point exception in SparseDenseCwiseDiv in TensorFlow. Affects tensorflow across 3 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
>= 2.5.0, < 2.5.1
Fixed in 2.3.4, 2.4.3 or 2.5.1, depending 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-2021-37636 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 ecosystem for developing and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. However, it is susceptible to a division by 0 error in the implementation of tf.raw_ops.SparseDenseCwiseDiv.
Vulnerability Detail
The vulnerability arises from a flaw in the implementation of tf.raw_ops.SparseDenseCwiseDiv. The code fails to handle the division by 0 case separately, leading to a potential division by 0 error. This vulnerability can be exploited by an attacker to cause a denial of service (DoS) condition or potentially execute arbitrary code. The issue has been addressed in the GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9 and will be included in TensorFlow 2.6.0. The fix will also be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
>= 2.3.0, < 2.3.42.3.4 -
>= 2.4.0, < 2.4.32.4.3 -
>= 2.5.0, < 2.5.12.5.1
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Workaround
As a workaround, users can modify the affected code to handle the division by 0 case separately. Here is an example of how the code can be modified:
# Original code
result = tf.raw_ops.SparseDenseCwiseDiv(sp_input, dense_input)
# Modified code
if tf.reduce_any(tf.equal(dense_input, 0)):
# Handle division by 0 case
result = tf.zeros_like(sp_input)
else:
result = tf.raw_ops.SparseDenseCwiseDiv(sp_input, dense_input)
By explicitly checking if dense_input contains any zeros before performing the division, the division by 0 error can be avoided.
Conclusion
In conclusion, TensorFlow is vulnerable to a division by 0 error in the implementation of tf.raw_ops.SparseDenseCwiseDiv. This vulnerability can be mitigated by updating to the patched versions or applying the respective patches. 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 ecosystem for developing and deploying machine learning models.
This vulnerability can lead to a division by 0 error, which may result in a denial of service (DoS) condition or potential execution of arbitrary code.
To fix this vulnerability, it is recommended to update TensorFlow to version 2.6.0 or apply the respective patches for TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.
Yes, a workaround is available. Users can modify the affected code to handle the division by 0 case separately.
For more information about this vulnerability, you can refer to the GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9 and the associated security advisories.
References
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