CVE-2022-29208: TensorFlow - Incomplete Validation in tf.raw_ops.EditDistance
Summary
CVE-2022-29208: Segfault and Out-of-bounds Write write due to incomplete validation in TensorFlow. Affects tensorflow across 3 version ranges.
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
Fixed in 2.6.4, 2.7.2 or 2.8.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
- tensorflow · PyPI
The same CVE-2022-29208 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 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 domains, including research, industry, and academia. It offers a flexible and efficient framework for building and training machine learning models, enabling developers to leverage the power of deep learning algorithms. However, prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow is vulnerable to an incomplete validation issue in the implementation of tf.raw_ops.EditDistance.
Vulnerability Detail
The vulnerability arises from incomplete validation in the tf.raw_ops.EditDistance implementation. This allows users to pass negative values, leading to a segmentation fault-based denial of service. The issue stems from the lack of proper bounds checking for array write operations. While the existing validation checks against the upper bound of the array, it fails to prevent writing before the array. By manipulating the input to generate negative values for loc, an attacker can exploit this vulnerability. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 have been patched to address this issue.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.6.42.6.4 -
>= 2.7.0, < 2.7.22.7.2 -
>= 2.8.0, < 2.8.12.8.1
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Workaround
As a workaround, users can apply input validation checks in their own code to prevent negative values from being passed to tf.raw_ops.EditDistance. By validating the input and ensuring that only valid values are used, the vulnerability can be mitigated. Here is an example of how to perform input validation in Python:
loc = ... # User-provided input
if loc < 0:
raise ValueError('Invalid input: loc must be a non-negative value')
# Continue with the rest of the code
Conclusion
The incomplete validation vulnerability in TensorFlow's tf.raw_ops.EditDistance has the potential to cause a denial of service. It is crucial to update TensorFlow to the patched versions (2.9.0, 2.8.1, 2.7.2, or 2.6.4) to mitigate this issue. Additionally, applying input validation checks in user code can serve as a temporary workaround. For further support on vulnerability remediation, contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models.
The vulnerability allows an attacker to cause a denial of service by passing negative values to tf.raw_ops.EditDistance, resulting in a segmentation fault. This can disrupt the normal operation of TensorFlow and potentially impact the availability of machine learning applications relying on TensorFlow.
Versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 are affected by this vulnerability. It is recommended to update to one of the patched versions to mitigate the issue.
To fix this vulnerability, update TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain the necessary patches. Follow the official upgrade instructions provided by the TensorFlow team.
Yes, users can apply input validation checks in their own code to prevent negative values from being passed to tf.raw_ops.EditDistance. By validating the input and ensuring that only valid values are used, the vulnerability can be mitigated.
References
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