CVE-2022-29192: TensorFlow - Denial of Service Vulnerability
Summary
CVE-2022-29192: Missing validation crashes QuantizeAndDequantizeV4Grad 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-2022-29192 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
Check if this vulnerability affects your application
Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.
No signup required · Results in about 60 seconds
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 industries and research fields for tasks such as image recognition, natural language processing, and recommendation systems. However, prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, TensorFlow is vulnerable to a denial of service attack due to a flaw in the implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad function. This vulnerability can be exploited by an attacker to cause a CHECK-failure, resulting in a denial of service condition.
Vulnerability Detail
The vulnerability in TensorFlow arises from the incomplete validation of input arguments in the tf.raw_ops.QuantizeAndDequantizeV4Grad function. By providing specially crafted input, an attacker can trigger a CHECK-failure, leading to a denial of service attack. This vulnerability affects versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4. However, TensorFlow 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
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no known workaround for this vulnerability. It is recommended to update to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
In conclusion, TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 are vulnerable to a denial of service attack due to incomplete input validation in the tf.raw_ops.QuantizeAndDequantizeV4Grad function. It is crucial to update to the patched versions of TensorFlow to mitigate this vulnerability. Regularly updating software dependencies is essential for maintaining the security of machine learning applications. 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, widely used for developing and deploying machine learning models.
This vulnerability can be exploited by an attacker to cause a denial of service condition, potentially disrupting the availability of TensorFlow-based applications.
Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 of TensorFlow are affected by this vulnerability.
Yes, TensorFlow versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this vulnerability.
No, there is no known workaround for this vulnerability. It is recommended to update to the patched versions of TensorFlow.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
