CVE-2021-29550: TensorFlow - Division by Zero Denial of Service
Summary
CVE-2021-29550: Division by 0 in FractionalAvgPool. 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-2021-29550 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
Habilume
Habit tracker with a companion that grows with you
Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent.
Background
TensorFlow is an end-to-end 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, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.
Vulnerability Detail
The vulnerability (CVE-2021-29550) exists in the tf.raw_ops.FractionalAvgPool function of TensorFlow. An attacker can exploit this vulnerability by providing user-controlled values for input_size[i] and pooling_ratio_[i], which are used to compute divisor quantities. If the value in input_size[i] is smaller than pooling_ratio_[i], the floor operation results in output_size[i] being 0. This leads to a division by zero error and a runtime crash. The impact of this vulnerability is a denial of service, as the TensorFlow runtime crashes and becomes unresponsive.
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
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 apply the official fix or upgrade to a fixed version of TensorFlow.
Conclusion
The vulnerability in TensorFlow can lead to a denial of service attack by causing a runtime division by zero error. It is crucial to update to the fixed versions of TensorFlow or apply the official fix once it is released. 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 tools and resources for developing and deploying machine learning models.
The vulnerability can be exploited to cause a denial of service by crashing the TensorFlow runtime.
The vulnerability will be fixed in TensorFlow 2.5.0. It is recommended to upgrade to this version or later once it is released.
Yes, older versions including TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, and TensorFlow 2.1.4 are also affected. The fix will be backported to these versions.
There is no known workaround. It is recommended to apply the official fix or upgrade to a fixed version of TensorFlow.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
Habilume
Habit tracker with a companion that grows with you
Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent.
