CVE-2020-15208: TensorFlow Lite - Out-of-Bounds Read/Write Vulnerability
Summary
CVE-2020-15208: Data corruption in tensorflow-lite. Affects tensorflow across 5 version ranges.
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
Fixed in 5 releases; the minimum fixed version depends 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-2020-15208 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 machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying machine learning models. TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. It enables efficient execution of machine learning models on resource-constrained platforms.
Vulnerability Detail
The vulnerability, identified as CVE-2020-15208, exists in versions of TensorFlow Lite prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. When determining the common dimension size of two tensors, TensorFlow Lite uses a DCHECK statement, which is a no-op outside of debug compilation modes. This means that the function always returns the dimension of the first tensor, regardless of the actual sizes of the tensors. Malicious attackers can exploit this behavior by crafting cases where the dimension of the first tensor is larger than that of the second tensor. As a result, the interpreter will incorrectly assume that there is enough data in both tensors, leading to out-of-bounds reads and writes. This vulnerability can be exploited to execute arbitrary code or cause a denial of service.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 1.15.41.15.4 -
>= 2.0.0, < 2.0.32.0.3 -
>= 2.1.0, < 2.1.22.1.2 -
>= 2.2.0, < 2.2.12.2.1 -
>= 2.3.0, < 2.3.12.3.1
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 crucial to update to the patched versions of TensorFlow Lite to mitigate the risk.
Conclusion
The out-of-bounds read/write vulnerability in TensorFlow Lite can lead to arbitrary code execution or denial of service. It is crucial to update to the patched versions of TensorFlow Lite (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate the risk. For further support on vulnerability remediation, contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. It enables efficient execution of machine learning models on resource-constrained platforms.
This vulnerability can be exploited by malicious attackers to execute arbitrary code or cause a denial of service.
Versions of TensorFlow Lite prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are affected by this vulnerability.
No, there is no known workaround for this vulnerability. It is recommended to update to the patched versions of TensorFlow Lite.
To update TensorFlow Lite, follow the official upgrade instructions provided by the TensorFlow project.
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
