CVE-2021-29529: TensorFlow Heap Buffer Overflow in tf.raw_ops.QuantizedResizeBilinear
Summary
CVE-2021-29529: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu across 5 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
< f851613f8f0fb0c838d160ced13c134f778e3ce7
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-29529 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 of tools, libraries, and resources for building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. The vulnerability described here affects the 'tensorflow-cpu' package, which is a Python package for TensorFlow that runs on CPUs. This package allows users to leverage TensorFlow's capabilities without requiring GPU support. The vulnerability specifically impacts the 'tf.raw_ops.QuantizedResizeBilinear' function, which is used for resizing images using bilinear interpolation.
Vulnerability Detail
The vulnerability arises from a heap buffer overflow in the 'tf.raw_ops.QuantizedResizeBilinear' function of TensorFlow. The issue occurs due to an off-by-one error in accessing image elements caused by float rounding during the computation of interpolation bounds. The implementation computes two integers representing upper and lower bounds for interpolation by ceiling and flooring a floating-point value. In certain cases, the upper bound can be smaller than the lower bound, resulting in the lower bound pointing outside of the image. This leads to a heap buffer overflow in the subsequent interpolation code. An attacker can exploit this vulnerability by manipulating input values to trigger the off-by-one error. The impact of this vulnerability can range from denial of service to potential remote code execution.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-cpu==2.21.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
< 2.1.42.1.4pip install --upgrade "tensorflow-cpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-cpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-cpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-cpu==2.4.2"
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Workaround
As a workaround, users can consider avoiding the usage of the 'tf.raw_ops.QuantizedResizeBilinear' function until the package is updated to a fixed version. Alternatively, users can switch to other image resizing functions available in TensorFlow that are not affected by this vulnerability.
Conclusion
The vulnerability in TensorFlow's 'tf.raw_ops.QuantizedResizeBilinear' function poses a potential security risk, allowing attackers to trigger a heap buffer overflow. Users are strongly advised to upgrade to TensorFlow 2.5.0 or the backported fixed versions (2.4.2, 2.3.3, 2.2.3, or 2.1.4) to mitigate this issue. Alternatively, users can consider workarounds such as avoiding the usage of the affected function or switching to other unaffected image resizing functions. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive set of tools and resources for building and deploying machine learning models.
'tensorflow-cpu' is a Python package for TensorFlow that allows users to utilize TensorFlow's capabilities on CPUs without requiring GPU support.
The 'tf.raw_ops.QuantizedResizeBilinear' function is used for resizing images using bilinear interpolation in TensorFlow.
An attacker can exploit this vulnerability by manipulating input values to trigger the off-by-one error in the 'tf.raw_ops.QuantizedResizeBilinear' function, leading to a heap buffer overflow.
The impact of this vulnerability can range from denial of service to potential remote code execution, depending on how it is exploited.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
