CVE-2022-41894

CVE-2022-41894 is a high-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 7.1. It is not currently listed as actively exploited by CISA, and its EPSS exploit-prediction score is low. The underlying weakness is classified as CWE-120.

Key facts

Description

TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Frequently asked questions

What is CVE-2022-41894?
TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
How severe is CVE-2022-41894?
CVE-2022-41894 has a CVSS 3.x base score of 7.1, rated high severity. It is exploitable over network with high attack complexity, requires low privileges and user interaction. Impact on confidentiality is high, integrity high, and availability high.
Is CVE-2022-41894 being actively exploited?
It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (41st percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2022-41894?
CVE-2022-41894 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2022-41894?
Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround. Given its high severity, prioritise patching exposed systems.
When was CVE-2022-41894 published?
CVE-2022-41894 was published on 2022-11-18 and last updated on 2026-06-17.

References

Affected products (1)

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