PyPI: tensorflow-macos

CVE-2021-37673

Safety vulnerability ID: SFTY-20210812-53624

Safety legacy ID: pyup.io-58484

Tensorflow-macos version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37673: In affected versions, an attacker can trigger a denial of service via a "CHECK"-fail in "tf.raw_ops.MapStage". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the "key" input is a valid non-empty tensor. The Tensorflow team has patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. https://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d

Created at: Jul 5, 2026Updated at: Jul 5, 2026

Overview

`CHECK`-fail in `MapStage`

Advisory

Tensorflow-macos version 2.3.4, 2.4.3, 2.5.1 and 2.6.0 include a fix for CVE-2021-37673: In affected versions, an attacker can trigger a denial of service via a "CHECK"-fail in "tf.raw_ops.MapStage". The implementation (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the "key" input is a valid non-empty tensor. The Tensorflow team has patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. https://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d

Affected Package

Affecting tensorflow-macos package, versions>=2.6.0rc0,<2.6.0>=2.5.0rc0,<2.5.1>=2.4.0rc0,<2.4.3>=2.3.0rc0,<2.3.4

Also affects

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How to Fix

Upgradetensorflow-macosto2.6.02.6.02.6.02.6.0or higher.

Mitigation and Workarounds

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Vulnerable Functions

Functions linked to known vulnerabilities.

Vulnerable function data is available for Enterprise customers

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Safety

Verified by Safety

Our Cybersecurity Intelligence Team reviewed this vulnerability. We combine public data with our own research to find issues not yet reported to public sources.

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