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CVE-2022-35973: Segfault in `QuantizedMatMul` in TensorFlow

• Severity: high (CVSS 7.5 v3.1)
• Exploited in the wild: not listed in CISA KEV
• EPSS: 0.5% probability of exploitation in the next 30 days
• Public exploit or detection template: none known
• Published: 2022-09-16 · updated 2026-06-17
• Weakness: CWE-20

Description

TensorFlow is an open source platform for machine learning. If `QuantizedMatMul` is given nonscalar input for: `min_a`, `max_a`, `min_b`, or `max_b` It gives a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Affected products

• tensorflow: from 2.7.0 before 2.7.2; from 2.8.0 before 2.8.1; from 2.9.0 before 2.9.1; 2.10-rc0, 2.10-rc1, 2.10-rc2, 2.10-rc3 — fixed in 2.7.2, 2.8.1, 2.9.1

How to fix

Upgrade to a fixed version: tensorflow 2.7.2, 2.8.1, 2.9.1. Check the vendor advisory for mitigations.

References

• https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v
• https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v

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