How severe is CVE-2022-35974?

CVE-2022-35974 has a CVSS 3.x base score of 5.9, rated medium severity. It is exploitable over network with high attack complexity, requires no privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.

Is CVE-2022-35974 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 0% (34th percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2022-35974?

CVE-2022-35974 primarily affects Google Tensorflow. In total, 5 product configurations (CPEs) are listed as vulnerable; see the affected-products list for the exact versions.

How do I fix CVE-2022-35974?

Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.

When was CVE-2022-35974 published?

CVE-2022-35974 was published on 2022-09-16 and last updated on 2026-06-17.

CVE-2022-35974

CVE-2022-35974 is a medium-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 5.9. 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-20.

Key facts

Severity: Medium (CVSS 3.x base score 5.9)
EPSS exploit prediction: 0% (34th percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-20
Affected product: Google Tensorflow
Published: 2022-09-16
Last modified: 2026-06-17

Description

TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.

Frequently asked questions

What is CVE-2022-35974?: TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.
How severe is CVE-2022-35974?: CVE-2022-35974 has a CVSS 3.x base score of 5.9, rated medium severity. It is exploitable over network with high attack complexity, requires no privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.
Is CVE-2022-35974 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 0% (34th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2022-35974?: CVE-2022-35974 primarily affects Google Tensorflow. In total, 5 product configurations (CPEs) are listed as vulnerable; see the affected-products list for the exact versions.
How do I fix CVE-2022-35974?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2022-35974 published?: CVE-2022-35974 was published on 2022-09-16 and last updated on 2026-06-17.

References

Affected products (5)

cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:2.10:rc0:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:2.10:rc1:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:2.10:rc2:*:*:*:*:*:*
cpe:2.3:a:google:tensorflow:2.10:rc3:*:*:*:*:*:*

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