How severe is CVE-2020-26266?

CVE-2020-26266 has a CVSS 3.x base score of 4.4, rated medium severity. It is exploitable over local access with low attack complexity, requires low privileges and no user interaction. Impact on confidentiality is none, integrity low, and availability low.

Is CVE-2020-26266 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 0% (15th percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2020-26266?

CVE-2020-26266 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2020-26266?

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

When was CVE-2020-26266 published?

CVE-2020-26266 was published on 2020-12-10 and last updated on 2026-06-17.

CVE-2020-26266

CVE-2020-26266 is a medium-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 4.4. 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-908.

Key facts

Severity: Medium (CVSS 3.x base score 4.4)
CVSS v2: 4.6
EPSS exploit prediction: 0% (15th percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-908
Affected product: Google Tensorflow
Published: 2020-12-10
Last modified: 2026-06-17

Description

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Frequently asked questions

What is CVE-2020-26266?: In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
How severe is CVE-2020-26266?: CVE-2020-26266 has a CVSS 3.x base score of 4.4, rated medium severity. It is exploitable over local access with low attack complexity, requires low privileges and no user interaction. Impact on confidentiality is none, integrity low, and availability low.
Is CVE-2020-26266 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 0% (15th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-26266?: CVE-2020-26266 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-26266?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2020-26266 published?: CVE-2020-26266 was published on 2020-12-10 and last updated on 2026-06-17.

References

Affected products (1)

cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*

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