How severe is CVE-2020-15265?

CVE-2020-15265 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-2020-15265 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (55th percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2020-15265?

CVE-2020-15265 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2020-15265?

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

When was CVE-2020-15265 published?

CVE-2020-15265 was published on 2020-10-21 and last updated on 2026-06-17.

CVE-2020-15265

CVE-2020-15265 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-125.

Key facts

Severity: Medium (CVSS 3.x base score 5.9)
CVSS v2: 5.0
EPSS exploit prediction: 1% (55th percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-125
Affected product: Google Tensorflow
Published: 2020-10-21
Last modified: 2026-06-17

Description

In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Frequently asked questions

What is CVE-2020-15265?: In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
How severe is CVE-2020-15265?: CVE-2020-15265 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-2020-15265 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (55th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-15265?: CVE-2020-15265 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-15265?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2020-15265 published?: CVE-2020-15265 was published on 2020-10-21 and last updated on 2026-06-17.

References

Affected products (1)

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

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