How severe is CVE-2020-15196?

CVE-2020-15196 has a CVSS 3.x base score of 8.5, rated high severity. It is exploitable over network with high attack complexity, requires low privileges and no user interaction. Impact on confidentiality is high, integrity high, and availability high.

Is CVE-2020-15196 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-2020-15196?

CVE-2020-15196 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2020-15196?

Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround. Given its high severity, prioritise patching exposed systems.

When was CVE-2020-15196 published?

CVE-2020-15196 was published on 2020-09-25 and last updated on 2026-06-17.

CVE-2020-15196

CVE-2020-15196 is a high-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 8.5. 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: High (CVSS 3.x base score 8.5)
CVSS v2: 6.5
EPSS exploit prediction: 1% (55th percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-125
Affected product: Google Tensorflow
Published: 2020-09-25
Last modified: 2026-06-17

Description

In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Frequently asked questions

What is CVE-2020-15196?: In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
How severe is CVE-2020-15196?: CVE-2020-15196 has a CVSS 3.x base score of 8.5, rated high severity. It is exploitable over network with high attack complexity, requires low privileges and no user interaction. Impact on confidentiality is high, integrity high, and availability high.
Is CVE-2020-15196 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-2020-15196?: CVE-2020-15196 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-15196?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround. Given its high severity, prioritise patching exposed systems.
When was CVE-2020-15196 published?: CVE-2020-15196 was published on 2020-09-25 and last updated on 2026-06-17.

References

Affected products (1)

cpe:2.3:a:google:tensorflow:2.3.0:*:*:*:-:*:*:*

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