How severe is CVE-2020-15197?

CVE-2020-15197 has a CVSS 3.x base score of 6.3, rated medium severity. It is exploitable over network with high attack complexity, requires low privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.

Is CVE-2020-15197 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (49th percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2020-15197?

CVE-2020-15197 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2020-15197?

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

When was CVE-2020-15197 published?

CVE-2020-15197 was published on 2020-09-25 and last updated on 2026-06-17.

CVE-2020-15197

CVE-2020-15197 is a medium-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 6.3. 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 6.3)
CVSS v2: 3.5
EPSS exploit prediction: 1% (49th percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-20
Affected product: Google Tensorflow
Published: 2020-09-25
Last modified: 2026-06-17

Description

In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Frequently asked questions

What is CVE-2020-15197?: In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
How severe is CVE-2020-15197?: CVE-2020-15197 has a CVSS 3.x base score of 6.3, rated medium severity. It is exploitable over network with high attack complexity, requires low privileges and no user interaction. Impact on confidentiality is none, integrity none, and availability high.
Is CVE-2020-15197 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (49th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-15197?: CVE-2020-15197 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-15197?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2020-15197 published?: CVE-2020-15197 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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