CVE-2020-15213

CVE-2020-15213 is a medium-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 4.0. 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-770.

Key facts

Description

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Frequently asked questions

What is CVE-2020-15213?
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
How severe is CVE-2020-15213?
CVE-2020-15213 has a CVSS 3.x base score of 4.0, 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 low.
Is CVE-2020-15213 being actively exploited?
It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (46th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-15213?
CVE-2020-15213 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-15213?
Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2020-15213 published?
CVE-2020-15213 was published on 2020-09-25 and last updated on 2026-06-17.

References

Affected products (1)

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