CVE-2020-15212

CVE-2020-15212 is a high-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 8.1. 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-787.

Key facts

Description

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. 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 the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. 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-2020-15212?
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. 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 the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. 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-2020-15212?
CVE-2020-15212 has a CVSS 3.x base score of 8.1, rated high severity. It is exploitable over network with high attack complexity, requires no privileges and no user interaction. Impact on confidentiality is low, integrity low, and availability high.
Is CVE-2020-15212 being actively exploited?
It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (45th percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2020-15212?
CVE-2020-15212 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2020-15212?
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-2020-15212 published?
CVE-2020-15212 was published on 2020-09-25 and last updated on 2026-06-17.

References

Affected products (1)

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