How severe is CVE-2019-16778?

CVE-2019-16778 has a CVSS 3.x base score of 2.6, rated low severity. It is exploitable over network with high attack complexity, requires low privileges and user interaction. Impact on confidentiality is none, integrity none, and availability low.

Is CVE-2019-16778 being actively exploited?

It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (51st percentile), an estimate of the probability of exploitation in the next 30 days.

What products are affected by CVE-2019-16778?

CVE-2019-16778 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.

How do I fix CVE-2019-16778?

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

When was CVE-2019-16778 published?

CVE-2019-16778 was published on 2019-12-16 and last updated on 2026-06-17.

CVE-2019-16778

CVE-2019-16778 is a low-severity vulnerability in Google Tensorflow with a CVSS 3.x base score of 2.6. 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-681.

Key facts

Severity: Low (CVSS 3.x base score 2.6)
CVSS v2: 7.5
EPSS exploit prediction: 1% (51st percentile)
Actively exploited: Not listed in CISA KEV
Weakness: CWE-681
Affected product: Google Tensorflow
Published: 2019-12-16
Last modified: 2026-06-17

Description

In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.

Frequently asked questions

What is CVE-2019-16778?: In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0.
How severe is CVE-2019-16778?: CVE-2019-16778 has a CVSS 3.x base score of 2.6, rated low severity. It is exploitable over network with high attack complexity, requires low privileges and user interaction. Impact on confidentiality is none, integrity none, and availability low.
Is CVE-2019-16778 being actively exploited?: It is not currently listed in CISA's KEV catalog. Its EPSS exploit-prediction score is 1% (51st percentile), an estimate of the probability of exploitation in the next 30 days.
What products are affected by CVE-2019-16778?: CVE-2019-16778 affects Google Tensorflow. See the affected-products list for the exact vulnerable versions.
How do I fix CVE-2019-16778?: Review the linked vendor and NVD advisories for patched versions and mitigations, then upgrade or apply the recommended workaround.
When was CVE-2019-16778 published?: CVE-2019-16778 was published on 2019-12-16 and last updated on 2026-06-17.

References

Affected products (1)

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

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