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Fencer Vulns
high 8.1

CVE-2020-15214 json

Out of bounds write in tensorflow-lite

published 2020-09-25 6 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2020-15214 , EUVD-2020-0210 , GHSA-p2cq-cprg-frvm , PYSEC-2020-137 , PYSEC-2020-294 , PYSEC-2020-329

cvss
8.1
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.7%
EPSS 0.7% chance of exploitation in 30 days, percentile 52%
public exploits
0
none known

Summary

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based 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 sorted, 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.

Patch / fix

  • pypi tensorflow 2.2.1, 2.3.1
  • pypi tensorflow-cpu 2.2.1, 2.3.1
  • pypi tensorflow-gpu 2.2.1, 2.3.1
  • bitnami tensorflow 2.2.1, 2.3.1
  • google tensorflow 2.2.1, 2.3.1

Exploit prediction

0.67% chance of exploitation in the next 30 days, higher than 52% of all scored CVEs (FIRST EPSS, 2026-11-08).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by version score vector
CNA 3.1 8.1 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
GitHub 3.1 8.1 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
NVD 3.1 8.1 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
NVD 2.0 6.8 AV:N/AC:M/Au:N/C:P/I:P/A:P
OSV 3.1 8.1 CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H

Affected packages

ecosystem package affected fixed in
pypi tensorflow
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
pypi tensorflow-cpu
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
pypi tensorflow-gpu
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1
bitnami tensorflow
>=2.2.0, <2.2.1
>=2.3.0, <2.3.1
2.2.1
2.3.1

Affected products

  • tensorflow tensorflow

Classification

Weakness

References

Sources

Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.