Skip to content
Fencer Vulns
medium 5.3

CVE-2021-29607 json

Incomplete validation in `SparseSparseMinimum`

published 2021-05-14 5 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2021-29607 , EUVD-2021-0348 , GHSA-gv26-jpj9-c8gq , PYSEC-2021-244 , PYSEC-2021-535 , PYSEC-2021-733

cvss
5.3
version 3.1, CNA
cisa kev
Not listed
not in CISA's catalog
epss
0.2%
EPSS 0.2% chance of exploitation in 30 days, percentile 14%
public exploits
0
none known

Summary

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Patch / fix

  • pypi tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
  • pypi tensorflow-cpu 2.1.4, 2.2.3, 2.3.3, 2.4.2
  • pypi tensorflow-gpu 2.1.4, 2.2.3, 2.3.3, 2.4.2
  • bitnami tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
  • google tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
  • tensorflow Arch Linux 2.5.0-1

arch linux advisories AVG-1962

Exploit prediction

0.23% chance of exploitation in the next 30 days, higher than 14% of all scored CVEs (FIRST EPSS, 2026-11-08).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by version score vector
CNA 3.1 5.3 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
GitHub 3.1 5.3 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H
NVD 3.1 7.8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
NVD 2.0 4.6 AV:L/AC:L/Au:N/C:P/I:P/A:P
OSV 3.1 7.8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Affected packages

ecosystem package affected fixed in
pypi tensorflow
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
pypi tensorflow-cpu
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
pypi tensorflow-gpu
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.1.4
2.2.3
2.3.3
2.4.2
bitnami tensorflow
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.1.4
2.2.3
2.3.3
2.4.2

Affected products

  • tensorflow tensorflow

Classification

Weakness
  • CWE-754 Improper Check for Unusual or Exceptional Conditions

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.