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Fencer Vulns
low 2.5

CVE-2021-29546 json

Division by 0 in `QuantizedBiasAdd`

published 2021-05-14 5 y ago modified 2026-09-10 1 mo ago also BIT-tensorflow-2021-29546 , EUVD-2021-0287 , GHSA-m34j-p8rj-wjxq , PYSEC-2021-183 , PYSEC-2021-474 , PYSEC-2021-672

cvss
2.5
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 10%
public exploits
0
none known

Summary

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. 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, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
  • pypi tensorflow-cpu 2.1.4, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
  • pypi tensorflow-gpu 2.1.4, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
  • 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

0.20% chance of exploitation in the next 30 days, higher than 10% of all scored CVEs (FIRST EPSS, 2026-11-08).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq referenced PoC, tagged "exploit" by a source, unverified

CVSS scores

assigned by version score vector
CNA 3.1 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
GitHub 3.1 2.5 CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
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
<2.2.0rc0
>=2.2.0, <2.3.0rc0
>=2.3.0, <2.3.4
>=2.4.0, <2.4.3
2.1.4
2.2.3
2.3.3
2.4.2
2.2.0rc0
2.3.0rc0
2.3.4
2.4.3
pypi tensorflow-cpu
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
<2.2.0rc0
>=2.2.0, <2.3.0rc0
>=2.3.0, <2.3.4
>=2.4.0, <2.4.3
2.1.4
2.2.3
2.3.3
2.4.2
2.2.0rc0
2.3.0rc0
2.3.4
2.4.3
pypi tensorflow-gpu
<2.1.4
>=2.2.0, <2.2.3
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
<2.2.0rc0
>=2.2.0, <2.3.0rc0
>=2.3.0, <2.3.4
>=2.4.0, <2.4.3
2.1.4
2.2.3
2.3.3
2.4.2
2.2.0rc0
2.3.0rc0
2.3.4
2.4.3
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

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.