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Fencer Vulns
low 2.5

CVE-2021-29550 json

Division by 0 in `FractionalAvgPool`

published 2021-05-14 5 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2021-29550 , EUVD-2021-0291 , GHSA-f78g-q7r4-9wcv , PYSEC-2021-187 , PYSEC-2021-478 , PYSEC-2021-676

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 8%
public exploits
0
none known

Summary

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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

0.19% chance of exploitation in the next 30 days, higher than 8% of all scored CVEs (FIRST EPSS, 2026-11-08).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv 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 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
NVD 2.0 2.1 AV:L/AC:L/Au:N/C:N/I:N/A:P
OSV 3.1 5.5 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/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.