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Fencer Vulns
high 7.8

CVE-2021-37652 json

Use after free in boosted trees creation in TensorFlow

published 2021-08-12 5 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2021-37652 , EUVD-2021-0378 , GHSA-m7fm-4jfh-jrg6 , PYSEC-2021-274 , PYSEC-2021-565 , PYSEC-2021-763

cvss
7.8
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 6%
public exploits
0
none known

Summary

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Patch / fix

  • pypi tensorflow 2.3.4, 2.4.3, 2.5.1
  • pypi tensorflow-cpu 2.3.4, 2.4.3, 2.5.1
  • pypi tensorflow-gpu 2.3.4, 2.4.3, 2.5.1
  • bitnami tensorflow 2.3.4, 2.4.3, 2.5.1
  • google tensorflow 2.3.4, 2.4.3
  • tensorflow2 Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
  • bazel3.7 Package Hub 15 SP3, openSUSE Leap 15.3 3.7.2-bp153.4.1
  • tensorflow2-lite Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
  • bazel-skylib1.0.3 Package Hub 15 SP3, openSUSE Leap 15.3 1.0.3-bp153.2.1
  • tensorflow2_2_6_0-gnu-hpc Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
  • tensorflow2_2_6_0-gnu-openmpi2-hpc Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1

opensuse advisories openSUSE-SU-2022:10014-1

  • tensorflow Arch Linux 2.5.1-1

arch linux advisories AVG-2292

Exploit prediction

0.17% chance of exploitation in the next 30 days, higher than 6% of all scored CVEs (FIRST EPSS, 2026-11-08).

CVSS scores

assigned by version score vector
CNA 3.1 7.8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
GitHub 3.1 7.8 CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/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
<2.3.4
>=2.4.0, <2.4.3
>=2.5.0, <2.5.1
>=2.3.0, <2.3.4
2.3.4
2.4.3
2.5.1
pypi tensorflow-cpu
<2.3.4
>=2.4.0, <2.4.3
>=2.5.0, <2.5.1
>=2.3.0, <2.3.4
2.3.4
2.4.3
2.5.1
pypi tensorflow-gpu
<2.3.4
>=2.4.0, <2.4.3
>=2.5.0, <2.5.1
>=2.3.0, <2.3.4
2.3.4
2.4.3
2.5.1
bitnami tensorflow
>=2.3.0, <2.3.4
>=2.4.0, <2.4.3
>=2.5.0, <2.5.1
2.3.4
2.4.3
2.5.1

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.