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Fencer Intel
low 2.5

CVE-2021-29521 json

Segfault in SparseCountSparseOutput

published 5y ago modified last mo. also BIT-tensorflow-2021-29521 , EUVD-2021-0262 , GHSA-hr84-fqvp-48mm , PYSEC-2021-158 , PYSEC-2021-449 , PYSEC-2021-647

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. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

Patch / fix

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

arch linux advisories AVG-1962

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Exploit prediction

0.19% chance of exploitation in the next 30 days, higher than 8% of all scored CVEs (FIRST EPSS, 2026-11-09).

Exploits and checks

  • github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm 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
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.3.3
2.4.2
pypi tensorflow-cpu
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.3.3
2.4.2
pypi tensorflow-gpu
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.3.3
2.4.2
bitnami tensorflow
>=2.3.0, <2.3.3
>=2.4.0, <2.4.2
2.3.3
2.4.2

Affected products

  • tensorflow tensorflow

Classification

Weakness
  • CWE-131 Incorrect Calculation of Buffer Size

References

Sources

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