Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow
Impact
The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:
import tensorflow as tf
import numpy as np
tf.histogram_fixed_width(values=np.nan, value_range=[1,2])
The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:
index_to_bin.device(d) =
((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
.template cast<double>() /
step)
.cwiseMin(nbins_minus_1)
.template cast<int32>();
If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.
This only occurs on the CPU implementation.
Patches
We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported externally via a GitHub issue.
Czego dotyczy
Kliknij technologię, aby zobaczyć wszystkie dotyczące jej podatności.
| Platforma | Paczka | Podatne wersje | Poprawka w wersji |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.6.4, >= 2.7.0, < 2.7.2, >= 2.8.0, < 2.8.1 | 2.6.4, 2.7.2, 2.8.1 |
| Python (PyPI) | tensorflow-cpu | < 2.6.4, >= 2.7.0, < 2.7.2, >= 2.8.0, < 2.8.1 | 2.6.4, 2.7.2, 2.8.1 |
| Python (PyPI) | tensorflow-gpu | < 2.6.4, >= 2.7.0, < 2.7.2, >= 2.8.0, < 2.8.1 | 2.6.4, 2.7.2, 2.8.1 |
Odnośniki
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrp2-fhq4-4q3w
- https://nvd.nist.gov/vuln/detail/CVE-2022-29211
- https://github.com/tensorflow/tensorflow/issues/45770
- https://github.com/tensorflow/tensorflow/commit/e57fd691c7b0fd00ea3bfe43444f30c1969748b5
- https://github.com/tensorflow/tensorflow
- https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/histogram_op.cc
- https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/histogram_op.cc#L35-L74
- https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0
- https://pypi.org/project/tensorflow-gpu
- https://github.com/advisories/GHSA-xrp2-fhq4-4q3w
- https://pypi.org/project/tensorflow
- https://pypi.org/project/tensorflow-cpu
- Wektor CVSS
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H- Słabość (CWE)
CWE-20- Platforma
- Python (PyPI)
- Paczka
tensorflow, tensorflow-cpu, tensorflow-gpu- Ujawniono
- 24.05.2022
- Aktualizacja
- 11.10.2026 14:18
Źródła i licencje
OSV / GitHub Advisory Database - PYSEC-2026-1048 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3260 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-xrp2-fhq4-4q3w - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3382 - PyPA Advisory Database; CC BY 4.0
Historia podatności: tensorflow-gpu
Wszystkie opisane podatności tej paczki (Python (PyPI)), od najnowszej. Widać, czy to jednorazowa wpadka, czy paczka, której trzeba pilnować.
- Wszystkich wpisów
- 427
- Pierwsza → ostatnia
- 04.2019 07.2024
- Bez poprawki
- 0
- Wykorzystywana w atakach
- 0
Rozkład istotności
- Krytyczna 6
- Wysoka 109
- Średnia 207
- Niska 105
Ujawnienia w latach
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
-
Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow ten wpis
Dowiedz się, które podatności dotyczą Twojej aplikacji
Po podłączeniu integracji DockRay i włączeniu raportowania oprogramowania DockRay porównuje zgłoszone wersje komponentów ze znanymi podatnościami i powiadamia o potwierdzonych dopasowaniach. Zakres zależy od obsługiwanej technologii i dostępnych biuletynów.