Crashes due to overflow and `CHECK`-fail in ops with large tensor shapes
Impact
TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an int64_t. If an overflow occurs, MultiplyWithoutOverflow would return a negative result. In the majority of TensorFlow codebase this then results in a CHECK-failure. Newer constructs exist which return a Status instead of crashing the binary.
For example AddDim calls should be replaced by AddDimWithStatus.
This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs).
Patches
We have patched the issue in GitHub commits 7c1692bd417eb4f9b33ead749a41166d6080af85 (merging #51732), d81b1351da3e8c884ff836b64458d94e4a157c15 (merging #51717), a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf (merging #51658), and d81b1351da3e8c884ff836b64458d94e4a157c15 (merging #51973). It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 GitHub issue, GitHub issue and GitHub issue.
Betroffene Software
Klicken Sie auf eine Technologie, um alle zugehörigen Schwachstellen anzuzeigen.
| Plattform | Paket | Anfällige Versionen | Behoben in |
|---|---|---|---|
| Python (PyPI) | tensorflow | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
| Python (PyPI) | tensorflow-cpu | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
| Python (PyPI) | tensorflow-gpu | >= 2.6.0, < 2.6.1, >= 2.5.0, < 2.5.2, < 2.4.4 | 2.6.1, 2.5.2, 2.4.4 |
Verweise
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-prcg-wp5q-rv7p
- https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf
- https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15
- https://github.com/tensorflow/tensorflow/issues/46890
- https://github.com/tensorflow/tensorflow/issues/51908
- https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85
- https://nvd.nist.gov/vuln/detail/CVE-2021-41197
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-607.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-805.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-390.yaml
- https://github.com/tensorflow/tensorflow
- CVSS-Vektor
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H- Schwäche (CWE)
CWE-190- Plattform
- Python (PyPI)
- Paket
tensorflow, tensorflow-cpu, tensorflow-gpu- Veröffentlicht
- 05.11.2021
- Aktualisiert
- 11.10.2026 13:19
Quellen und Lizenzen
OSV / GitHub Advisory Database - PYSEC-2021-390 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-607 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-805 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-prcg-wp5q-rv7p - CC BY 4.0
Schwachstellenhistorie: tensorflow
Alle veröffentlichten Schwachstellen dieses Pakets (Python (PyPI)), neueste zuerst. Zeigt, ob es ein einmaliger Ausrutscher ist oder ein Paket, das man im Auge behalten muss.
- Einträge insgesamt
- 433
- Erste → letzte
- 04.2019 07.2024
- Ohne Fix
- 0
- In Angriffen ausgenutzt
- 0
Verteilung nach Schweregrad
- Kritisch 6
- Hoch 112
- Mittel 210
- Niedrig 105
Veröffentlichungen pro Jahr
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 169
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Crashes due to overflow and `CHECK`-fail in ops with large tensor shapes dieser Eintrag
Erkennen Sie Schwachstellen in Ihrer Anwendung
Mit der DockRay-Integration und aktivierter Softwareinventarisierung prüft DockRay gemeldete Komponentenversionen auf bekannte Schwachstellen und benachrichtigt Sie über bestätigte Treffer. Die Abdeckung hängt von der unterstützten Technologie und den verfügbaren Meldungen ab.