Use after free in boosted trees creation
Impact
The implementation for tf.raw_ops.BoostedTreesCreateEnsemble can result in a use after free error if an attacker supplies specially crafted arguments:
import tensorflow as tf
v= tf.Variable([0.0])
tf.raw_ops.BoostedTreesCreateEnsemble(
tree_ensemble_handle=v.handle,
stamp_token=[0],
tree_ensemble_serialized=['0'])The implementation 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.
Patches
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.
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 by members of the Aivul Team from Qihoo 360.
Betroffene Software
Klicken Sie auf eine Technologie, um alle zugehörigen Schwachstellen anzuzeigen.
| Plattform | Paket | Anfällige Versionen | Behoben in |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
| Python (PyPI) | tensorflow-cpu | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
| Python (PyPI) | tensorflow-gpu | < 2.3.4, >= 2.4.0, < 2.4.3, >= 2.5.0, < 2.5.1 | 2.3.4, 2.4.3, 2.5.1 |
Verweise
- https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m7fm-4jfh-jrg6
- https://nvd.nist.gov/vuln/detail/CVE-2021-37652
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-565.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-763.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-274.yaml
- https://github.com/tensorflow/tensorflow
- CVSS-Vektor
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H- Schwäche (CWE)
CWE-415, CWE-416- Plattform
- Python (PyPI)
- Paket
tensorflow, tensorflow-cpu, tensorflow-gpu- Veröffentlicht
- 12.08.2021
- Aktualisiert
- 11.10.2026 13:19
Quellen und Lizenzen
OSV / GitHub Advisory Database - PYSEC-2021-274 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-565 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-763 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-m7fm-4jfh-jrg6 - 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
-
Use after free in boosted trees creation dieser Eintrag
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