Heap OOB in nested `tf.map_fn` with `RaggedTensor`s
Impact
It is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:
import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information.
The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions in the above example.
The same implementation can result in data loss, if input tensor is tweaked:
import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)Here, the output tensor will only have 2 elements for each inner dimension.
Patches
We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12.
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 Haris Sahovic.
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/security/advisories/GHSA-g8wg-cjwc-xhhp
- https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12
- https://nvd.nist.gov/vuln/detail/CVE-2021-37679
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-592.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-790.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-301.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:N- Schwäche (CWE)
CWE-125, CWE-681- 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-2021-301 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-592 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-790 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-g8wg-cjwc-xhhp - CC BY 4.0
Schwachstellenhistorie: tensorflow-cpu
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
- 430
- Erste → letzte
- 04.2019 07.2024
- Ohne Fix
- 0
- In Angriffen ausgenutzt
- 0
Verteilung nach Schweregrad
- Kritisch 6
- Hoch 111
- Mittel 208
- Niedrig 105
Veröffentlichungen pro Jahr
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Heap OOB in nested `tf.map_fn` with `RaggedTensor`s 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.