Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow
Impact
The implementation of tf.raw_ops.EditDistance has incomplete validation. Users can pass negative values to cause a segmentation fault based denial of service:
import tensorflow as tf
hypothesis_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64)
hypothesis_values = tf.constant(0, shape=[3], dtype=tf.int64)
hypothesis_shape = tf.constant(0, shape=[3], dtype=tf.int64)
truth_indices = tf.constant(-1250999896764, shape=[3, 3], dtype=tf.int64)
truth_values = tf.constant(2, shape=[3], dtype=tf.int64)
truth_shape = tf.constant(2, shape=[3], dtype=tf.int64)
tf.raw_ops.EditDistance(
hypothesis_indices=hypothesis_indices,
hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape,
truth_indices=truth_indices,
truth_values=truth_values,
truth_shape=truth_shape)In multiple places throughout the code, we are computing an index for a write operation:
if (g_truth == g_hypothesis) {
auto loc = std::inner_product(g_truth.begin(), g_truth.end(),
output_strides.begin(), int64_t{0});
OP_REQUIRES(
ctx, loc < output_elements,
errors::Internal("Got an inner product ", loc,
" which would require in writing to outside of "
"the buffer for the output tensor (max elements ",
output_elements, ")"));
output_t(loc) =
gtl::LevenshteinDistance<T>(truth_seq, hypothesis_seq, cmp);
// ...
}However, the existing validation only checks against the upper bound of the array. Hence, it is possible to write before the array by massaging the input to generate negative values for loc.
Patches
We have patched the issue in GitHub commit 30721cf564cb029d34535446d6a5a6357bebc8e7.
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 by Neophytos Christou from Secure Systems Lab at Brown University.
Betroffene Software
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| Plattform | Paket | Anfällige Versionen | Behoben in |
|---|---|---|---|
| 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 |
Verweise
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r2f-g8mw-9gvr
- https://nvd.nist.gov/vuln/detail/CVE-2022-29208
- https://github.com/tensorflow/tensorflow/commit/30721cf564cb029d34535446d6a5a6357bebc8e7
- https://github.com/tensorflow/tensorflow
- 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
- https://github.com/advisories/GHSA-2r2f-g8mw-9gvr
- https://pypi.org/project/tensorflow-cpu
- https://pypi.org/project/tensorflow-gpu
- CVSS-Vektor
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H- Schwäche (CWE)
CWE-787- Plattform
- Python (PyPI)
- Paket
tensorflow, tensorflow-cpu, tensorflow-gpu- Veröffentlicht
- 24.05.2022
- Aktualisiert
- 11.10.2026 14:17
Quellen und Lizenzen
OSV / GitHub Advisory Database - PYSEC-2026-3088 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-3266 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-2r2f-g8mw-9gvr - CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2026-943 - PyPA Advisory Database; CC BY 4.0
Schwachstellenhistorie: tensorflow-gpu
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
- 427
- Erste → letzte
- 04.2019 07.2024
- Ohne Fix
- 0
- In Angriffen ausgenutzt
- 0
Verteilung nach Schweregrad
- Kritisch 6
- Hoch 109
- Mittel 207
- Niedrig 105
Veröffentlichungen pro Jahr
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 18
- 2024: 1
- 2025: 0
- 2026: 0
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Segfault and OOB write due to incomplete validation in `EditDistance` in TensorFlow dieser Eintrag
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