Heap OOB and CHECK fail in `ResourceGather`
Impact
An attacker can trigger a crash via a CHECK-fail in debug builds of TensorFlow using tf.raw_ops.ResourceGather or a read from outside the bounds of heap allocated data in the same API in a release build:
import tensorflow as tf
tensor = tf.constant(value=[[1,2],[3,4],[5,6]],shape=(3,2),dtype=tf.uint32)
v = tf.Variable(tensor)
tf.raw_ops.ResourceGather(
resource=v.handle,
indices=[0],
dtype=tf.uint32,
batch_dims=10,
validate_indices=False)
The implementation does not check that the batch_dims value that the user supplies is less than the rank of the input tensor.
Since the implementation uses several for loops over the dimensions of tensor, this results in reading data from outside the bounds of heap allocated buffer backing the tensor:
// batch_dims_ = > params.dims() (10 > 2)
for (int i = 0; i < batch_dims_; ++i) {
result_shape.AddDim(params.dim_size(i));
}
for (int i = batch_dims_; i < indices.dims(); ++i) {
result_shape.AddDim(indices.dim_size(i));
}
for (int i = batch_dims_ + 1; i < params.dims(); ++i) {
result_shape.AddDim(params.dim_size(i));
}
In debug mode, .dim_size(i) validates that the argument is less than .dims() using a DCHECK. But the DCHECK is a no-op in release builds.
Patches
We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d.
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
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| 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-2r8p-fg3c-wcj4
- https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d
- https://nvd.nist.gov/vuln/detail/CVE-2021-37654
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-567.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-765.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-276.yaml
- https://github.com/tensorflow/tensorflow
- CVSS-Vektor
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:H- Schwäche (CWE)
CWE-125- 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-2021-276 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-567 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-765 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-2r8p-fg3c-wcj4 - 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
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Heap OOB and CHECK fail in `ResourceGather` dieser Eintrag
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