Segfault in SparseCountSparseOutput
Impact
Specifying a negative dense shape in tf.raw_ops.SparseCountSparseOutput results in a segmentation fault being thrown out from the standard library as std::vector invariants are broken.
import tensorflow as tf
indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)
weights = tf.constant([], shape=[0, 0], dtype=tf.int64)
tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)This is because the implementation assumes the first element of the dense shape is always positive and uses it to initialize a BatchedMap<T> (i.e., std::vector<absl::flat_hash_map<int64,T>>) data structure.
bool is_1d = shape.NumElements() == 1;
int num_batches = is_1d ? 1 : shape.flat<int64>()(0);
...
auto per_batch_counts = BatchedMap<W>(num_batches);If the shape tensor has more than one element, num_batches is the first value in shape.
Ensuring that the dense_shape argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.
Patches
We have patched the issue in GitHub commit c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
Software afectado
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| Plataforma | Paquete | Versiones vulnerables | Corregida en |
|---|---|---|---|
| Python (PyPI) | tensorflow | >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-cpu | >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-gpu | >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.3.3, 2.4.2 |
Referencias
- https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm
- https://nvd.nist.gov/vuln/detail/CVE-2021-29521
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-449.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-647.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-158.yaml
- Vector CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Debilidad (CWE)
CWE-131- Plataforma
- Python (PyPI)
- Paquete
tensorflow, tensorflow-cpu, tensorflow-gpu- Publicada
- 14.05.2021
- Actualizado
- 11.10.2026 15:17
Fuentes y licencias
OSV / GitHub Advisory Database - PYSEC-2021-158 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-449 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-647 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-hr84-fqvp-48mm - CC BY 4.0
Historial de vulnerabilidades: tensorflow-cpu
Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.
- Entradas en total
- 430
- Primera → última
- 04.2019 07.2024
- Sin corrección
- 0
- Explotada en ataques
- 0
Distribución por gravedad
- Crítica 6
- Alta 111
- Media 208
- Baja 105
Divulgaciones por año
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
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Segfault in SparseCountSparseOutput esta entrada
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